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1
Mason, Jesse Cheyenne. "On improving wind-turbine hub-height wind-speed forecasts." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46558.
Повний текст джерела
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Siuta, David. "Improving hub-height wind forecasts in complex terrain." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/61055.
Повний текст джерелаАнотація:
Wind-speed forecasts from numerical-weather-prediction (NWP) models are important for daily wind-resource generation planning. However, NWP models are imperfect. The ability of energy planners to efficiently manage resources is a function of the accuracy of deterministic wind forecasts and of the associated probability estimates of forecast uncertainty. As the amount of energy generated from wind increases to significant levels, improving forecast accuracy and representation of forecast uncertainty is a key area of active research. This dissertation advances wind forecasting over regions of complex topography using the Weather Research and Forecasting (WRF) model. The optimal WRF-model configuration is a function of planetary-boundary-layer (PBL) physics, grid length, and initial-condition choice. The first component of this work determines which of these three factors most influences forecast accuracy over complex terrain. The two largest factors influencing forecast accuracy are the PBL-physics scheme and the grid length, with the dominant factor being a function of location, season, and time of day.
The second component of the research addresses the need for probability-based forecast information, which is only recently being used within the industry. Wind forecasts from an ensemble using eight PBL schemes, three grid lengths, and two initial-conditions sources are converted into probability models that are then evaluated. Using the full, empirical ensemble distribution produces uncalibrated probabilistic forecasts. Prescribing a Gaussian probability distribution based on statistical moments of a past training dataset results in calibrated and sharp probabilistic forecasts. Such a method is also computationally cheap. The final aspect of this study evaluates the role of boundary-layer static stability on forecast performance. Traditionally, empirical surface-layer similarity theory has been used to relate surface fluxes of heat, momentum, and moisture to vertical profiles of temperature and wind. To evaluate and improve surface-layer similarity theories over mountain ridges, a year-long field campaign of temperature and wind measurements was conducted at wind farms in British Columbia. New empirical equations for complex terrain are proposed based on the field data, and found to perform well at an independent test location.Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
Graduate
3
Welsh, David J. S. "The improvement of wind-wave forecasts in the Great Lakes /." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487948807587679.
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Nchaba, Teboho. "Verification of gridded seasonal wind speed forecasts over South Africa." Master's thesis, University of Cape Town, 2013. http://hdl.handle.net/11427/4970.
Повний текст джерелаАнотація:
Includes abstract.Includes bibliographical references.
The Climate System Analysis Group (CSAG) at the University of Cape Town produces provisional global and Southern African seasonal wind forecasts generated using the United Kingdom Meteorological Office Atmospheric General Circulation Model (AGCM) HadAM3P (non-standard version of HadAM3). This study examines the quality of the seasonal wind speed forecasts through a forecast verification process for continuous variables using reanalysis products of the National Centers for Environmental Prediction and the Department of Energy (NCEP-DOE) as observations data. The verification analyses are performed using summary measures Mean Error (ME), Mean Absolute Error (MAE), Mean Squared Error (MSE), correlation coefficients, Linear Error in Probability Space (LEPS) and exploratory methods, scatter and conditional quantile plots. These methods are used to determine the aspects of forecast quality namely, bias, accuracy, reliability, resolution, and skill over a 20 year period (1991 to 2010). The results of the study have determined that the use of both accuracy and skill measures for the verification analyses provide more information about the quality of the forecasts, as opposed only one of these. In all provinces, the highest quality seasonal wind speed forecasts are made at 500 hPa and the lowest quality forecasts at 1000 hPa. Furthermore regions, pressure levels, and seasons with the highest forecast quality share the common characteristic that their wind speeds are relatively high. The forecasts add value to the climatology and thus are a useful tool for wind assessment at a seasonal scale. It is suggested that adding spatial resolution to the forecasts through downscaling may prepare them for use in applications such as wind power output forecasting.
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Lau, Ada. "Probabilistic wind power forecasts : from aggregated approach to spatiotemporal models." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:f5a66568-baac-4f11-ab1e-dc79061cfb0f.
Повний текст джерелаАнотація:
Wind power is one of the most promising renewable energy resources to replace conventional generation which carries high carbon footprints. Due to the abundance of wind and its relatively cheap installation costs, it is likely that wind power will become the most important energy resource in the near future. The successful development of wind power relies heavily on the ability to integrate wind power effciently into electricity grids. To optimize the value of wind power through careful power dispatches, techniques in forecasting the level of wind power and the associated variability are critical. Ideally, one would like to obtain reliable probability density forecasts for the wind power distributions. As wind is intermittent and wind turbines have non-linear power curves, this is a challenging task and many ongoing studies relate to the topic of wind power forecasting. For this reason, this thesis aims at contributing to the literature on wind power forecasting by constructing and analyzing various time series models and spatiotemporal models for wind power production. By exploring the key features of a portfolio of wind power data from Ireland and Denmark, we investigate different types of appropriate models. For instance, we develop anisotropic spatiotemporal correlation models to account for the propagation of weather fronts. We also develop twostage models to accommodate the probability masses that occur in wind power distributions due to chains of zeros. We apply the models to generate multi-step probability forecasts for both the individual and aggregated wind power using extensive data sets from Ireland and Denmark. From the evaluation of probability forecasts, valuable insights are obtained and deeper understanding of the strengths of various models could be applied to improve wind power forecasts in the future.
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de, Almeida Francisco M. S. C. "The influence of wind on HF radar surface current forecasts." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://edocs.nps.edu/npspubs/scholarly/theses/2008/Dec/08Dec%5Fde%5FAlmeida.pdf.
Повний текст джерелаАнотація:
Thesis (M.S. in Physical Oceanography)--Naval Postgraduate School, December 2008.Thesis Advisor(s): Paduan, Jeffrey. "December 2008." Description based on title screen as viewed on January 29, 2009. Includes bibliographical references (p. 67-69). Also available in print.
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Sjöberg, Ludvig. "Wind Forecasts Using Large Eddy Simulations for Stratospheric Balloon Applications." Thesis, Luleå tekniska universitet, Rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74457.
Повний текст джерелаАнотація:
The launch of large stratospheric balloons is highly dependant on the meteorological conditions at ground level, including wind speed. The balloon launch base Esrange Space Center in northern Sweden currently uses forecasts delivered through the Swedish Meteorological and Hydrological Institute to predict opportunities for balloon launches. However the staff at Esrange Space Center experience that the current forecasts are not accurate enough. For that reason the Weather Research and Forecasting model is used to improve the forecast. The model performs a Large Eddy Simulation over the area closest to Esrange Space Center to predict wind speed and turbulence. During twelve hypothetical launch days the improved forecast have an overall accuracy of 93% compared to the old forecast accuracy of 69%. With some improvements and the right computational power the system is thought to be operationally viable.
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Gallaher, Shawn G. "Performance of a high resolution diagnostic model for short range mesoscale wind forecasts in complex terrain." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FGallaher.pdf.
Повний текст джерелаАнотація:
Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 2002.Thesis advisor(s): Douglas K. Miller, Wendell A. Nuss. Includes bibliographical references (p. 125-128). Also available online.
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Olaofe, Zaccheus Olaniyi. "Wind energy generation and forecasts: a case study of Darling and Vredenburg sites." Master's thesis, University of Cape Town, 2013. http://hdl.handle.net/11427/16831.
Повний текст джерелаАнотація:
This research study presents the wind resource assessment at two potential onshore wind sites at the Western Cape of South Africa for small and large scale wind energy generation. It is anticipated that by virtue of the enormous wind resources prevalent along the South Africa West Coast, it is economical and cost effective to generate electricity from the wind to offset the increasing cost of energy generation from non-renewable sources (coal-fired, nuclear, gas etc.) which are the major source of power generation. Despite the environmental benefit and economic potentials of the wind energy, its variability and the inability to accurately predict (estimate) the long term energy generation potentials usually lead to difficulties in the selection and development of a suitable wind site for any proposed wind farm project(s) in the country.
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Ive, Federica. "Improving numerical simulation methods for the assessment of wind source availability and related power production for wind farms over complex terrain." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/350981.
Повний текст джерелаАнотація:
One of the Sustainable Development Goals set in 2015 by the United Nations aims to ensure access to affordable, reliable, sustainable, and modern energy for all, increasing the global share of renewable energy to 32-35% by 2030. Moving towards this goal, the University of Trento funded the interdepartmental strategic project ERiCSol (Energie Rinnovabili e Combustibili Solari), in order to promote the research on renewable energy storage and solar fuels. The research activity presented in this thesis lies in the framework of this project, focusing on the development of new advanced simulation approaches to improve the estimation of the wind resource availability and the related power production for Italian wind farms in complex terrain. The wind farms, operated by the company AGSM S.p.A., are located in two different geographical contexts: Rivoli Veronese and Affi are at the inlet of the Adige Valley, while Casoni di Romagna and Carpinaccio Firenzuola, are on the crest of the Apennines close to the borders between the provinces of Bologna e Firenze. The analysis of data from year-long field measurements highlighted the different peculiarities of these areas. The wind farms at the mouth of the Adige Valley are influenced by a daily periodic thermally-driven circulation, characterised by a nocturnal intense down-valley wind alternating with a diurnal weaker up-valley wind, while the Apennines wind farms are primarily affected by synoptic-scale winds. Simulations, with the mesoscale Weather Research and Forecasting (WRF) model, are performed and compared with field measurements in both cases, to highlight strengths and weaknesses. The results show that the model is able to capture with good accuracy wind speed and direction in the Apennines wind farms, while larger errors arise for Rivoli Veronese and Affi wind farms, where the intensity of the nocturnal down-valley wind is generally underestimated. Considering the former case, modelled and observed yearly wind speed density distributions are compared, in order to evaluate the impact of model errors in the estimation of the wind resource at these sites. Since reliable simulations of the wind resource are also essential to ensure the security in power transmission and to prevent penalties to energy operators, an analysis of the power production is also performed, to evaluate how errors in the estimate of the resource translate into errors in the estimate of the production. Considering the wind farms at the mouth of the Adige Valley, the research work mainly focuses on the evaluation of the impact of data assimilation by means of observational nudging on model results, in order to optimize the setup for operational forecasts. Different configurations are tested and compared, varying the temporal window for the assimilation of local data.
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Cavicchioli, Niccolò. "Preparing for a future satellite mission to measure wind and improve climate forecasts." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23037/.
Повний текст джерелаАнотація:
Stratospheric Inferred Winds (SIW) is a future satellite mission, which has been selected by the Swedish Space Agency to become the next Swedish research satellite, to be launched in 2024. It will consist of a sub-millimetre radiometer instrument, optimised for wind measurements in the middle atmosphere, and orbiting the Earth aboard a microsatellite platform. The goal of this master thesis was to carry out a preliminary study to assess the potential of the mission to contribute to a better understanding of the middle atmospheric dynamical events, and thus to improve weather and climate forecasts. The analysis of zonal mean eastward wind from two five-year-long reanalysis data sets, namely ERA5 and MERRA-2, is described and compared to SIW estimated performances. The areas of major disagreement are investigated in details. It appears that the models have important difficulties to accurately reproduce the dynamical phenomena in the regions out of geostrophic balance due to wave forcing processes. The results show that a significant contribution can be provided by the SIW mission particularly at low latitudes, where the effects related to the Semi-Annual Oscillation can be studied, and at high latitudes during winter-time, where the effects of Sudden Stratospheric Warming events can be investigated. In those regions, at mesospheric altitudes, SIW estimated precision is most of the time significantly lower than the observed differences.
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Zamo, Michaël. "Statistical Post-processing of Deterministic and Ensemble Wind Speed Forecasts on a Grid." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLA029/document.
Повний текст джерелаАнотація:
Les erreurs des modèles de prévision numérique du temps (PNT) peuvent être réduites par des méthodes de post-traitement (dites d'adaptation statistique ou AS) construisant une relation statistique entre les observations et les prévisions. L'objectif de cette thèse est de construire des AS de prévisions de vent pour la France sur la grille de plusieurs modèles de PNT, pour les applications opérationnelles de Météo-France en traitant deux problèmes principaux. Construire des AS sur la grille de modèles de PNT, soit plusieurs milliers de points de grille sur la France, demande de développer des méthodes rapides pour un traitement en conditions opérationnelles. Deuxièmement, les modifications fréquentes des modèles de PNT nécessitent de mettre à jour les AS, mais l'apprentissage des AS requiert un modèle de PNT inchangé sur plusieurs années, ce qui n'est pas possible dans la majorité des cas.Une nouvelle analyse du vent moyen à 10 m a été construite sur la grille du modèle local de haute résolution (2,5 km) de Météo-France, AROME. Cette analyse se compose de deux termes: une spline fonction de la prévision la plus récente d'AROME plus une correction par une spline fonction des coordonnées du point considéré. La nouvelle analyse obtient de meilleurs scores que l'analyse existante, et présente des structures spatio-temporelles réalistes. Cette nouvelle analyse, disponible au pas horaire sur 4 ans, sert ensuite d'observation en points de grille pour construire des AS.Des AS de vent sur la France ont été construites pour ARPEGE, le modèle global de Météo-France. Un banc d'essai comparatif désigne les forêts aléatoires comme meilleure méthode. Cette AS requiert un long temps de chargement en mémoire de l'information nécessaire pour effectuer une prévision. Ce temps de chargement est divisé par 10 en entraînant les AS sur des points de grille contigü et en les élaguant au maximum. Cette optimisation ne déteriore pas les performances de prévision. Cette approche d'AS par blocs est en cours de mise en opérationnel.Une étude préalable de l'estimation du « continuous ranked probability score » (CRPS) conduit à des recommandations pour son estimation et généralise des résultats théoriques existants. Ensuite, 6 AS de 4 modèles d'ensemble de PNT de la base TIGGE sont combinées avec les modèles bruts selon plusieurs méthodes statistiques. La meilleure combinaison s'appuie sur la théorie de la prévision avec avis d'experts, qui assure de bonnes performances par rapport à une prévision de référence. Elle ajuste rapidement les poids de la combinaison, un avantage lors du changement de performance des prévisions combinées. Cette étude a soulevé des contradictions entre deux critères de choix de la meilleure méthode de combinaison : la minimisation du CRPS et la platitude des histogrammes de rang selon les tests de Jolliffe-Primo. Il est proposé de choisir un modèle en imposant d'abord la platitude des histogrammes des rangsErrors of numerical weather prediction (NWP) models can be reduced thanks to post-processing methods (model output statistics, MOS) that build a statistical relationship between the observations and associated forecasts. The objective of the present thesis is to build MOS for windspeed forecasts over France on the grid of several NWP models, to be applied on operations at Météo-France, while addressing the two main issues. First, building MOS on the grid of some NWP model, with thousands of grid points over France, requires to develop methods fast enough for operational delays. Second, requent updates of NWP models require updating MOS, but training MOS requires an NWP model unchanged for years, which is usually not possible.A new windspeed analysis for the 10 m windspeed has been built over the grid of Météo-France's local area, high resolution (2,5km) NWP model, AROME. The new analysis is the sum of two terms: a spline with AROME most recent forecast as input plus a correction with a spline with the location coordinates as input. The new analysis outperforms the existing analysis, while displaying realistic spatio-temporal patterns. This new analysis, now available at an hourly rate over 4, is used as a gridded observation to build MOS in the remaining of this thesis.MOS for windspeed over France have been built for ARPEGE, Météo-France's global NWP model. A test-bed designs random forests as the most efficient MOS. The loading times is reduced by a factor 10 by training random forests over block of nearby grid points and pruning them as much as possible. This time optimisation goes without reducing the forecast performances. This block MOS approach is currently being made operational.A preliminary study about the estimation of the continuous ranked probability score (CRPS) leads to recommendations to efficiently estimate it and to generalizations of existing theoretical results. Then 4 ensemble NWP models from the TIGGE database are post-processed with 6 methods and combined with the corresponding raw ensembles thanks to several statistical methods. The best combination method is based on the theory of prediction with expert advice, which ensures good forecast performances relatively to some reference forecast. This method quickly adapts its combination weighs, which constitutes an asset in case of performances changes of the combined forecasts. This part of the work highlighted contradictions between two criteria to select the best combination methods: the minimization of the CRPS and the flatness of the rank histogram according to the Jolliffe-Primo tests. It is proposed to choose a model by first imposing the flatness of the rank histogram
13
Stamp, Alexander. "The relationship between weather forecasts and observations for predicting electricity output from wind turbines." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215651.
Повний текст джерелаАнотація:
Wind power production is of growing importance to many countries around the world. To improve reliability and power grid stability related to wind power, forecasting of wind power is becoming an important commercial and research area. Machine learning methods are considered to be highly valuable when making predictions on time series data and as such have become prominent within wind forecasting as well. This thesis extends an existing neural network prediction system with new input data series, in particular the observed wind speed from the wind farm itself. The goal was to investigate the effect this new data series has, and whether or not it could be used to improve predictions as compared to the baseline prediction system defined within this thesis. To do this multiple methods of including the observed wind speed are developed, including a multi-stage network concept. These results are statistically tested to give more evidence for their comparison to baseline. The results show that the multi-stage network concept can use the observed wind speed to improve performance over the baseline case for specific prediction horizons.Betydelsen för vindkraftsproduktion växer i länder runt om i världen. För att förbättratillförlitligheten och elnätstabiliteten i vindkraften blir dess prognoser viktiga kommersielltoch ett forskningsområde. Maskininlärningsmetoder anses vara mycket värdefullanär man gör förutsägelser om tidsseriedata och har därmed framträdat inom vindprognoser. Detta arbete utökar ett existerande prediktionssystem av neurala nätverk med ny indata,med särskilt den observerade vindhastigheten från själva vindkraftparken. Måletvar att undersöka effekten av denna nya dataserie, och huruvida den skulle kunna användasför att förbättra förutsägelserna jämfört med det befintliga referensprognossystemetdefinierat i denna uppsats. För att kunna göra detta utvecklas flera metoder för att inkludera den observeradevindhastigheten, inklusive ett flerstegs nätverkskoncept. Dessa resultat är statistiskt testadeför att ge mer grund i deras jämförelse med referensmodellen. Resultaten visar att detflerstega nätverkskonceptet kan använda den observerade vindhastigheten för att förbättraprestanda över referensmodellen för specifika prediktionshorisonter.
14
Nachmani, Gil. "Minimum-energy flight paths for UAVs using mesoscale wind forecasts and approximate dynamic programming." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Dec%5FNachmani.pdf.
Повний текст джерелаАнотація:
Thesis (M.S. in Operations Research)--Naval Postgraduate School, December 2007.Thesis Advisor(s): Royset, Johannes O. Description based on title screen as viewed on January 22, 2007. Includes bibliographical references (p. 57-60). Also available in print.
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Neese, Jay M. "Evaluating Atlantic tropical cyclone track error distributions for use in probabilistic forecasts of wind distribution." Thesis, Monterey, California. Naval Postgraduate School, 2010. http://hdl.handle.net/10945/5150.
Повний текст джерелаАнотація:
Approved for public release; distribution is unlimitedThis thesis investigates whether the National Hurricane Center (NHC) operational product for producing probabilistic forecasts of tropical cyclone (TC) wind distributions could be further improved by examining the distributions of track errors it draws upon to calculate probabilities. The track spread/skill relationship for several global ensemble prediction system forecasts is examined as a condition for a description of a full probability distribution function. The 2007, 2008, and 2009 NHC official track forecasts are compared to the ensemble prediction system model along-, cross-, and forecast-track errors. Significant differences in statistical properties were then identified among the groups to determine whether conditioning based on geographic location was warranted. Examination of each regional distribution interval suggests that differences in distributions existed for along-track and cross-track errors. Because errors for ensemble mean and deterministic forecasts typically have larger mean errors and larger variance than official forecast errors, it is unlikely that independent error distributions based on these models would refine the PDFs used in the probabilistic model. However, this should be tested with a sensitivity analysis and verified with the probability swath. Overall, conditional formatting suggests that the NHC probability product may be improved if the Monte Carlo (MC) model would draw from refined distributions of track errors based on TC location.
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Plyler, Mitchell. "A machine learning approach to wind estimation and uncertainty using remote dropsondes and deterministic forecasts." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112476.
Повний текст джерелаАнотація:
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 100-102).
The U.S. Air force currently has a need for high altitude, unguided airdrops without making two passes over a drop zone (DZ). During conventional high altitude drops, aircrews fly over a DZ, release a dropsonde, compute a payload release point, loop back to the DZ, and release a payload. This work proposes a machine learning method that enables a single pass airdrop mission where a dropsonde is released en route to a DZ, the dropsonde measurement is merged with a weather forecast using machine learning methods, and the aircrew releases the payload when they reach the drop zone. Machine learning models are trained to use a deterministic weather forecast and a dropsonde measurement to predict the winds over a DZ. The uncertainty in the DZ wind prediction is inferred using quantile regression. The uncertainty estimate is nonstatic meaning it is unique for each airdrop mission, and the uncertainty estimate is derived from data that is already available to aircrews. The quantile regression uncertainty estimate replaces the single pass mission's potential need for ensemble forecasts. The developed models are evaluated using data near Yuma, AZ, with later evaluation of several other locations in the US. The machine learning models are shown to improve the accuracy of the wind prediction at the DZ from a remote location up to 117 km away by up to 43% over other methods. To generalize findings, we develop models at several US locations and demonstrate the machine learning methodology is successful at other geographic locations. Models trained on data from a set of DZs are then shown to be transferrable to DZs unseen by models during training. This moves the wind prediction methodology closer to a global solution. The inferred prediction uncertainty is found to reliably reflect the accuracy in the wind prediction. The dynamic wind uncertainty estimate allows for the assessment of mission risks as a function of day-of-drop conditions. For nominal drop parameters, single pass airdrop missions were simulated around the Yuma DZ, and the machine learning methodology is shown to be approximately 20% more accurate than other methods.
by Mitchell Plyler.
S.M.
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Gensler, André [Verfasser]. "Wind Power Ensemble Forecasting : Performance Measures and Ensemble Architectures for Deterministic and Probabilistic Forecasts / André Gensler." Kassel : Kassel University Press, 2019. http://d-nb.info/117653517X/34.
Повний текст джерела
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Zelenke, Brian Christopher. "An empirical statistical model relating winds and ocean surface currents : implications for short-term current forecasts." Thesis, Connect to the title online, 2005. http://hdl.handle.net/1957/2166.
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SILVA, ILITCH VITALI GOMES DA. "THE WIND FORECAST FOR WIND POWER GENERATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16824@1.
Повний текст джерелаАнотація:
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOA energia eólica é uma das alternativas mais promissoras para geração de energia elétrica, pois assegura a diversidade e segurança no fornecimento de energia e atende à necessidade premente de reduzir os níveis de emissão de gases poluentes. Na operação de sistemas elétricos com forte presença de geração eólica é fundamental prever com pelo menos um dia de antecedência os valores futuros (pelo menos horários) da veloci-dade do vento, pois assim pode-se avaliar a disponibilidade de energia para o próximo dia, uma informação útil no despacho das unidades geradoras e no controle do sistema elétrico. A proposta dessa dissertação objetiva especificamente desenvolver modelos de previsão de curto prazo da velocidade do vento, baseado em técnicas de inteligência artificial, modelo da rede neural artificial e neuro-fuzzy adaptativa (ANFIS) e um mode-lo Estatístico composto por um modelo de regressão harmônica e Box-Jenkins. Para aplicação da metodologia considerou-se o município de São João do Cariri (Estado de Paraíba), onde está localizada uma das estações de referência do projeto SONDA (Sis-tema Nacional de Dados Ambientais para o setor de energia). O desempenho dos mode-los rede neural, neuro-fuzzy (ANFIS) e modelo Estatístico são comparados nas previ-sões de 6 horas, 12 horas, 18 h e 24horas a frente. Os resultados obtidos mostram o me-lhor desempenho da modelagem ANFIS e encorajam novos estudos no tema.
Wind power is one of the most promising options for power generation. It ensures the diversity and security of energy supply and meets the pressing need to reduce the levels of emission of polluting gases. In the operation of electrical systems with a strong presence of wind generation, it is essential to provide at least one day in advance the future values (at least hourly) of wind speed, so that we can assess the availability of energy for the next day, a useful information in the order of the generating units and electrical control system. The purpose of this dissertation aims to develop models spe-cifically to develop models to forecast short-term wind speed, based on artificial intelligence techniques, artificial neural network model and adaptive neuro-fuzzy Systems (ANFIS) and a statistical model composed of a harmonic regression model and Box-Jenkins. For application of the methodology, the city of São João do Cariri (State of Paraíba), where a reference station of SONDA project (National Environmental Data for the energy sector) is located, was considered.To apply the methodology was consi-dered the city of the ray tracing model (State of Paraíba), which is located a station ref-erence design (National Environmental Data for the energy sector). The performance of artificial neural network model and adaptive neuro-fuzzy Systems (ANFIS) and a statis-tical model are compared mixed forecasts of 6 hours, 12 hours, 18hours and 24 hours ahead. The results show the best performance of the ANFIS model and encourage fur-ther studies on the subject.
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Gahard, Claude F. "An estimation of the ability to forecast boundary layer mixing height and wind parameters through forecast verification over Fort Ord." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03sep%5FGahard.pdf.
Повний текст джерелаАнотація:
Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 2003.Thesis advisor(s): Wendell A. Nuss, David S. Brown. Includes bibliographical references (p. 65-66). Also available online.
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Jarmander, Sara. "Wind Power Forecast Accuracy in Scandinavia:Analysis of Forecast Errors Using TAPM." Thesis, KTH, Kraft- och värmeteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-226146.
Повний текст джерелаАнотація:
Wind power has different characteristics compared to conventional energy sources. The main difference is that wind power fluctuates under the influence of meteorological variables. This gives rise to problems related to grid stability. It is therefore important to understand these variations and their predictability in order to improve the integration of wind power into grids. Wind power forecasts play an important role. In this study, the accuracy of Numerical Weather Prediction (NWP) wind speed forecasts over the period 1st Sept. 2013 – 31st Dec. 2016 has been investigated. This was done for four Scandinavian windfarms; Rødsand II, Kårehamn, Jokkmokksliden and Storliden. The NWP used was The Air Pollution Model (TAPM). The main aim of the study was to examine if the implementation of bias correction methodologies could improve the accuracy of uncorrected TAPMforecasts. In addition to this main aim, the study was also intended to investigate the differences in the performance of TAPM between Australian and Scandinavian weather conditions. The results indicated that TAPM has a tendency to under-predict against actual observations. It was also observed that the accuracy varied throughout the year, with the best performance during summer and the worst during winter. Additionally, the performance varied between turbines within the wind farms. The magnitude of forecast errors was lowest for turbines which experience high degrees of wake effects from upstream turbines. Furthermore, the results showed that the implementation of certain bias correction methodologies improved the accuracy. The greatest enhancements were achieved by the implementation of two correction methodologies, both based on the combination of bias correction and timing correction. The NRMSE of wind speed was reduced by almost 50% for “hour ahead” forecasts for Rødsand II and Kårehamn when full bias correction was applied and by nearly 70% for Jokkmokksliden and Storliden. A reduction in the wind speed magnitude error by as much as half was also reached. Based on these results, the overall conclusion is that TAPM can be applied for sites with completely different weather conditions with moderately good accurateness, especially if bias correction is applied. However, due to the limited time frame and other delimitations of this study, further studies are necessary in order to draw deeper conclusions.Vindkraft är en förnybar energikälla som skiljer sig på flera sätt jämfört med konventionell energiproduktion. Konvensionell produktion avser planerbar och icke väderberoende energiproduktion som dessutom är synkront kopplad till elnätet, exempelvis vattenkraft och kärnkraft. Den huvudsakliga skillnaden är att energiproduktionen från vindkraftverk är direkt kopplad till meteorologiska förhållanden och är därmed beroende av de rätta väderbetingelserna för att kunna producera el, framförallt rådande vindhastigheter. Detta innebär att elproduktionen varierar på ett oförutsägbart sätt vilket medför att den producerade elkraften från vindkraftverk är mindre stabil jämfört med elkraft som utnyttjar konventionells krafttekniker. En ökad andel variabel elproduktion från vindkraft medför stora utmaningar för det befintliga och framtida kraftsystemet. Den främsta utmaningen är att upprätthålla balansen i systemet, både i det korta och långa tidsperspektivet. Om inga åtgärder genomförs förväntas kraftsystemets utformning och egenskaper att bli sämre i form av ökad känslighet för störningar och försämrad leveranstid. En ökad förståelse för vindkraftsproduktionens variabilitet och förutsägbarhet är därmed av intresse för att kunna förbättra integrationen av variabel vindkraftsproduktion. Prognosmodeller för vindkraftsproduktion (analogt med prognoer för vindhastighet) utgör en viktig faktor i detta. I denna studie har noggrannheten av numeriska väderprognoser (NWP) analyserats. Analysen genomfördes för fyra skandinaviska vindkraftparker mellan 1 september 2013 och 31 december 2016. De granskade parkerna var: Rødsand II, Kårehamn, Jokkmokksliden och Storliden. Den numeriska prognosmodell som används i denna studie var The Air Pollution Model (TAPM). TAPM utvecklades i Australien och modellen bygger på observationsbaserade meterologiska input. TAPM är i själva verket ansluten till globala databaser med struktuerad meterologisk data bestående av bland annat terränghöjd, vegetation och synoptisk metrologisk information. TAPM har tidigare tillämpats för att förutspå vindhastigheter för ett flertal vindkraftparker i Australien och ett antal platser i USA. Inga tidigare studier har dock gjorts för scandinaviska förhållanden. Det huvudsakliga målet med denna studie var därmed att undersöka huruvida biaskorrigerade metoder kan förbättra noggrannheten av okorrigerade TAPMprognoser för de fyra utvalda vindkraftparkerna. Denna studie avsåg även att undersöka om prognosernas noggrannhet skiljer sig nämnvärt mellan skandinaviska och australienska väderförhållanden. Numeriska modeller innehåller alltid fel jämför med de ”sanna” värdena. Resultatet av denna studie indikerade att TAPM-prognoserna har en tendens att underskatta vindhastigheter, därmed även vindkraftsproduktionen gentemot den verkliga produktionen. Dessutom observerades att prognosernas noggrannhet varierade under året. Den bästa tillförlitligheten erhölls under vintern och den sämsta tillförlitligheten under sommarhalvåret. Vidare varierade prognosernas noggrannhet mellan turbinerna inom de enskilda vindkraftparkerna. Storleken på felet i TAPMprognoserna var generellt sett lägst för turbiner som utsetts för så kallade vakar. Vakar är ett fenomen som uppstår bakom rotorbladen och påverkar energiproduktionen för bakomliggande vindkraftverk. Storleken på felet var lägst för turbiner som i stor utsträckning påverkas av vakar från turbiner uppströms. Resultatet visade även att implementeringen av biaskorrigerande metoder förbättrade noggrannheten av TAPM-prognoserna. Sammantaget undersöktes fyra biaskorrigerande metoder varav två uppvisade de största förbättringarna. Gemensamt för dessa två metoder var att de baserades på en kombination av biaskorrigering och tidskorrigering. Olika statistiska metoder användes för att uppskatta storleken av felet för den förutspådda vindhastigheten som modellerats i TAPM. Bland annat användes Root Mean Square Error (RMSE), Mean Absolute Error (MAE) och Mean Bias Error (MBE). Dessa värden normaliserades därefter med avseende på medelvärdet av den verkliga produktionen för önskad tidsperiod. Resultatet visade bland annat att NRMSE för TAPM-modellerade timvisa vindhastigheter minskade med nästan 50 % för Rødsand II och Kårehamn när full biaskorrektion tillämpades och med uppemot 70 % för Jokkmokksliden och Storliden. Med utgångspunkt från de erhållna resultaten är den övergripande slutsatsen att TAPM kan tillämpas för geografiska platser med olika väderförhållanden och samtidigt generera prognoser med relativt god noggrannhet, speciellt om biaskorrigerade metoder appliceras. Till följd av den begränsande tidsramen och andra avgränsningar i denna studie är dock ytterligare analyser nödvändiga för att dra djupare slutsatser.
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Mauch, Brandon Keith. "Managing Wind Power Forecast Uncertainty in Electric Grids." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/199.
Повний текст джерелаАнотація:
Electricity generated from wind power is both variable and uncertain. Wind forecasts provide valuable information for wind farm management, but they are not perfect. Chapter 2 presents a model of a wind farm with compressed air energy storage (CAES) participating freely in the day-ahead electricity market without the benefit of a renewable portfolio standard or production tax credit. CAES is used to reduce the risk of committing uncertain quantities of wind energy and to shift dispatch of wind generation to high price periods. Using wind forecast data and market prices from 2006 – 2009, we find that the annual income for the modeled wind-CAES system would not cover annualized capital costs. We also estimate market prices with a carbon price of $20 and $50 per tonne CO2 and find that the revenue would still not cover the capital costs. The implied cost per tonne of avoided CO2 to make a wind-CAES profitable from trading on the day-ahead market is roughly $100, with large variability due to electric power prices.
Wind power forecast errors for aggregated wind farms are often modeled with Gaussian distributions. However, data from several studies have shown this to be inaccurate. Further, the distribution of wind power forecast errors largely depends on the wind power forecast value. The few papers that account for this dependence bin the wind forecast data and fit parametric distributions to the actual wind power in each bin. A method to model wind power forecast uncertainty as a single closed-form solution using a logit transformation of historical wind power forecast and actual wind power data is presented in Chapter 3. Once transformed, the data become close to jointly normally distributed. We show the process of calculating confidence intervals for wind power forecast errors using the jointly normally distributed logit transformed data. This method has the advantage of fitting the entire dataset with five parameters while also providing the ability to make calculations conditioned on the value of the wind power forecast.
The model present in Chapter 3 is applied in Chapter 4 to calculate increases in net load uncertainty introduced from day-ahead wind power forecasts. Our analyses uses data from two different electric grids in the U.S. having similar levels of installed wind capacity with large differences in wind and load forecast accuracy due to geographic characteristics. A probabilistic method to calculate the dispatchable generation capacity required to balance day-ahead wind and load forecast errors for a given level of reliability is presented. Using empirical data we show that the capacity requirements for 95% day-ahead reliability range from 2100 MW to 5600 MW for ERCOT and 1900 MW to 4500 MW for MISO, depending on the amount of wind and load forecast for the next day. We briefly discuss the additional requirements for higher reliability levels and the effect of correlated wind and load forecast errors. Additionally, we show that each MW of additional wind power capacity in ERCOT must be matched by a 0.30 MW day-ahead dispatchable generation capacity to cover 95% of day-ahead uncertainty. Due to the lower wind forecast uncertainty in MISO, the value drops to 0.13 MW of dispatchable capacity for each MW of additional wind capacity.
Direct load control (DLC) has received a lot of attention lately as an enabler of wind power. One major benefit of DLC is the added flexibility it brings to the grid. Utilities in some parts of the U.S. can bid the load reduction from DLC into energy markets. Forecasts of the resource available for DLC assist in determining load reduction quantities to offer. In Chapter 5, we present a censored regression model to forecast load from residential air conditioners using historical load data, hour of the day, and ambient temperature. We tested the forecast model with hourly data from 467 air conditioners located in three different utilities. We used two months of data to train the model and then ran day-ahead forecasts over a six week period. Mean square errors ranged from 4% to 8% of mean air conditioner load. This method produced accurate forecasts with much lower data requirements than physics based forecast models.
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Scharff, Richard. "On Distributed Balancing of Wind Power Forecast Deviations in Competitive Power Systems." Licentiate thesis, KTH, Elektriska energisystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-103608.
Повний текст джерелаАнотація:
Wind power generation does, on the one hand, contribute to a less polluting and more sustainable electric power generation mix. On the other hand, the uncertainty and the variability of the power output do challenge the operation of the power system: hourly variations in wind power generation are hardly predictable in a precise way. To decrease the need for balancing power, it might be beneficial from the overall system-perspective to subject power generating companies to stricter balancing incentives/rules. The way the market is designed has become crucial to exploit the existing flexibility in the power system and to increase the efficiency in its operation: inappropriate market designs can counteract all technical achievements. The work conducted for this thesis is embedded in a project on wind power integration and electricity market design following the aim to develop a simulation tool to analyse the consequences of changes in specific market rules. This thesis analyses wind power variations and forecast errors in the Swedish power system and explores the question whether internal ex-ante self-balancing can efficiently reduce the need for balancing power. Applying internal ex-ante self-balancing, every power generating company re-schedules its own power plants in order to balance its commitments towards other market actors with its newest production forecast. This is done shortly before the hour of delivery. To assess the value of this self-balancing, possible trading and scheduling decisions for power generating companies are modelled based on a hydro-thermal generation portfolio within the framework of the Nordic electricity market design. The model is based on a sequence of mixed-integer linear optimisation problems for the clearing of the different sub-markets. Both the data and the model have an hourly time resolution. In a case study, the model is applied on a simplified test-system. The need of real-time balancing by the transmission system operator, the total variable generation cost of the system, as well as the extent to which the power generating companies re-scheduled their production are then used as indicators to evaluate self-balancing.QC 20121017
Short-term hydro power planning in power systems with large amounts of wind power
Elektra 36141: Korttidsplanering av vatten-värmekraftsystem vid stora mängder vindkraft: System-perspektivet
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Matusevicius, Tadas. "Analysis of Swedish Wind Power Forecast Quality over Forecast Horizon and Power System Operation Implications." Thesis, KTH, Elkraftteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213706.
Повний текст джерелаАнотація:
Wind power, as one of the fastest growing means of generation, can oer environmentalbenets. However, due to its stochastic nature it is dicult to designaccurate prediction tools, thus the forecast errors are inherently present. Goodunderstanding of the errors that may occur is imperative for greater penetrationof wind power into the system as it can facilitate power system planning andoperation.In this paper, wind power forecasts for dierent price areas in Sweden areanalysed and compared. For this purpose, data starting November 2015 weredownloaded every hour from the Nord Pool spot database and Python code forparsing and analysis was written. Common indicators, such as the root-meansquare(RMSE) and bias error were used to characterise the accuracy of theforecasts. As expected, it was shown that in general the RMSE decreases as theforecast hour approaches the delivery hour. In addition, systematic bias erroraround the day-ahead market closure time was identied and discussed.The paper continues to analyse wind power forecast error distribution withrespect to the forecast time and dierent production levels. Four statistical momentsof distribution function were calculated and compared. It was shown thatfor the forecast horizons between 0 and 36 hours the forecast error distributionfunctions are negatively skewed leptokurtic.The temporal wind power forecast error correlations between dierent horizonsas well as the spatial correlations between dierent price areas are calculatedand discussed. As expected, a stronger correlation was identied betweenneighbouring price areas. In addition, correlation coecients between the forecasterrors and the up and down regulation prices were calculated.Finally, a model was developed to quantify the amount of operating reservelevel needed to compensate the uncertainty in the system due to wind powerand load forecasts. Future scenarios with an increasing wind power penetrationlevel are simulated and the amount of operating reserve level for individualprice areas is calculated. It was shown that with rapidly increasing wind powerpenetration, especially price area SE3 will need to plan for higher operatingreserve levels to successfully cope with the uncertainty in the load and windpower forecasts.Vindkraft, som den mest okande satt for elproduktion, kan skapa miljofordelar.Pa grund av sin stokastiska karaktar ar det svart att skapa ett exakt vindkraftprognosverktyg utan prognosfel. Lamplig uppfattning om fel ar avgorande foratt underlatta driften och planeringen av elsystemet.I den har uppsatsen analyseras och jamfors vindkraftprognoser for olika prisomradena i Sverige. Analys har utforts av data som hamtats fran och mednovember 2015 med en timmes mellanrum fran Nord Pool-spot databasen. Vanligaindikatorer, som RMSE (root-mean-square) och metodfel anvandes for attkarakterisera prognosens noggrannhet. Det visar att RMSE i allmanhet minskarnar prognostiden nar leveranstiden. Dessutom identierades och diskuteradessystematiska fel runt den dagen-forre marknaden's stangningstid.Uppsatsen fortsatter att analysera vindkraftprognosfelfordelning med hansyntill prognostid och olika produktionsnivaer. Fyra statistiska moment av fordelningsfunktionenberaknades och jamfordes. Det visades att for prognoshorisonternamellan 0 och 36 timmar ar prognosfelfordelningsfunktionerna negativaskevade leptokurtiska.Den tidsmassiga vindkraftprognosfelkorrelationen mellan olika horisonter samtde rumsliga korrelationerna mellan olika prisomraden beraknas och diskuteras.Som forvantat identierades en starkare korrelation mellan narliggande prisomraden. Dessutom beraknades korrelationskoecienter mellan prognosfel ochupp- och nedreguleringspriser.Slutligen utvecklades en modell for att kvantiera mangden av drift reservniva som behovs for att kompensera osakerheten i systemet pa grund avosakra vindkraft och lastprognoser. Framtidsscenarier med okande vindkraftspenetrationsniva simuleras och mangden driftsreservniva for enskilda prisomradenberaknas. Det visar att, med en snabbt okande vindkraftspenetration, sarskiltprisomradet SE3 kommer att behova planeras for hogre driftsreservnivaer foratt framgangsrikt klara osakerheten i last- och vindkraftprognoserna.
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Hawkins, Samuel Lennon. "High resolution re-analysis of wind speeds over the British Isles for wind energy integration." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/7898.
Повний текст джерелаАнотація:
The UK has highly ambitious targets for wind development, particularly offshore, where over 30GW of capacity is proposed for development. Integrating such a large amount of variable generation presents enormous challenges. Answering key questions depends on a detailed understanding of the wind resource and its temporal and spatial variability. However, sources of wind speed data, particularly offshore, are relatively sparse: satellite data has low temporal resolution; weather buoys and met stations have low spatial resolution; while the observations from ships and platforms are affected by the structures themselves. This work uses a state-of-the art mesoscale atmospheric model to produce a new high-resolution wind speed dataset over the British Isles and surrounding waters. This covers the whole region at a resolution of 3km for a period of eleven consecutive years, from 2000 to 2010 inclusive, and is thought to be the first high resolution re-analysis to represent a true historic time series, rather than a statistically averaged climatology. The results are validated against observations from met stations, weather buoys, offshore platforms and satellite-derived wind speeds, and model bias is reduced offshore using satellite derived wind speeds. The ability of the dataset to predict power outputs from current wind farms is demonstrated, and the expected patterns of power outputs from future onshore and offshore wind farms are predicted. Patterns of wind production are compared to patterns of electricity demand to provide the first conclusive combined assessment of the ability of future onshore and offshore wind generation meet electricity demand and contribute to secure energy supplies.
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STERMASI, ADRIST. "Offshore wind energy in the Adriatic Sea: Modelling of the microscale wind field and short-term forecast of wind power potential." Doctoral thesis, Università Politecnica delle Marche, 2015. http://hdl.handle.net/11566/242945.
Повний текст джерелаАнотація:
Studying wind energy is still a challenge. Many arguments are involved in this field such as
environmental sustainability, infrastructures, economics and many others, has to find the
equilibrium and coexist together for the further development of this technology. The P.O.W.E.R.E.D
project, part of the IPA-PROGRAMME deal with these issues concerning the Adriatic basin.
Numerical and experimental evaluation of wind energy resources is assigned to Università
Politecnica delle Marche. Two maps of different spatial resolutions of one and three kilometers
respectively, for one and five years are produced using the PSU/NCAR mesoscale MM5 (fifth
generation) in hind-casting.
The existing meteorological network data's are used as single point monitor the inter area so
experimental data will be available for different uses. Further more a forecasting system will be
available for the possible wind farms. For the expansion of wind energy to new markets for the
Adriatic Basin poses a challenge into estimating this resource for the whole area. An accurate
climatic description relies on a comprehensive regional observation network, characteristic often not
available on emerging markets, and generally not extensive on established markets. In these cases
the use of Mesoscale modeling can provide a large-scale analysis with regional features. Considering
existing limitations, this approach can be used to support an initial ranking and selection of sites for
wind farm projects.
Initial large-scale conditions are obtained from the Reanalysis database, which contains global
meteorological observations, covering over 30 years at 1° km spatial resolution. An initial large-scale
analysis of 3 km of spatial resolution is conducted for the study of the climatology of the Adriatic
region for 5 consecutive years; from 2008 to 2012 describing the average conditions in across a
desired area. In a second time, a finest resolution wind map of 1 km is obtained in order to better
understand the evolution of the climatology near the costs.
The simulation of the regional wind climate is conducted using the Mesoscale Modeling (MM5). This
modeling system calculates the dynamics and thermodynamics of the atmosphere in limited areas,
and is currently developed and employed at several universities and research centers worldwide.
Through the process of downscaling the Reanalysis data is used to describe the initial conditions for
the desired area, allowing the MM5 to calculate in great detail the effects of local features over the
large-scale conditions.
In order to assess the limitations of this approach, several validation efforts are conducted over
different areas, providing an overview of the benefits of this methodology for wind energy
estimation.
The results generated from such a method can be used to represent the wind resource distribution
over an area of interest for a wind farms projects, and data points can be obtained in order to
represent time-series and vertical profiles for a specific area. However, as with any numerical
simulation, the limitations of this approach should be carefully considered on a case-per-case
analysis, and its results should be used as an additional aid to initial studies and green-field
procurement.
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Valee, Joris. "Using airborne laser scans to model roughness length and forecast energy production of wind farms." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-393953.
Повний текст джерелаАнотація:
Successful wind power projects start with a realistic representation of the surface, more specifically the surface roughness of the site. This thesis investigates the use of airborne laser scans to model the surface roughness around a new wind farm. Estimations are made to find out how forest management and tree growth affects roughness length and displacement height. Data from scans two years apart for a specific site is provided by the Swedish governmental land registration authority. Next, tree height and plant area index methods are applied and analyzed using MATLAB. The results shows a difference of roughness length between 10.34% and 36.21% during an eight year period. WindPRO/WAsP is used to import roughness lengths for four specific cases. Height contour lines and meteorological data is taken from a long term corrected MESO data set. The results indicate a reduction in uncertainty in annual energy production between 0.79% and 2.89% across four different cases. This effect becomes significantly larger (12.76%) when comparing with classical land cover maps. Further on, effects of turbulence intensity are simulated.Finally, the results of a survey, sent to three large forest land owners in Sweden, show there is an interest in adapting forest management plans in favor of wind energy production if benefits can be shared.
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Jaklovsky, Simon. "Drag based forecast for CME arrival." Thesis, Uppsala universitet, Institutionen för fysik och astronomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-415153.
Повний текст джерелаАнотація:
Coronal Mass Ejections (CMEs) are considered to be one of the most energetic events in the heliosphere. Capable of inducing geomagnetic storms on Earth that can cause damage to electronics, a pillar which the modern society we live in leans heavily upon. Being able to accurately predict the arrival of CMEs would present us with the ability to issue timely warnings to authorities and commercial actors, allowing for protective measures to be put in place minimizing the damage. In this study the predicted arrival times and speeds from the Drag Based Model (DBM) and Drag Based Ensemble Model (DBEM) were compared to observational data from a set of 12 events containing fast, Earth-directed Halo CMEs and their corresponding shocks. Although DBM was developed to model CME propagation, varying some parameters allow it to be used for estimating shock/sheath arrival. The results presented in this study indicate that on average DBM performs best when the drag-parameter γ is in the range 0.2 ≤ γ ≤ 0.3. However the variability in the results show that determining a universal value of γ for fast CMEs does not increase the consistency in the model's performance. For completeness, further investigation is needed to account for not only halo CMEs. This will allow to test broader range of variation in the DBEM input parameters.
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Gustafsson, Johan. "Managing Forecast Errors at the Nordic Power Market at Presence of Large Amounts of Wind Power." Thesis, Institutionen för energi och teknik, SLU, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-162594.
Повний текст джерелаАнотація:
The aim of the study is to investigate possibilities to manage forecast errors at the Nordic power market based on the size of the actor. This is part of a larger question at hand, whether the Nordic power market structure is suitable to support large wind power installations. An increased amount of wind power will unavoidably generate an increased amount of forecast errors and raise the demand for adjustment and regulating power. The investigation is carried out in three steps. ·First a scenario is created containing eight actors that is balance responsible for varying size of wind power production. Forecast error volumes are modeled associated with each actor in the scenario. ·Secondly, conditions at the intraday market and the regulating market during 2006 are investigated and the result is used as input for the next step. ·Last, price models are developed and used to calculate future imbalance costs associated to each actor, and the cost saving potential in different options. Because of uncertainties about the future intraday/regulating market situation, several calculations are carried out with different perspectives for the model calibration, different distributions of the forecast error volumes between the intraday market and the regulating market, and different options for managing the forecasts error. The results indicate that it is a major difference in the cost saving potential if the forecast error is “sold” or if the adjustment is “bought”. The cost saving potential differs significantly between the smaller and the larger actors.
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Jaworsky, Christina A. "The effects of energy storage properties and forecast accuracy on mitigating variability in wind power generation." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81605.
Повний текст джерелаАнотація:
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 84-87).
Electricity generation from wind power is increasing worldwide. Wind power can offset traditional fossil fuel generators which is beneficial to the environment. However, wind generation is unpredictable. Wind speeds have minute to minute variability which causes minute to minute generation to fluctuate. Additionally, wind forecasting does not perfectly predict wind generation, so it is difficult for wind to meet a generation schedule. Therefore, with increased wind production, there is a need for flexibility in the electricity grid. Electricity storage is one method of achieving greater flexibility. With storage, wind generators can have a less variable power output. They can also be made to follow a generation schedule the same way traditional generation does. This study discusses the storage requirements for reducing the variability of wind power. It also assesses the value of an accurate forecast in terms of storage requirements. Storage capacity requirements are shown to be modest compared to the size of a generator, representing approximately one minute of full power generation capacity. Accurate forecasting can reduce the storage requirements of a wind generator. However, forecasts have little added value for greater accuracy beyond correctly predicting the mean of the wind generation on delivery scheduling intervals.
by Christina A. Jaworsky.
S.M.
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Mauger, Léo. "Generation of wind speed and solar irradiance time series for power plants with storage." Thesis, KTH, Energiteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181923.
Повний текст джерелаАнотація:
Sizing renewable energy power plants with storage devices needs new resource assessment. Global amount of energy available has to be replaced by time series to depict the resource as a function of time. This paper introduces methodology to generate time series for wind speed and solar irradiance with a granularity between 10minutes and 1seconde. Ground measurements and macro-date from satellite imagery are analyzed and processed to obtain long-term site-specific time series. Because renewable energy forecasting is a growing concern, a second part of the work presents how to modify previously generated profiles in order to obtain forecasts with an expected error.
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Leblebici, Engin. "Terrain Modeling And Atmospheric Turbulent Flowsolutions Based On Meteorological Weather Forecast Data." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614103/index.pdf.
Повний текст джерелаАнотація:
In this study, atmospheric and turbulent flow solutions are obtained using meteorological flowfield and topographical terrain data in high resolution. The terrain topology of interest, which may be obtained in various resolution levels, is accurately modeled using structured or unstructured grids depending on whether high-rise building models are present or not.
Meteorological weather prediction software MM5, is used to provide accurate and unsteady boundary conditions for the solution domain. Unsteady turbulent flow solutions are carried out via FLUENT with the help of several User Defined Functions developed.
Unsteady flow solutions over topographical terrain of METU campus are computed with 25m x 25m x 15m resolution using structured grids. These FLUENT solutions are compared with the MM5 solutions. Also, the accuracy of the boundary layer velocity profiles is assessed. Finally, effects of surface roughness model extracted from MM5 for the region of interest is investigated.
In addition, unsteady flow solutions over METU campus are repeated in presence of high-rise building models using unstructured grids with resolution varying from 5 meters around buildings to 80 meters further away.
The study shows that unsteady, turbulent flow solutions can be accurately obtained using low resolution atmospheric weather prediction models and high resolution Navier-Stokes solutions over topographical terrains.
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Boone, Andrew. "Simulation of Short-term Wind Speed Forecast Errors using a Multi-variate ARMA(1,1) Time-series Model." Thesis, KTH, Elektriska energisystem, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-118084.
Повний текст джерелаАнотація:
The short-term (1 to 48 hours) predictability of wind power production from wind power plants in a power system is critical to the value of wind power. Advanced wind power prediction tools, based on numerical weather prediction models and designed for power system operators, are being developed and continuously improved. One objective of the EU-supported WILMAR (Wind power Integration in Liberalised electricity MARkets) project is to simulate the stochastic optimization of the operation of the Nordic and German power systems, in order to estimate the value of potential improvements of wind power prediction tools. For power system simulations including wind power, a model must be developed to simulate realistic wind speed predictions with adjustable accuracy, in which the correlations between wind speed prediction error at the spatially distributed wind power plants is accurate. The simulated wind speed predictions are then converted to aggregate wind power predictions for regions within the Nordic and German power systems. A Wind Speed Forecast Error Simulation Model, based on a multi-variate ARMA(1,1) time-series model, has been developed in Matlab. The accuracy of the model in representing real wind speed predictions in Denmark has been assessed, and various errors resulting from practical limitations of input data have been quantified.
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HOELTGEBAUM, HENRIQUE HELFER. "FORECAST OF THE JOINT DENSITY OF WIND CAPACITY FACTOR THROUGH THE USE OF A MULTIVARIATE GAS MODEL." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25286@1.
Повний текст джерелаАнотація:
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Neste trabalho usamos o arcabouço dos modelos GAS para gerar previsões conjuntas de fator de capacidade eólico, pertencentes a diferentes usinas localizadas em áreas geográficas distintas. Esses cenários são insumos para gerar uma distribuição de fluxo de caixa associada a um portfólio de contratos atrelados aos parques eólicos em questão. Inicialmente modelamos as densidades marginais via um modelo GAS, supondo densidade Beta. De maneira a capturar a estrutura de dependência entre esses fatores de capacidade, usamos uma cópula t-Student com a matriz de correlação também sendo atualizada via mecanismo GAS. Uma das contribuições importantes desse trabalho para o setor elétrico está na geração de cenários conjuntos apenas em um passo, evitando a necessidade de modelar variáveis transformadas e posteriormente transforma-las para retornar às suas respectivas escalas originais. Assim como é feito no caso supondo normalidade para as marginais. Como é sabido, exponenciar valores simulados a partir de uma densidade normal pode gerar resultados equivocados para fatores de capacidade eólico, e por propagação, isso pode afetar severamente as medidas de risco que são obtidas a partir da distribuição simulada de fluxo de caixa associada com o portfolio das usinas eólicas. Nossos resultados mostram que quando a dependência é levada em consideração, os fluxos de caixa tendem a ser maiores do que quando ignora-se a dependência.
In this work we use the framework of GAS models to generate joint forecasts for capacity factors of several wind plants belonging to different geographical areas. Such scenarios are then used as input to raise the distribution of cash flows associated with a portfolio of contracts attached to these wind plants. We first model the marginal density of each capacity factor using a GAS model with Beta density. In order to capture the observed dependence among these capacity factors, we use a copula t- Student with correlation matrix evolving through a GAS mechanism. One of the important contributions of our framework is that generation of scenarios is accomplished in just one step, avoiding the need of transforming back variables to its original scale, as it is the case under a Gaussian assumption for the marginals. As it is known, exponentiation of simulated Gaussian values can result in unrealistic sampling paths for the wind capacity factor, and by propagation, this can badly a ect the risk measures obtained from the simulated distribution of the cash flows associated with a particular portfolio of wind plants. Our results shows that when taking into account dependence the cash flows are higher than when ignoring dependence.
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Scharff, Richard. "Design of Electricity Markets for Efficient Balancing of Wind Power Generation." Doctoral thesis, KTH, Elektriska energisystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-171063.
Повний текст джерелаАнотація:
Deploying wind power to a larger extent is one solution to reduce negative environmental impacts of electric power supply. However, various challenges are connected with increasing wind power penetration levels. From the perspective of transmission system operators, this includes balancing of varying as well as - to some extent - uncertain generation levels. From the perspective of power generating companies, changes in the generation mix will affect the market's merit order and, hence, their profits. This thesis focuses on provision and use of flexibility in the Nordic electricity market. First, this thesis studies wind power variations and accuracy of wind power forecasts in Sweden using statistical methods. Even though today’s wind penetration levels are still low in Sweden, power systems and electricity markets have to cope with these characteristics of variations and forecast errors to a larger extent in future. Second, it investigates to which extent an increased exchange and use of flexibility that is available in the intraday time-frame could efficiently facilitate system balancing and whether this would also be profitable from the power generating companies' perspective. Here, a simulation model is developed that reflects important aspects of production planning and trading decisions in the intraday time-frame. In a first case study, it is shown that the benefits of internal rescheduling strongly depend on the costs to adjust production plans in the intraday time-frame as compared to real-time. In a second case study, it becomes evident that trading flexibility in the intraday time-frame can reduce the need for system balancing more efficiently than internal rescheduling within each balance responsible party. Motivated by the positive gains of intraday trading and the challenge of appropriately modelling continuous intraday markets, trading activity and price development on Elbas is investigated. The results provide insights into trading behaviour on a continuous intraday market and show that trading is not always in accordance to the power system's physical situation. To the extent to which better information and adaptations in the market design could improve the market participants' base for trading decisions, policy recommendations and further research questions areas suggested.Att använda vindkraft i en större utsträckning är en möjlighet att minska elproduktionens negativa miljöpåverkan. Det finns dock också olika utmaningar med stora mängder vindkraft. Från ett systemperspektiv gäller det till exempel att hålla balansen mellan tillförsel och konsumtion av el. Från elproducenternas perspektiv bör vindkraftens påverkan på elmarknaden nämnas eftersom det påverka aktörernas vinster. Avhandlingen titta närmare in i hur man kan få tillgång till mer flexibilitet på produktionssidan. Avhandlingen består av tre delar. För det första undersöks variationer och prognosfel av vindkraft i Sverige med hjälp av statistiska metoder. Även om andel vindkraft hittills är låg i Sverige, behöver elsystemet och elmarknader i framtiden hantera samma egenskaper av själva variationer och prognosfel som idag men i en större utsträckning. För det andra undersöks hur den flexibiliteten som finns i tidshorisonten några timmar innan leveranstimmen kan utnyttjas för att integrera vindkraften på ett sätt som är både fördelaktigt från systemets och från aktörernas perspektiv. Undersökningen sker med hjälp av en simuleringsmodell som omfattar viktiga delar i produktionsplanering och intradayhandel. I en fallstudie uppvisas att vinster av intern omplanering är i högsta grad beroende på kostnadsskillnaden mellan omplanering några timmar innan leveranstimmen och anpassning av körscheman under själva leveranstimmen. Resultat av ytterligare en fallstudie uppvisar att det är betydligt billigare och mer effektivt att använda intradayhandel istället för intern omplanering för att utnyttja den befintliga flexibiliteten och för att reducera obalanser som systemoperatörer annars behöver ta hand om under leveranstimmen. Detta är en anledning till att undersöka handelsmönster på Elbas som är en intradaymarknad med kontinuerlig handel. En annan anledning till den här tredje delen är utmaningarna i att modellera kontinuerlig intradayhandel. Studien beskriver handelsaktiviteten på Elbas och hur priserna utvecklas under handelstiden. Ett resultat är att handeln inte alltid återspeglar den fysiska situationen i elsystemet. I den utsträckningen som ett snabbare informationsflöde och förändringar i marknadsdesignen kunde förbättrar aktörernas underlag för intradayhandel, föreslås förbättringar och öppna forskningsfrågor.
QC 20150911
Elektra 36141: Korttidsplanering av vatten-värmekraftsystem vid stora mängder vindkraft: System-perspektivet
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Jones, Marcia L. "A prototype expert system to forecast severe winds in the western Mediterranean Sea." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/27227.
Повний текст джерела
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Gordon, Ronald Walter. "Impact of Assimilating Airborne Doppler Radar Winds on the Inner-Core Structure and Intensity of Hurricane Ike (2008)." Scholarly Repository, 2011. http://scholarlyrepository.miami.edu/oa_theses/276.
Повний текст джерелаАнотація:
Accurate prediction of Tropical Cyclones (TC) is vital for the protection of life and property in areas that are prone to their destructive forces. While significant improvements have been made in forecasting TC track, intensity remains a challenge. It is hypothesized that accurate TC intensity forecast requires, among other things, an adequate initial description of their inner-core region. Therefore, there must be reliable observations of the inner-core area of the TC and effective data assimilation (DA) methods to ingest these data into the Numerical Weather Prediction (NWP) models. However, these requirements are seldom met at the relatively low resolution of operational global prediction models and the lack of routine observations assimilated in the TC inner-core. This study tests the impacts of assimilating inner-core Airborne Doppler Radar (ADR) winds on the initial structure and subsequent intensity forecast of Hurricane Ike (2008). The 4-dimensional variational (4DVar) and the 3-dimensional variational (3DVar) methods are used to perform DA while the Weather Research and Forecasting (WRF) model is used to perform forecasts. It is found that assimilating data helps to initialize a more realistic inner-core structure using both DA methods. Additionally, the resulting short-term and long-term intensity forecasts are more accurate when data is assimilated versus cases when there is no DA. Additionally, it is found that in some cases the impact of DA lasts up to 12 hours longer with 4DVar versus 3DVar. It is shown that this is because the flow-dependent 4DVar method produces more dynamically and balanced analysis increments compared to the static and isotropic increments of 3DVar. However, the impact of using both methods is minimal in the long-range. The analyses show that at longer forecast range the dynamics of hurricane Ike was influenced more by outer environment features than the inner-core winds.
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Bartošík, Tomáš. "Metody simulace dodávky výkonu z větrných elektráren." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217592.
Повний текст джерелаАнотація:
Theme Master’s thesis was studying of wind energy power supply. Comparison of character of wind power supply in Czech Republic to power supply abroad. Thesis begins with short introduction of historical wind applications. It continues by theory of wind engines, the wind engines construction and its facilities. Next part describes wind energy characteristics and physics. It describes wind speed influence to power supply of wind turbine, a physical limits of wind engines efficiency. Later, meteorological forecast possibilities are mentioned. Following chapter classifies wind power plants by geographical locations and characterizes them. It presents and explains individual cases of wind energy business growth in Czech Republic and other countries. There are also mentioned many suitable locations for wind parks in Czech Republic. There are described data analysis methods in chapter number 5. Analysis results of day period graph and year period graphs are shown. Unsophisticated forecast model is sketched out and created in following chapter. Here the regressive analysis methods are described, such as Autoregressive moving average model (ARMA), which can bring satisfactory results. Another example is Markov switching autoregressive model (MSAR). Next step from statistic forecast models is to sophisticated large forecasting systems. Those systems require meteorological forecast data and historical wind power data. Data are analyzed by statistical models. They have been developed recently and they are ordinary used nowadays.
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Tino, Clayton P. "Wind models and stochastic programming algorithms for en route trajectory prediction and control." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50242.
Повний текст джерелаАнотація:
There is a need for a fuel-optimal required time of arrival (RTA) mode for aircraft flight management systems capable of enabling controlled time of arrival functionality in the presence of wind speed forecast uncertainty. A computationally tractable two-stage stochastic algorithm utilizing a data-driven, location-specific forecast uncertainty model to generate forecast uncertainty scenarios is proposed as a solution. Three years of Aircraft Communications Addressing and Reporting Systems (ACARS) wind speed reports are used in conjunction with corresponding wind speed forecasts from the Rapid Update Cycle (RUC) forecast product to construct an inhomogeneous Markov model quantifying forecast uncertainty characteristics along specific route through the national airspace system. The forecast uncertainty modeling methodology addresses previously unanswered questions regarding the regional uncertainty characteristics of the RUC model, and realizations of the model demonstrate a clear tendency of the RUC product to be positively biased along routes following the normal contours of the jet stream. A two-stage stochastic algorithm is then developed to calculate the fuel optimal stage one cruise speed given a required time of arrival at a destination waypoint and wind forecast uncertainty scenarios generated using the inhomogeneous Markov model. The algorithm utilizes a quadratic approximation of aircraft fuel flow rate as a function of cruising Mach number to quickly search for the fuel-minimum stage one cruise speed while keeping computational footprint small and ensuring RTA adherence. Compared to standard approaches to the problem utilizing large scale linear programming approximations, the algorithm performs significantly better from a computational complexity standpoint, providing solutions in fractional power time while maintaining computational tractability in on-board systems.
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Lönnberg, Joakim. "Short-term regulating capacity and operational patterns of The Lule River with large wind power penetration." Thesis, Uppsala universitet, Elektricitetslära, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-230972.
Повний текст джерелаАнотація:
The growing share of installed wind power in the Swedish electricity system has caused concerns whether the available regulating power will be sufficient. Several studies have examined the need of regulating power using both statistical and modelling approaches. However, there is a risk that some aspects of the short-term regulation of hydropower might have been missed. By using one of Vattenfall’s hydropower planning tools, the short-term operation of The Lule River has been simulated with an increasing penetration of wind power. The tool includes detailed models of reservoirs, generating units including efficiency curves and start/stop costs. By introducing a day-by-day simulation with a seven-day window price forecast, updated with a new wind forecast for each iteration, a 21-days scenario has been simulated. Transmission limits are disregarded and the thermal production is reduced with the average wind production. To quantify and compare the regulation capacity, the regulation factor is introduced. It reflects the ability to utilise high-price hours and considering that the need of regulating power for the short-term perspective is reflected in the price it will also reflect the regulation capacity. It is shown that the regulating factor is correlated to the discharge factor,whichis the relation between the maximum discharge to the average statistical discharge for a plant. A high discharge factor provides the flexibility to utilise the fluctuations in price. The discharge factor is adapted to the plants placement in the reach, accounting for both reservoirs located upstream and downstream, especially for The Lule River which has been designed to regulate for the fluctuations in the load. The flexibility required by the rest of the Nordic rivers is quantified for future studies. It is concluded that The Lule River is able to meet some of the fluctuations of wind power production due to the overcapacity ininstalled power. The production can, at the expense of decreased efficiency of the generating units, alter the production to suit a more fluctuating price.It is important to emphasise that The Lule River alone cannot balance a large penetration of wind power. To fully take into account the effects of a large penetration of wind power the study must be expanded to include more scenarios. The study should include different types of hydrological prerequisites and the seasonal variations in power production as well as additional rivers.
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Filho, Celso Luís de Oliveira. "Prognóstico das variáveis meteorológicas e da evapotranspiração de referência com o modelo de previsão do tempo GFS/NCEP." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/11/11131/tde-21082007-111326/.
Повний текст джерелаАнотація:
Avaliou-se o desempenho de um modelo numérico de previsão do tempo (GFS - Global Forecast System – antigo AVN – AViatioN model - do Centro Nacional para Previsão Ambiental – NCEP) no prognóstico de variáveis meteorológicas temperatura, déficit de pressão de vapor do ar, saldo de radiação e velocidade do vento, e da evapotranspiração de referência calculada pelos métodos de Thornthwaite (1948) e de Penman-Monteith (Allen et al., 1998). O desempenho foi avaliado por comparação com dados provenientes de uma estação meteorológica, situada em Piracicaba, São Paulo. A temperatura e o déficit de pressão de vapor do ar foram os elementos melhor prognosticados, com desempenho "muito bom" e "bom", de acordo com o índice de desempenho proposto por Camargo e Sentelhas (1997), para no máximo quatro e três dia de antecedência, respectivamente, durante o período seco. Para o período úmido, somente o prognóstico do déficit de pressão de vapor do ar para o primeiro dia mostrou-se "bom". Os prognósticos de saldo de radiação e velocidade do vento foram ruins para ambos os períodos. Em decorrência do bom desempenho do modelo para prognosticar a temperatura, verificou-se que a estimativa de ETo pelo método de Thornthwaite teve boa concordância com o calculado a partir dos dados da estação meteorológica, com antecedência de até três dias para o período seco. Para o úmido, este fato foi observado apenas para o primeiro dia de antecedência. A concordância entre os valores estimados pelo modelo e a partir da estação para o método de Penman-Monteith foi muito baixa, em conseqüência do desempenho do modelo de previsão do tempo em prognosticar o saldo de radiação e a velocidade do vento.The performance of a numeric weather forecast model (GFS- Forecast System, former AVN - AvatioN model, National Center for Environmental Prediction-NCEP) was evaluated for predicting weather variables, like air temperature and vapor pressure deficit, net radiation and wind speed, as well as reference evapotranspiration calculated by Thornthwaite (1948) and Penman-Monteith (Allen et al., 1948) methods, by the comparison with data obtained by an automatic weather station, in Piracicaba, State of São Paulo, Brazil. Temperature and vapor pressure deficit were the variables predicted with the best accuracy, with a "very good" and "good" performance, according to the index of confidence proposed by Camargo and Sentelhas (1997), for the maximum of four and three days in advance, respectively, during the dry season. For the wet season, only vapor pressure deficit was predicted with a "good" performance of the model. The predictions of net radiation and wind speed were very poor for both seasons. As the weather forecast model predicted temperature well, ETo estimated by Thornthwaite method showed a good agreement with ETo values estimated by observed data from the weather station, with till three days in advance for the dry season. For the wet season, such agreement was observed just for one day in advance. When ETo estimated by Penman-Monteith method with data from the weather forecast model and from weather station were compared any agreement was observed, which was caused by the poor performance of the numeric weather forecast model to predict net radiation and wind speed.
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Музика, М. О. "Обґрунтування перспектив розвитку виноробної галузі на мезорівні (на прикладі Одеської області)". Thesis, Одеський національний економічний університет, 2020. http://dspace.oneu.edu.ua/jspui/handle/123456789/12431.
Повний текст джерелаАнотація:
У роботі розглядаються теоретичні аспекти розвитку виноробної галузі, її потенціал та основні визначення, досліджено особливості державного та зарубіжного регулювання виноробства. Проаналізовано основні тенденцій розвитку виноробної галузі у світі та Україні. Проведено аналіз стану виноробної галузі в Одеській області та здійснено оцінка основних перешкод її розвитку. Визначено основні конкурентні переваги та стратегічні завдання розвитку виноробної галузі регіону. На онові проведеного дослідження запропоновано прогноз перспектив розвитку.The theoretical aspects of the development of wine-producing branch have been considered in the master thesis. The potential, necessity and specialness of the national and foreign regulation of winemaking has been revealed. The main indicators of the development tendencies of wine branch in the world and Ukraine have been analyzed. The analysis of the winery hall in the Odessa region has been carried out and the assessment of the main changes in development has been completed. The key competitive trials and strategic development of the wine region have been designated. The forecast of the development prospects has been propounded on the basis of the last update.
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Haessig, Pierre. "Dimensionnement et gestion d’un stockage d’énergie pour l'atténuation des incertitudes de production éolienne." Thesis, Cachan, Ecole normale supérieure, 2014. http://www.theses.fr/2014DENS0030/document.
Повний текст джерелаАнотація:
Le contexte de nos travaux de thèse est l'intégration de l'énergie éolienne sur les réseaux insulaires. Ces travaux sont soutenus par EDF SEI, l'opérateur électrique des îles françaises. Nous étudions un système éolien-stockage où un système de stockage d'énergie doit aider un producteur éolien à tenir, vis-à-vis du réseau, un engagement de production pris un jour à l'avance. Dans ce contexte, nous proposons une démarche pour l'optimisation du dimensionnement et du contrôle du système de stockage (gestion d'énergie). Comme les erreurs de prévision J+1 de production éolienne sont fortement incertaines, la gestion d'énergie du stockage est un problème d'optimisation stochastique (contrôle optimal stochastique). Pour le résoudre, nous étudions tout d'abord la modélisation des composants du système (modélisation énergétique du stockage par batterie Li-ion ou Sodium-Soufre) ainsi que des entrées (modélisation temporelle stochastique des entrées incertaines). Nous discutons également de la modélisation du vieillissement du stockage, sous une forme adaptée à l'optimisation de la gestion. Ces modèles nous permettent d'optimiser la gestion de l'énergie par la méthode de la programmation dynamique stochastique (SDP). Nous discutons à la fois de l'algorithme et de ses résultats, en particulier de l'effet de la forme des pénalisations sur la loi de gestion. Nous présentons également l'application de la SDP sur des problèmes complémentaires de gestion d'énergie (lissage de la production d'un houlogénérateur, limitation des rampes de production éolienne). Cette étude de l'optimisation de la gestion permet d'aborder l'optimisation du dimensionnement (choix de la capacité énergétique). Des simulations temporelles stochastiques mettent en évidence le fort impact de la structure temporelle (autocorrélation) des erreurs de prévision sur le besoin en capacité de stockage pour atteindre un niveau de performance donné. La prise en compte de paramètres de coût permet ensuite l'optimisation du dimensionnement d'un point de vue économique, en considérant les coûts de l'investissement, des pertes ainsi que du vieillissement. Nous étudions également le dimensionnement du stockage lorsque la pénalisation des écarts à l'engagement comporte un seuil de tolérance. Nous terminons ce manuscrit en abordant la question structurelle de l'interaction entre l'optimisation du dimensionnement et celle du contrôle d'un système de stockage, car ces deux problèmes d'optimisation sont couplésThe context of this PhD thesis is the integration of wind power into the electricity grid of small islands. This work is supported by EDF SEI, the system operator for French islands. We study a wind-storage system where an energy storage is meant to help a wind farm operator fulfill a day-ahead production commitment to the grid. Within this context, we propose an approach for the optimization of the sizing and the control of the energy storage system (energy management). Because day-ahead wind power forecast errors are a major source of uncertainty, the energy management of the storage is a stochastic optimization problem (stochastic optimal control). To solve this problem, we first study the modeling of the components of the system. This include energy-based models of the storage system, with a focus on Lithium-ion and Sodium-Sulfur battery technologies. We then model the system inputs and in particular the stochastic time series like day-ahead forecast errors. We also discuss the modeling of storage aging, using a formulation which is adapted to the control optimization. Assembling all these models enables us to optimize the energy management of the storage system using the stochastic dynamic programming (SDP) method. We introduce the SDP algorithms and present our optimization results, with a special interest for the effect of the shape of the penalty function on the energy control law. We also present additional energy management applications with SDP (mitigation of wind power ramps and smoothing of ocean wave power). Having optimized the storage energy management, we address the optimization of the storage sizing (choice of the rated energy). Stochastic time series simulations show that the temporal structure (autocorrelation) of wind power forecast errors have a major impact on the need for storage capacity to reach a given performance level. Then we combine simulation results with cost parameters, including investment, losses and aging costs, to build a economic cost function for sizing. We also study storage sizing when the penalization of commitment deviations includes a tolerance threshold. We finish this manuscript with a structural study of the interaction between the optimizations of the sizing and the control of an energy storage system, because these two optimization problems are coupled
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Franěk, Lukáš. "Flight Management System Model." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219075.
Повний текст джерелаАнотація:
Diplomová práce shrnuje nejdůležitější informace o letectví, jako například základní používané termíny, popis letových fází apod. V této práci je popsán flight management system, jeho funkce a schopnosti vytvořit cenově příznivý a současně absolutně spolehlivý letový plán. V další části práce je nastíněna důležitost předpovědi počasí pro bezpečnou a současně cenově příznivou leteckou dopravu. Tato práce je vytvořena v programu Matlab a všechny bloky jsou naprogramovány jako m-funkce. Důležité části kódu jsou z důvodu názornosti zobrazeny jako vývojové diagramy. Praktická část práce je rozdělena do několika podkapitol, kde každá podkapitola popisuje jeden blok z blokového schématu pro výpočet nejistoty odhadované doby příletu. Současně je zde vysvětlena funkce ostatních bloků pro plánování letu, předpověď počasí, kombinování větrů a výpočet odhadnuté doby příletu a její nejistoty.
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Novozámský, Adam. "Střih větru jako nebezpečný jev v letectví." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-231313.
Повний текст джерелаАнотація:
This thesis is about wind shear and its influence on aviation. There is wide theoretical description of wind shear and it’s implemented into aviation practice. There are also methods of observation, forecasting and reporting, for both pilots and meteorologist described. In this thesis, pilots can find useful recommendations for dealing with wind shear during the flight. Every reader can also read about famous aviation accidents and look at models of weather that caused those accidents. At the end of thesis there are analyses of a rawiosonde measurements related to wind shear reports. This whole thesis is focused on increasing awareness of wind shear in aviation from angle of view of both pilots and aviation meteorologist.
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Scheele, Kyle Fred. "Wind forecast verification : a study in the accuracy of wind forecasts made by the Weather Channel and AccuWeather." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-08-4234.
Повний текст джерелаАнотація:
The Weather Channel (TWC) and AccuWeather (AWX) are leading providers of weather information to the general public. The purpose of this Master’s Report is to examine the wind speed forecasts made by these two providers and determine their reliability and accuracy. The data used within this report was collected over a 12-month period at 51 locations across the state of Texas. The locations were grouped according to wind power class, which ranged from Class 1 to Class 4. The length of the forecast period was 9 days for TWC and 14 days for AWX.
It was found that the values forecasted by TWC were generally not well calibrated, but were never far from being perfectly calibrated and always demonstrated positive skill. The sharpness of TWC’s forecasts decreased consistently with lead time, allowing them to maintain a skill score greater than the climatological average throughout the forecast period. TWC tended to over-forecast wind speed in short term forecasts, especially within the lower wind power class regions. AWX forecasts were found to have positive skill the first 6 days of the forecasting period before becoming near zero or negative. AWX’s forecasts maintained a fairly high sharpness throughout the forecast period, which helped contribute to increasingly un-calibrated forecast values and negative skill in longer term forecasts. The findings within this report should help provide a better understanding of the wind forecasts made by TWC and AWX, and determine the strengths and weaknesses of both companies.text
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"Short-Term Wind Power Forecasts using Doppler Lidar." Master's thesis, 2014. http://hdl.handle.net/2286/R.I.27540.
Повний текст джерелаАнотація:
abstract: With a ground-based Doppler lidar on the upwind side of a wind farm in the Tehachapi Pass of California, radial wind velocity measurements were collected for repeating sector sweeps, scanning up to 10 kilometers away. This region consisted of complex terrain, with the scans made between mountains. The dataset was utilized for techniques being studied for short-term forecasting of wind power by correlating changes in energy content and of turbulence intensity by tracking spatial variance, in the wind ahead of a wind farm. A ramp event was also captured and its propagation was tracked.
Orthogonal horizontal wind vectors were retrieved from the radial velocity using a sector Velocity Azimuth Display method. Streamlines were plotted to determine the potential sites for a correlation of upstream wind speed with wind speed at downstream locations near the wind farm. A "virtual wind turbine" was "placed" in locations along the streamline by using the time-series velocity data at the location as the input to a modeled wind turbine, to determine the extractable energy content at that location. The relationship between this time-dependent energy content upstream and near the wind farm was studied. By correlating the energy content with each upstream location based on a time shift estimated according to advection at the mean wind speed, several fits were evaluated. A prediction of the downstream energy content was produced by shifting the power output in time and applying the best-fit function. This method made predictions of the power near the wind farm several minutes in advance. Predictions were also made up to an hour in advance for a large ramp event. The Magnitude Absolute Error and Standard Deviation are presented for the predictions based on each selected upstream location.Dissertation/Thesis
Masters Thesis Mechanical Engineering 2014
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CHEN, YI-CHENG, and 陳奕成. "Application of Deep Learning Techniques to Wind Power Forecasts." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/r72g79.
Повний текст джерелаАнотація:
碩士逢甲大學
產業研發碩士班
107
In response to the issues of climate change and the pursuit of sustainable development in the world. In renewable energy, wind power is an indispensable part. Taiwan has a good geographical area in the west, as well as the promotion of conditions and policies, and the cost reduction year by year. They are all making Taiwan more suitable for developing wind power, as well as conducting related research and evaluating its benefits. This paper proposes to combine the two methods of Convolutional Neural Network (CNN) and Long Short Term Memory Network (LSTM) based on deep learning. These techniques take advantages of the CNN's expertise in feature extraction and LSTM are good at processing time series data. Then, the results are subjected to point forecast and probabilistic forecast evaluation. In them also uses multi-step forecast to output and observe the forecast results at different time points. The probabilistic forecast uses Empirical Cumulative Distribution Function (ECDF) to define the upper and lower limits of the prediction interval. In order to prove the accuracy of the proposed method for wind power generation, the CNN model and LSTM models are also predicted to verify whether the proposed model is complete and the effective features in the data are extracted to improve learning accuracy and predict. In this way, the three-party comparison is carried out with point forecast, probabilistic forecast, multi-step forecast, multi-step probabilistic forecast and probabilistic forecast results of different prediction intervals to verify that the proposed model performance and its stability are better than the other two methods. Index Terms- Deep learning, machine learning, point forecast, probabilistic forecast, multi-step forecast, CNN, LSTM, feature extraction, ECDF
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Tsai, Sheng-Hang, and 蔡盛行. "A Study on the Suspended Particulate Concentration Forecasts using Back-Propagation Neural Network for Controlling Wind and Sand Disasters." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/54301662797486498421.
Повний текст джерелаАнотація:
碩士國立嘉義大學
土木與水資源工程學系研究所
99
In this research, we estimate the wind and sand disasters around Taitung City with the back-propagation neural network (BPN). The goal of research includes: (1) to estimate and analyze the correlation between meteorological factor and the suspended particulate matter density PM10 and (2) to suggest effective solutions and prevention measures for wind and sand disasters around Taitung City based on data collection and investigation in accordance with the BPN forecasting results. This study applies separated data (summer and winter) for network testing to examine the effect of seasonality on prediction results. Using the forward selection in the whole-year set, the results show that the optimum grouping of input variables includes the PM10 concentration, relative humidity, wind velocity, and wind direction. The correlation coefficient between network test value and the actual concentration of suspended particulates is R = 0.636. For the network predictions in summer and winter, the results in winter is better and the best combination of input variables is the suspended particles, relative humidity, wind speed, and rainfall, with the correlation coefficient of R = 0.72. The summer data yields worse test results: the top combination of input variables is suspended particulates, relative humidity, wind velocity, temperature, and rainfall, with the correlation coefficient of R = 0.548. This research develops an engineering control method based on the data collection and investigation, in accordance with the BPN forecasting results. Summer engineering methods are mainly used in a long-term engineering base by setting up self-propelled sprinkler system and outsourcing sprinkler. This mechanism starts before the arrival of the typhoon and then repairs and adds the sprinkler gun systems at the levee on Chung Hua Bridge. In winter, besides the self-propelled sprinkler systems, sprinkler gun systems and outsourcing sprinkler, the short-term engineering methods can also set up sand fence in the estuary area and apply the island-hopping-type vegetation to prevent wind and sand disasters.
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Daniel, Lucky Oghenechodja. "Short term wind power forecasting in South Africa using neural networks." Diss., 2020. http://hdl.handle.net/11602/1591.
Повний текст джерелаАнотація:
MSc (Statistics)Department of Statistics
Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed for wind energy generation, using data obtained from the South African Wind Atlas Project. Forecasting is carried out on a two days ahead time horizon. We investigate the predictive performance of artificial neural networks (ANN) trained with Bayesian regularisation, decision trees based stochastic gradient boosting (SGB) and generalised additive models (GAMs). The results of the comparative analysis suggest that ANN displays superior predictive performance based on root mean square error (RMSE). In contrast, SGB shows outperformance in terms of mean average error (MAE) and the related mean average percentage error (MAPE). A further comparison of two forecast combination methods involving the linear and additive quantile regression averaging show the latter forecast combination method as yielding lower prediction accuracy. The additive quantile regression averaging based prediction intervals also show outperformance in terms of validity, reliability, quality and accuracy. Interval combination methods show the median method as better than its pure average counterpart. Point forecasts combination and interval forecasting methods are found to improve forecast performance.
NRF