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Academic literature on the topic 'SOLAR POWER FORECASTING'

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Published: 11 September 2023

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Journal articles on the topic "SOLAR POWER FORECASTING"

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El hendouzi, Abdelhakim, and Abdennaser Bourouhou. "Solar Photovoltaic Power Forecasting." Journal of Electrical and Computer Engineering 2020 (December 31, 2020): 1–21. http://dx.doi.org/10.1155/2020/8819925.

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The management of clean energy is usually the key for environmental, economic, and sustainable developments. In the meantime, the energy management system (EMS) ensures the clean energy which includes many sources grouped in a small power plant such as microgrid (MG). In this case, the forecasting methods are used for helping the EMS and allow the high efficiency to the clean energy. The aim of this review paper is providing the necessary data about the basic principles and standards of photovoltaic (PV) power forecasting by stating numerous research studies carried out on the PV power forecasting topic specifically in the short-term time horizon which is advantageous for the EMS and grid operator. At the same time, this contribution can offer a state of the art in different methods and approaches used for PV power forecasting along with a careful study of different time and spatial horizons. Furthermore, this current review paper can support the tenders in the PV power forecasting.
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K., D., and Isha I. "Solar Power Forecasting: A Review." International Journal of Computer Applications 145, no. 6 (July 15, 2016): 28–50. http://dx.doi.org/10.5120/ijca2016910728.

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Iheanetu, Kelachukwu J. "Solar Photovoltaic Power Forecasting: A Review." Sustainability 14, no. 24 (December 19, 2022): 17005. http://dx.doi.org/10.3390/su142417005.

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The recent global warming effect has brought into focus different solutions for combating climate change. The generation of climate-friendly renewable energy alternatives has been vastly improved and commercialized for power generation. As a result of this industrial revolution, solar photovoltaic (PV) systems have drawn much attention as a power generation source for varying applications, including the main utility-grid power supply. There has been tremendous growth in both on- and off-grid solar PV installations in the last few years. This trend is expected to continue over the next few years as government legislation and awareness campaigns increase to encourage a shift toward using renewable energy alternatives. Despite the numerous advantages of solar PV power generation, the highly variable nature of the sun’s irradiance in different seasons of various geopolitical areas/regions can significantly affect the expected energy yield. This variation directly impacts the profitability or economic viability of the system, and cannot be neglected. To overcome this challenge, various procedures have been applied to forecast the generated solar PV energy. This study provides a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures. It critically analyzes recent studies on solar PV power forecasting to highlight the strengths and weaknesses of the techniques or models implemented. The clarity provided will form a basis for higher accuracy in future models and applications.
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Kim, Kihan, and Jin Hur. "Weighting Factor Selection of the Ensemble Model for Improving Forecast Accuracy of Photovoltaic Generating Resources." Energies 12, no. 17 (August 28, 2019): 3315. http://dx.doi.org/10.3390/en12173315.

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Among renewable energy sources, solar power is rapidly growing as a major power source for future power systems. However, solar power has uncertainty due to the effects of weather factors, and if the penetration rate of solar power in the future increases, it could reduce the reliability of the power system. A study of accurate solar power forecasting should be done to improve the stability of the power system operation. Using the empirical data from solar power plants in South Korea, the short-term forecasting of solar power outputs were carried out for 2016. We performed solar power forecasting with the support vector regression (SVR) model, the naïve Bayes classifier (NBC), and the hourly regression model. We proposed the ensemble method including the selection of weighting factors for each model to improve forecasting accuracy. The forecasted solar power generation error was indicated using normalized mean absolute error (NMAE) compared to the plant capacity. For the ensemble method, the results of each forecasting model were weighted with the reciprocal of the standard deviation of the forecast error, thus improving the solar power outputs forecast accuracy.
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Divya, R., and S. Umamaheswari. "Solar Power Forecasting Methods – A Review." International Journal of Advanced Science and Engineering 9, no. 1 (August 1, 2022): 2591–98. http://dx.doi.org/10.29294/ijase.9.1.2022.2591-2598.

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Okhorzina, Alena, Alexey Yurchenko, and Artem Kozloff. "Autonomous Solar-Wind Power Forecasting Systems." Advanced Materials Research 1097 (April 2015): 59–62. http://dx.doi.org/10.4028/www.scientific.net/amr.1097.59.

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The paper reports on the results of climatic testing of silicon photovoltaic modules and photovoltaic power systems conducted in Russia (Siberia and the Far East). The monitoring system to control the power system work was developed. Testing over 17 years and a large amount of experimental studies enabled us to develop a precise mathematical model of the photovoltaic module in natural environment taking into account climatic and hardware factors.
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Bacher, Peder, Henrik Madsen, and Henrik Aalborg Nielsen. "Online short-term solar power forecasting." Solar Energy 83, no. 10 (October 2009): 1772–83. http://dx.doi.org/10.1016/j.solener.2009.05.016.

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Kumar, R. Dhilip, Prakash K, P. Abirama Sundari, and Sathya S. "A Hybrid Machine Learning Model for Solar Power Forecasting." E3S Web of Conferences 387 (2023): 04003. http://dx.doi.org/10.1051/e3sconf/202338704003.

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The paper presents a near investigation of different AI procedures for solar power forecasting. The objective of the research is to identify the most accurate and efficient machine learning algorithms for solar power forecasting. The paper also considers different parameters such as weather conditions, solar radiation, and time of day in the forecasting model. This paper proposes a hybrid machine learning model for solar power forecasting that consolidates the strengths of multiple algorithms, including support vector regression, random forest regression, and artificial neural network. However, the study also highlights the importance of incorporating domain knowledge and feature engineering in machine learning models for better forecasting accuracy.
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Nath, N. C., W. Sae-Tang, and C. Pirak. "Machine Learning-Based Solar Power Energy Forecasting." Journal of the Society of Automotive Engineers Malaysia 4, no. 3 (September 1, 2020): 307–22. http://dx.doi.org/10.56381/jsaem.v4i3.25.

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The expanding interest in energy is one of the main motivations behind the integration of solar energy into electric grids or networks. The exact expectation of solar oriented irradiance variety can improve the nature of administration. This coordination of solar-based vitality and exact expectations can help in better arranging and distributing of energy. Discovering vitality sources to fulfil the world’s developing interest is one of the general public’s major difficulties for the coming fifty years. In this research, two machine learning techniques utilized for hourly solar power forecasting are presented. The solar power prediction model becomes robust and efficient for solar power energy forecasting once the redundant information is removed from raw data, experimental data is transformed into a settled range, the best features selection method is chosen, four different weather profiles are made based on different weather conditions and the right time series machine learning algorithm is chosen.
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Arias, Mariz B., and Sungwoo Bae. "Design Models for Power Flow Management of a Grid-Connected Solar Photovoltaic System with Energy Storage System." Energies 13, no. 9 (April 29, 2020): 2137. http://dx.doi.org/10.3390/en13092137.

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This paper provides models for managing and investigating the power flow of a grid-connected solar photovoltaic (PV) system with an energy storage system (ESS) supplying the residential load. This paper presents a combination of models in forecasting solar PV power, forecasting load power, and determining battery capacity of the ESS, to improve the overall quality of the power flow management of a grid-connected solar PV system. Big data tools were used to formulate the solar PV power forecasting model and load power forecasting model, in which real historical solar electricity data of actual solar homes in Australia were used to improve the quality of the forecasting models. In addition, the time-of-use electricity pricing was also considered in managing the power flow, to provide the minimum cost of electricity from the grid to the residential load. The output of this model presents the power flow profiles, including the solar PV power, battery power, grid power, and load power of weekend and weekday in a summer season. The battery state-of-charge of the ESS was also presented. Therefore, this model may help power system engineers to investigate the power flow of each system of a grid-connected solar PV system and help in the management decision for the improvement of the overall quality of the power management of the system.
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Dissertations / Theses on the topic "SOLAR POWER FORECASTING"

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Wang, Zheng. "Solar Power Forecasting." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21248.

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Solar energy is a promising environmentally-friendly energy source. Yet its variability affects negatively the large-scale integration into the electricity grid and therefore accurate forecasting of the power generated by PV systems is needed. The objective of this thesis is to explore the possibility of using machine learning methods to accurately predict solar power. We first explored the potential of instance-based methods and proposed two new methods: the data source weighted nearest neighbour (DWkNN) and the extended Pattern Sequence Forecasting (PSF) algorithms. DWkNN uses multiple data sources and considers their importance by learning the best weights based on previous data. PSF1 and PSF2 extended the standard PSF algorithm deal with data from multiple related time series. Then, we proposed two clustering-based methods for PV power prediction: direct and pair patterns. We used clustering to partition the days into groups with similar weather characteristics and then created a separate PV power prediction model for each group. The direct clustering groups the days based on their weather profiles, while the pair patterns consider the weather type transition between two consecutive days. We also investigated ensemble methods and proposed static and dynamic ensembles of neural networks. We first proposed three strategies for creating static ensembles based on random example and feature sampling, as well as four strategies for creating dynamic ensembles by adaptively updating the weights of the ensemble members based on past performance. We then explored the use of meta-learning to further improve the performance of the dynamic ensembles. The methods proposed in this thesis can be used by PV plant and electricity market operators for decision making, improving the utilisation of the generated PV power, planning maintenance and also facilitating the large-scale integration of PV power in the electricity grid.
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Isaksson, Emil, and Conde Mikael Karpe. "Solar Power Forecasting with Machine Learning Techniques." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229065.

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The increased competitiveness of solar PV panels as a renewable energy source has increased the number of PV panel installations in recent years. In the meantime, higher availability of data and computational power have enabled machine learning algorithms to perform improved predictions. As the need to predict solar PV energy output is essential for many actors in the energy industry, machine learning and time series models can be employed towards this end. In this study, a comparison of different machine learning techniques and time series models is performed across five different sites in Sweden. We find that employing time series models is a complicated procedure due to the non-stationary energy time series. In contrast, machine learning techniques were more straightforward to implement. In particular, we find that the Artificial Neural Networks and Gradient Boosting Regression Trees perform best on average across all sites.
Sänkta produktionskostnader och ökad effektivitet har de senaste åren gjort solceller till ett attraktivt alternativ som energikälla. Detta har lett till en stor ökning av dess användning runt om i världen. Parallellt med denna utveckling har större tillgänglighet av data samt datorers förbättrade beräkningskapacitet möjliggjort förbättrade prediktionsresultat för maskininlärningsmetoder. Det finns för många aktörer anledning att intressera sig för prediktion av solcellers energiproduktion och från denna utgångspunkt kan maskininlärningsmetoder samt tidsserieanalys användas. I denna studie jämför vi hur metoder från de båda fälten presterar på fem olika geografiska områden i Sverige. Vi finner att tidsseriemodeller är komplicerade att implementera på grund av solcellernas icke-stationära tidsserier. I kontrast till detta visar sig maskininlärningstekniker enklare att implementera. Specifikt finner vi att artificiella neurala nätverk och så kallade Gradient Boosting Regression Trees presterar bäst i genomsnitt över de olika geografiska områdena.
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Almquist, Isabelle, Ellen Lindblom, and Alfred Birging. "Workplace Electric Vehicle Solar Smart Charging based on Solar Irradiance Forecasting." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-323319.

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The purpose of this bachelor thesis is to investigate different outcomes of the usage of photovoltaic (PV) power for electric vehicle (EV) charging adjacent to workplaces. In the investigated case, EV charging stations are assumed to be connected to photovoltaic systems as well as the electricity grid. The model used to simulate different scenarios is based on a goal of achieving constant power exchange with the grid by adjusting EV charging to a solar irradiance forecast. The model is implemented in MATLAB. This enables multiple simulations for varying input parameters. Data on solar irradiance are used to simulate the expected PV power generation. Data on driving distances are used to simulate hourly electricity demands of the EVs at the charging stations. A sensitivity analysis, based on PV irradiance that deviates from the forecast, is carried out. The results show what power the grid needs to have installed capacity for if no PV power system is installed. Furthermore, appropriate PV power installation sizes are suggested. The suggestions depend on whether the aim is to achieve 100 percent self-consumption of PV generated power or full PV power coverage of charging demands. For different scenarios, PV power installations appropriate for reducing peak powers on the grid are suggested. The sensitivity analysis highlights deviations caused by interference in solar irradiance.
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Kim, Byungyu. "Solar Energy Generation Forecasting and Power Output Optimization of Utility Scale Solar Field." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2149.

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The optimization of photovoltaic (PV) power generation system requires an accurate system performance model capable of validating the PV system optimization design. Currently, many commercial PV system modeling programs are available, but those programs are not able to model PV systems on a distorted ground level. Furthermore, they were not designed to optimize PV systems that are already installed. To solve these types of problems, this thesis proposes an optimization method using model simulations and a MATLAB-based PV system performance model. The optimization method is particularly designed to address partial shading issues often encountered in PV system installed on distorted ground. The MATLAB-based model was validated using the data collected from the Cal Poly Gold Tree Solar Field. It was able to predict the system performance with 96.4 to 99.6 percent accuracy. The optimization method utilizes the backtracking algorithm already installed in the system and the pitch distance to control the angle of the tracker and reduces solar panels partial shading on the adjacent row to improve system output. With pitch distances reduced in the backtracking algorithm between 2.5 meters and 3 meters, the inverter with inter-row shading can expect a 10.4 percent to 28.9 percent increase in power production. The implementation and calibration of this optimization method in the field this spring was delayed due to COVID-19. The field implementation is now expected to start this summer.
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D, Pepe. "New techniques for solar power forecasting and building energy management." Doctoral thesis, Università di Siena, 2019. http://hdl.handle.net/11365/1072873.

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The electrical grid can no longer be considered a unidirectional means of distributing energy from conventional plants to the final users, but a Smart Grid, where strong interaction between producers and users takes place. In this context, the importance of independent renewable generation is constantly increasing, and new tools are needed in order to reliably manage conventional power plant operation, grid balancing, real-time unit dispatching, demand constraints and energy market requirements. This dissertation is focused on two aspects of this general problem: cost-optimal management of smart buildings in a Demand-Response framework, and estimation of photovoltaic generation forecasting models. In the first part of this thesis a novel Model Predictive Control approach for integrated management of HVAC, electrical and thermal storage, and photovoltaic generation in building is presented. The proposed methodology also considers participation of the building in a Demand-Response program, which allows the consumer to become an active player in the electricity system. The related optimization problems turn out to be computationally appealing, even uncertainty sources is also addressed by means of a two-step procedure. The second part deals with the problem of estimating photovoltaic generation forecasting models in scenarios where measurements of meteorological variables (i.e., solar irradiance and temperature) at the plant site are not available. This scenario is relevant to electricity network operation, when a large number of photovoltaic plants are deployed in the grid. In particular, two methods have been developed. The first approach makes use of raw cloud cover data provided by a weather service combined with power generation measurements to estimate the parameters of a novel class of models. The second approach is based on a set of tests performed on the generated power time series aimed at detecting data portions that were generated under clear sky conditions. These data are then used for fit the parameters of the PVUSA model to the theoretical clear sky irradiance. All the methods covered in this thesis have been extensively validated either using industry-standard simulation frameworks or via experiments performed on real data.
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Rudd, Timothy Robert. "BENEFITS OF NEAR-TERM CLOUD LOCATION FORECASTING FOR LARGE SOLAR PV." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/597.

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As the ‘green’ energy movement continues to gain momentum, photovoltaic generation is becoming an increasingly popular source for new power generation. The primary focus of this paper is to demonstrate the benefits of close-to real-time cloud sensing for Photovoltaic generation. In order to benefit from this close-to real-time data, a source of cloud cover information is necessary. This paper looks into the potential of point insolation sensors to determine overhead cloud coverage. A look into design considerations and economic challenges of implementing such a monitoring system is included. The benefits of cloud location sensing are examined using computer simulations to target important time-scales and options available to plant operators. Finally, the economics of advanced forecasting options will be examined in order to determine the benefit to plant operators.
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van, der Meer Dennis. "Spatio-temporal probabilistic forecasting of solar power, electricity consumption and net load." Licentiate thesis, Uppsala universitet, Fasta tillståndets fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-363448.

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The increasing penetration of renewable energy sources into the electricity generating mix poses challenges to the operational performance of the power system. Similarly, the push for energy efficiency and demand response—i.e., when electricity consumers are encouraged to alter their demand depending by means of a price signal—introduces variability on the consumption side as well. Forecasting is generally viewed as a cost-efficient method to mitigate the adverse effects of the aforementioned energy transition because it enables a grid operator to reduce the operational risk by, e.g., unit-commitment or curtailment. However, deterministic—or point—forecasting is currently still the norm. This thesis focuses on probabilistic forecasting, a method with which the uncertainty ac- companying the forecast is expressed by means of a probability distribution. In this framework, the thesis contributes to the current state-of-the-art by investigating properties of probabilistic forecasts of PV power production, electricity consumption and net load at the residential and distribution level of the electricity grid. The thesis starts with an introduction to probabilistic forecasting in general and two models in specific: Gaussian processes and quantile regression. The former model has been used to produce probabilistic forecasts of PV power production, electricity consumption and net load of individual residential buildings—particularly challenging due to the stochasticity involved— but important for home energy management systems and potential peer-to-peer energy trading. Furthermore, both models have been utilized to investigate what effects spatial aggregation and increasing penetration have on the predictive distribution. The results indicated that only 20- 25 customers—out of a data set containing 300 customers—need to be aggregated in order to improve the reliability of the probabilistic forecasts. Finally, this thesis explores the potential of Gaussian process ensembles, which is an effective way to improve the accuracy of the forecasts.
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Barbieri, Florian Benjamin Eric. "Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking." Thesis, Curtin University, 2019. http://hdl.handle.net/20.500.11937/77126.

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Tracking clouds with a sky camera within a very short horizon below thirty seconds can be a solution to mitigate the effects of sunlight disruptions. A Probability Hypothesis Density (PHD) filter and a Cardinalised Probability Hypothesis Density (CPHD) filter were used on a set of pre-processed sky images. Both filters have been compared with the state-of-the-art methods for performance. It was found that both filters are suitable to perform very-short term irradiance forecasting.
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Uppling, Hugo, and Adam Eriksson. "Single and multiple step forecasting of solar power production: applying and evaluating potential models." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384340.

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The aim of this thesis is to apply and evaluate potential forecasting models for solar power production, based on data from a photovoltaic facility in Sala, Sweden. The thesis evaluates single step forecasting models as well as multiple step forecasting models, where the three compared models for single step forecasting are persistence, autoregressive integrated moving average (ARIMA) and ARIMAX. ARIMAX is an ARIMA model that also takes exogenous predictors in consideration. In this thesis the evaluated exogenous predictor is wind speed. The two compared multiple step models are multiple step persistence and the Gaussian process (GP). Root mean squared error (RMSE) is used as the measurement of evaluation and thus determining the accuracy of the models. Results show that the ARIMAX models performed most accurate in every simulation of the single step models implementation, which implies that adding the exogenous predictor wind speed increases the accuracy. However, the accuracy only increased by 0.04% at most, which is determined as a minimal amount. Moreover, the results show that the GP model was 3% more accurate than the multiple step persistence; however, the GP model could be further developed by adding more training data or exogenous variables to the model.
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Lorenzo, Antonio Tomas, and Antonio Tomas Lorenzo. "Short-Term Irradiance Forecasting Using an Irradiance Monitoring Network, Satellite Imagery, and Data Assimilation." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/624494.

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Solar and other renewable power sources are becoming an integral part of the electrical grid in the United States. In the Southwest US, solar and wind power plants already serve over 20% of the electrical load during the daytime on sunny days in the Spring. While solar power produces fewer emissions and has a lower carbon footprint than burning fossil fuels, solar power is only generated during the daytime and it is variable due to clouds blocking the sun. Electric utilities that are required to maintain a reliable electricity supply benefit from anticipating the schedule of power output from solar power plants. Forecasting the irradiance reaching the ground, the primary input to a solar power forecast, can help utilities understand and respond to the variability. This dissertation will explore techniques to forecast irradiance that make use of data from a network of sensors deployed throughout Tucson, AZ. The design and deployment of inexpensive sensors used in the network will be described. We will present a forecasting technique that uses data from the sensor network and outperforms a reference persistence forecast for one minute to two hours in the future. We will analyze the errors of this technique in depth and suggest ways to interpret these errors. Then, we will describe a data assimilation technique, optimal interpolation, that combines estimates of irradiance derived from satellite images with data from the sensor network to improve the satellite estimates. These improved satellite estimates form the base of future work that will explore generating forecasts while continuously assimilating new data.
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Books on the topic "SOLAR POWER FORECASTING"

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United States. Bureau of Labor Statistics, ed. Careers in solar power. Washington, D.C.]: U.S. Bureau of Labor Statistics, 2011.

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Ray, George, Bush Brian, National Renewable Energy Laboratory (U.S.), and Colorado Renewable Energy Conference (2009), eds. Estimating solar PV output using modern space/time geostatistics. Golden, Colo.]: National Renewable Energy Laboratory, 2009.

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National Renewable Energy Laboratory (U.S.) and IEEE Photovoltaic Specialists Conference (37th : 2011 : Seattle, Wash.), eds. An economic analysis of photovoltaics versus traditional energy sources: Where are we now and where might we be in the near future? : preprint. Golden, Colo.]: National Renewable Energy Laboratory, 2011.

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Solar Energy Technologies Program (U.S.), National Renewable Energy Laboratory (U.S.), and IEEE Photovoltaic Specialists Conference (37th : 2011 : Seattle, Wash.), eds. An economic analysis of photovoltaics versus traditional energy sources: Where are we now and where might we be in the near future? [Golden, Colo.]: National Renewable Energy Laboratory, U.S. Dept. of Energy, Office of Energy Efficienty and Renewable Energy, 2011.

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Paulescu, Marius. Weather Modeling and Forecasting of PV Systems Operation. London: Springer London, 2013.

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Lipták, Béla G. Post-oil energy technology: The world's first solar-hydrogen demonstration power plant. Boca Raton: CRC Press, 2009.

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European Commission. Directorate-General for Energy and European Photovoltaic Industry Association, eds. Photovoltaics in 2010. Luxembourg: Office for Official Publications of the European Communities, 1996.

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Nelson, Brent P. Potential of Photovoltaics. Washington, D.C: National Renewable Energy Laboratory, 2008.

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Liptak, Bela G. Post-oil energy technology: After the age of fossil fuels. Boca Raton, Fl: Taylor & Francis, 2008.

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Wiley, John. Photovoltaic Materials: An Analysis of Emerging Technology and Markets (Technical Insights, R-259). John Wiley & Sons Inc, 1999.

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Book chapters on the topic "SOLAR POWER FORECASTING"

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Khurana, Agrim, Ankit Dabas, Vaibhav Dhand, Rahul Kumar, Bhavnesh Kumar, and Arjun Tyagi. "Solar Power Forecasting." In Artificial Intelligence for Solar Photovoltaic Systems, 23–41. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003222286-2.

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Zack, John W. "Wind and Solar Forecasting." In Power Electronics and Power Systems, 135–65. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55581-2_4.

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Wang, Zheng, Irena Koprinska, and Mashud Rana. "Solar Power Forecasting Using Pattern Sequences." In Artificial Neural Networks and Machine Learning – ICANN 2017, 486–94. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_55.

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Syu, Jia-Hao, Chi-Fang Chao, and Mu-En Wu. "Forecasting System for Solar-Power Generation." In Recent Challenges in Intelligent Information and Database Systems, 65–72. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1685-3_6.

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Piazza, Antonino, and Giuseppe Faso. "Concentrated Solar Power: Ontologies for Solar Radiation Modeling and Forecasting." In Advances in Intelligent Systems and Computing, 325–37. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03992-3_23.

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Shareef Syed, Mahaboob, Ch V. Suresh, B. Sreenivasa Raju, M. Ravindra Babu, and Y. S. Kishore Babu. "Forecasting of Wind Power Using Hybrid Machine Learning Approach." In Wind and Solar Energy Applications, 27–34. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003321897-3.

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Lin, Yang, Irena Koprinska, Mashud Rana, and Alicia Troncoso. "Pattern Sequence Neural Network for Solar Power Forecasting." In Communications in Computer and Information Science, 727–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36802-9_77.

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Dahl, Astrid, and Edwin Bonilla. "Scalable Gaussian Process Models for Solar Power Forecasting." In Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy, 94–106. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71643-5_9.

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Sampathraja, N., L. Ashok Kumar, R. Saravana Kumar, and I. Made Wartana. "Solar Power Forecasting Using Adaptive Curve-Fitting Algorithm." In Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications, 227–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-24051-6_22.

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Mohammed, Azhar Ahmed, Waheeb Yaqub, and Zeyar Aung. "Probabilistic Forecasting of Solar Power: An Ensemble Learning Approach." In Intelligent Decision Technologies, 449–58. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19857-6_38.

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Conference papers on the topic "SOLAR POWER FORECASTING"

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Bacher, Peder, Henrik Madsen, and Bengt Perers. "Short-Term Solar Collector Power Forecasting." In ISES Solar World Congress 2011. Freiburg, Germany: International Solar Energy Society, 2011. http://dx.doi.org/10.18086/swc.2011.28.03.

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Jascourt, Stephen D., Daniel Kirk-Davidhoff, and Christopher Cassidy. "Forecasting Solar Power and Irradiance – Lessons from Real-World Experiences." In American Solar Energy Society National Solar Conference 2016. Freiburg, Germany: International Solar Energy Society, 2016. http://dx.doi.org/10.18086/solar.2016.01.15.

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Lee, Jeong-In, Young-Mee Shin, Il-Woo Lee Energy, and Sang-Ha Kim. "Solar Power Generation Forecasting Service." In 2019 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2019. http://dx.doi.org/10.1109/ictc46691.2019.8939757.

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Panamtash, Hossein, and Qun Zhou. "Coherent Probabilistic Solar Power Forecasting." In 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2018. http://dx.doi.org/10.1109/pmaps.2018.8440483.

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Wanady, Irene, Aparna Viswanath, and Kaushik Mahata. "Solar Forecasting for Power System Operator." In 2018 IEEE Electrical Power and Energy Conference (EPEC). IEEE, 2018. http://dx.doi.org/10.1109/epec.2018.8598379.

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Chen, Zezhou, and Irena Koprinska. "Ensemble Methods for Solar Power Forecasting." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206713.

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Abuella, Mohamed, and Badrul Chowdhury. "Hourly probabilistic forecasting of solar power." In 2017 North American Power Symposium (NAPS). IEEE, 2017. http://dx.doi.org/10.1109/naps.2017.8107270.

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Shedbalkar, Kaustubha H., and D. S. More. "Bayesian Regression for Solar Power Forecasting." In 2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2022. http://dx.doi.org/10.1109/aisp53593.2022.9760559.

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Shedbalkar, Kaustubha H., and D. S. More. "Bayesian Regression for Solar Power Forecasting." In 2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2022. http://dx.doi.org/10.1109/aisp53593.2022.9760559.

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Amreen, T. Sana, Radharani Panigrahi, and N. R. Patne. "Solar Power Forecasting Using Hybrid Model." In 2023 5th International Conference on Energy, Power and Environment: Towards Flexible Green Energy Technologies (ICEPE). IEEE, 2023. http://dx.doi.org/10.1109/icepe57949.2023.10201483.

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Reports on the topic "SOLAR POWER FORECASTING"

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Haupt, Sue Ellen. A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting. Office of Scientific and Technical Information (OSTI), April 2016. http://dx.doi.org/10.2172/1408392.

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Marquis, Melinda, Stan Benjamin, Eric James, kathy Lantz, and Christine Molling. A Public-Private-Academic Partnership to Advance Solar Power Forecasting. Office of Scientific and Technical Information (OSTI), April 2015. http://dx.doi.org/10.2172/1422824.

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