Готові списки джерел за темами / SOLAR ENERGY FORECASTING

Добірка наукової літератури з теми "SOLAR ENERGY FORECASTING"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "SOLAR ENERGY FORECASTING"

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Sangrody, Hossein, Morteza Sarailoo, Ning Zhou, Nhu Tran, Mahdi Motalleb, and Elham Foruzan. "Weather forecasting error in solar energy forecasting." IET Renewable Power Generation 11, no. 10 (July 11, 2017): 1274–80. http://dx.doi.org/10.1049/iet-rpg.2016.1043.

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2

Chaudhary, Pankaj, Rohith Gattu, Soundarajan Ezekiel, and James Allen Rodger. "Forecasting Solar Radiation." Journal of Cases on Information Technology 23, no. 4 (October 2021): 1–21. http://dx.doi.org/10.4018/jcit.296263.

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Анотація:
Renewable energy, such as solar and wind, has been increasing in popularity for over a decade. This is especially true in rural, underdeveloped areas, and urban households that desire energy independence. Renewable energy sources, such as solar, provide enhanced environmental benefits while simultaneously minimizing the carbon footprint. One popular technology that can capture solar energy is solar panels. The demand for solar panels has been on the rise due to increases in energy conversion efficiency, long-term financial advantages, and contributions to decreasing fossil fuel usage. However, solar panels need a steady supply of sunlight. This can be challenging in many situations, geographies, and environments. This paper uses multiple machine learning (ML) algorithms that can predict future values of solar radiation based on previously observed values and other environmental features measured without the use of complex equipment with methods that are computationally efficient so that forecasting can be done on consumer premises.
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3

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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Анотація:
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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4

A. G. M. Amarasinghe, P., N. S. Abeygunawardana, T. N. Jayasekara, E. A. J. P. Edirisinghe, and S. K. Abeygunawardane. "Ensemble models for solar power forecasting—a weather classification approach." AIMS Energy 8, no. 2 (2020): 252–71. http://dx.doi.org/10.3934/energy.2020.2.252.

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5

Paulescu, Marius, Nicoleta Stefu, Ciprian Dughir, Robert Blaga, Andreea Sabadus, Eugenia Paulescu, and Sorin Bojin. "Online Forecasting of the Solar Energy Production." Annals of West University of Timisoara - Physics 60, no. 1 (August 1, 2018): 104–10. http://dx.doi.org/10.2478/awutp-2018-0011.

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Анотація:
AbstractForecasting the solar energy production is a key issue in the large-scale integration of the photovoltaic plants into the existing electricity grid. This paper reports on the research progress in forecasting the solar energy production at the West University of Timisoara, Romania. Firstly, the experimental facilities commissioned on the Solar Platform for testing the forecasting models are briefly described. Secondly, a new tool for the online forecasting of the solar energy production is introduced. Preliminary tests show that the implemented procedure is a successful trade-off between simplicity and accuracy.
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6

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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Анотація:
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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Madhiarasan, Manoharan, Mohamed Louzazni, and Brahim Belmahdi. "Statistical Analysis of Novel Ensemble Recursive Radial Basis Function Neural Network Performance on Global Solar Irradiance Forecasting." Journal of Electrical and Computer Engineering 2023 (March 28, 2023): 1–10. http://dx.doi.org/10.1155/2023/2554355.

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Анотація:
Reliable operation of energy management systems, grid stability, and managing energy demand responses are becoming challenging because of the flickering nature of solar irradiance. Accurate forecasting of global solar irradiance, i.e., global horizontal irradiance (GHI), plays a significant role in energy policy-making and the energy market. This paper proposes a novel global solar irradiance forecasting model based on the ensemble recursive radial basis function neural networks (ERRBFNNs). The various atmospheric inputs based on the built ensemble recursive radial basis function neural networks make the network more stable and robust to climatic uncertainty. This paper statistically investigates the performance of novel feed-forward neural networks based on forecasting models with various hidden nodes for global solar irradiance forecasting applications. We validated the proposed ERRBFNN global solar irradiance forecasting model using real-time data sets. The simulation results confirm that the proposed ensemble recursive radial basis function neural network based on global solar irradiance forecasting improves the accuracy, generalization, and network stability. Furthermore, the proposed ERRBFNN lowers the forecasting error to the least compared to other state-of-the-art forecasting models.
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8

Al-Ali, Elham M., Yassine Hajji, Yahia Said, Manel Hleili, Amal M. Alanzi, Ali H. Laatar, and Mohamed Atri. "Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model." Mathematics 11, no. 3 (January 28, 2023): 676. http://dx.doi.org/10.3390/math11030676.

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Анотація:
Green energy is very important for developing new cities with high energy consumption, in addition to helping environment preservation. Integrating solar energy into a grid is very challenging and requires precise forecasting of energy production. Recent advances in Artificial Intelligence have been very promising. Particularly, Deep Learning technologies have achieved great results in short-term time-series forecasting. Thus, it is very suitable to use these techniques for solar energy production forecasting. In this work, a combination of a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and a Transformer was used for solar energy production forecasting. Besides, a clustering technique was applied for the correlation analysis of the input data. Relevant features in the historical data were selected using a self-organizing map. The hybrid CNN-LSTM-Transformer model was used for forecasting. The Fingrid open dataset was used for training and evaluating the proposed model. The experimental results demonstrated the efficiency of the proposed model in solar energy production forecasting. Compared to existing models and other combinations, such as LSTM-CNN, the proposed CNN-LSTM-Transformer model achieved the highest accuracy. The achieved results show that the proposed model can be used as a trusted forecasting technique that facilitates the integration of solar energy into grids.
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9

Chodakowska, Ewa, Joanicjusz Nazarko, Łukasz Nazarko, Hesham S. Rabayah, Raed M. Abendeh, and Rami Alawneh. "ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations." Energies 16, no. 13 (June 28, 2023): 5029. http://dx.doi.org/10.3390/en16135029.

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Анотація:
The increasing demand for clean energy and the global shift towards renewable sources necessitate reliable solar radiation forecasting for the effective integration of solar energy into the energy system. Reliable solar radiation forecasting has become crucial for the design, planning, and operational management of energy systems, especially in the context of ambitious greenhouse gas emission goals. This paper presents a study on the application of auto-regressive integrated moving average (ARIMA) models for the seasonal forecasting of solar radiation in different climatic conditions. The performance and prediction capacity of ARIMA models are evaluated using data from Jordan and Poland. The essence of ARIMA modeling and analysis of the use of ARIMA models both as a reference model for evaluating other approaches and as a basic forecasting model for forecasting renewable energy generation are presented. The current state of renewable energy source utilization in selected countries and the adopted transition strategies to a more sustainable energy system are investigated. ARIMA models of two time series (for monthly and hourly data) are built for two locations and a forecast is developed. The research findings demonstrate that ARIMA models are suitable for solar radiation forecasting and can contribute to the stable long-term integration of solar energy into countries’ systems. However, it is crucial to develop location-specific models due to the variability of solar radiation characteristics. This study provides insights into the use of ARIMA models for solar radiation forecasting and highlights their potential for supporting the planning and operation of energy systems.
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Vennila, C., Anita Titus, T. Sri Sudha, U. Sreenivasulu, N. Pandu Ranga Reddy, K. Jamal, Dayadi Lakshmaiah, P. Jagadeesh, and Assefa Belay. "Forecasting Solar Energy Production Using Machine Learning." International Journal of Photoenergy 2022 (April 30, 2022): 1–7. http://dx.doi.org/10.1155/2022/7797488.

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Анотація:
When it comes to large-scale renewable energy plants, the future of solar power forecasting is vital to their success. For reliable predictions of solar electricity generation, one must take into consideration changes in weather patterns over time. In this paper, a hybrid model that integrates machine learning and statistical approaches is suggested for predicting future solar energy generation. In order to improve the accuracy of the suggested model, an ensemble of machine learning models was used in this study. The results of the simulation show that the proposed method has reduced placement cost, when compared with existing methods. When comparing the performance of an ensemble model that integrates all of the combination strategies to standard individual models, the suggested ensemble model outperformed the conventional individual models. According to the findings, a hybrid model that made use of both machine learning and statistics outperformed a model that made sole use of machine learning in its performance.
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Дисертації з теми "SOLAR ENERGY FORECASTING"

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Montornès, Torrecillas Alex. "A study of the shortwave schemes in the Weather Research and Forecasting model." Doctoral thesis, Universitat de Barcelona, 2017. http://hdl.handle.net/10803/401501.

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Анотація:
The radiative transfer cannot be explicitly resolved in the atmospheric models for two reasons: i) a full treatment of the radiative transfer equation (RTE) requires a high amount of computational resources and ii) the radiative transfer fields such as the optical thickness are not a direct solution of the Euler equations and hence, they must be parameterized as a function of the meteorological fields. Consequently, the physical processes related with radiation are simplified and approximated in physical schemes. In the particular case of the solar radiation, the use of these parameterizations were reduced for many years to represent the day/night cycle inside the model. Therefore, the accuracy of the solar schemes was left in the background and the computational resources were prioritized. With the growth of the solar energy industry during the last decade, a paradigm shift has occurred. Now, the solar irradiance (i.e. global horizontal GHI, direct horizontal DHI and diffuse DIF) becomes an important product for resource assessment as well as for forecasting applications. The main objective of this thesis is the identification and quantification of the sources of error that have a direct or an indirect contribution to the accuracy of the solar schemes, particularly, in those available in the Weather Research and Forecasting (WRF-ARW) model, widely used in the sector. First, the thesis presents a review of the set of physical approximations considered in six solar parameterizations available in the WRF-ARW model: Dudhia, Goddard, New Goddard, Rapid Radiative Transfer Model for General Circulation Models (RRTMG), Climate Atmospheric Model (CAM) and Fu-Liou-Gu (FLG). The sources of error are limitations in the representation of the radiative transfer as a conse- quence of the set of approximations assumed by one scheme. In this thesis three sources of error are analyzed: i) errors due to the vertical discretization of the atmosphere in a set of layers that are assumed to be homogeneous (truncation error), ii) errors due to the misrepresentation of the layer between the top of the model (TOM) and the top of the atmosphere (TOA), called TOM error and iii) errors due to the physical simplifications and parameterizations in the RTE, named physical error. In order to avoid the uncertainty introduced by the other components of the model, the source code of each one of the six solar schemes has been separated of the model and adapted for working with 1-dimensional vertical profiles. The studies of the truncation and TOM errors are performed by using ideal vertical profiles under four scenarios: a dry atmosphere, a wet cloudless sky, low water cloud and a high ice cloud. The results for the ETOM show that for the typical range of TOM values in mesoscale appli- cations (i.e. 10 hPa), the error with respect to a full atmospheric column is less than 0.5% and hence, the TOM error can be neglected. The analysis of the Etrun reveals that the sensitivity of the solar schemes on the vertical config- uration (i.e. number of vertical levels and their distribution) is directly related with the method used for the vertical integration of the multiscattering processes. For the typical mesoscale config- urations, the Etrun under clear-sky conditions is determined around 1.1%, 0.9% and 4.9% for the GHI, DHI and DIF, respectively. In both cloudy scenarios, the Etrun increases significantly, being more important for the high clouds. The Ephys is analyzed under clear-sky conditions using real soundings from the Integrated Global Radiosonde Archive data-set and comparing the irradiance outcomes with the Baseline Solar Radiation Network measurements. With the exception of Dudhia, the behavior for all the parameterizations is the same. A large overestimation of the DHI with a large underestimation of the DIF that leads to a near-zero bias for the GHI. Polar sites show the lowest errors with a mean MAE of 2.1%, 5.2% and 3.7% for GHI, DHI and DIF, respectively. Midlatitude sites show the worst results with a mean MAE of 3.4% in GHI, 11.6% in DHI and 7.8% in the DIF.
L’objectiu principal d’aquesta tesi ´es la identificaci´o i quantificaci´o de les fonts d’error que tenen una contribuci´o directa o indirecta en la precisi´o dels esquemes solars, particularment en aquells disponibles en el model Weather Research and Forecasting (WRF-ARW), `ampliament emprat en el sector de l’energia solar. Les fonts d’error s´on limitacions en la representaci´o del transport radiatiu com a consequ¨`encia del conjunt d’aproximacions assumides per cada esquema. En aquesta tesi hi ha tres fonts d’error que s´on analitzades: i) l’error degut a la discretitzaci´o vertical de l’atmosfera en un conjunt d’estrats que s’assumeixen homogenis (error de truncament, Etrun), ii) l’error com a resultat d’una repre- sentaci´o insuficient de l’estrat entre el cim del model (TOM) i el cim de l’atmosfera (TOA), anomenat error de TOM Etom, i iii) l’error degut a les simplificacions i a les parametritzacions f´ısiques de l’RTE, definit com a error físic, Ephys. Per tal d’evitar la incertesa introdu¨ıda pels altres components del model, el codi font de cadas- cun dels sis esquemes solars ha estat separat del model i adaptat per treballar amb perfils verticals 1-dimensionals. Mitjan¸cant aquest m`etode, les habilitats dels esquemes solars poden ´esser anal- itzades sota condicions d’entrada id`entiques. D’una banda l’error de TOM i el de truncament s’analitzen a partir de perfils ideals. De l’altra, l’error f´ısic s’evalua prenent dades de radiosondatge com a perfil vertical i comparant les sortides dels esquemes radiatius amb mesures en superf´ıcie. Els resultats d’aquesta tesi mostren que l’Etom esdev´e negligible per la majoria d’aplicacions de mesoscala. Per configuracions t´ıpiques del model, l’Etrun en condicions de cel ser`e es troba al voltant de l’1.1%, el 0.9% i el 4.9% per la GHI, DHI i DIF, respectivament. En el cas amb nu´vols augmenta de forma significativa. L’estudi de l’Ephys mostra una relaci´o significativa amb el contingut de vapor d’aigua i els aerosols.
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2

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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Анотація:
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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3

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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Анотація:
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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4

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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Анотація:
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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5

Uwamahoro, Jean. "Forecasting solar cycle 24 using neural networks." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1005253.

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Анотація:
The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near-Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. These techniques include, for example, neural networks and geomagnetic precursor methods. In this thesis, various neural network based models were developed and the model considered to be optimum was used to estimate the shape and timing of solar cycle 24. Given the recent success of the geomagnetic precusrsor methods, geomagnetic activity as measured by the aa index is considered among the main inputs to the neural network model. The neural network model developed is also provided with the time input parameters defining the year and the month of a particular solar cycle, in order to characterise the temporal behaviour of sunspot number as observed during the last 10 solar cycles. The structure of input-output patterns to the neural network is constructed in such a way that the network learns the relationship between the aa index values of a particular cycle, and the sunspot number values of the following cycle. Assuming January 2008 as the minimum preceding solar cycle 24, the shape and amplitude of solar cycle 24 is estimated in terms of monthly mean and smoothed monthly sunspot number. This new prediction model estimates an average solar cycle 24, with the maximum occurring around June 2012 [± 11 months], with a smoothed monthly maximum sunspot number of 121 ± 9.
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6

Sfetsos, Athanasios. "Time series forecasting of wind speed and solar radiation for renewable energy sources." Thesis, Imperial College London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313886.

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7

Ferrer, Martínez Claudia. "Machine Learning for Solar Energy Prediction." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-27423.

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Анотація:
This thesis consists of the study of different Machine Learning models used to predict solar power data in photovoltaic plants. The process of implement a model of Machine Learning will be reviewed step by step: to collect the data, to pre-process the data in order to make it able to use as input for the model, to divide the data into training data and testing data, to train the Machine Learning algorithm with the training data, to evaluate the algorithm with the testing data, and to make the necessary changes to achieve the best results. The thesis will start with a brief introduction to solar energy in one part, and an introduction to Machine Learning in another part. The theory of different models and algorithms of supervised learning will be reviewed, such as Decision Trees, Naïve Bayer Classification, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Linear Regression, Logistic Regression, Artificial Neural Network (ANN). Then, the methods Linear Regression, SVM Regression and Artificial Neural Network will be implemented using MATLAB in order to predict solar energy from historical data of photovoltaic plants. The data used to train and test the models is extracted from the National Renewable Energy Laboratory (NREL), that provides a dataset called “Solar Power Data for Integration Studies” intended for use by Project developers and university researchers. The dataset consist of 1 year of hourly power data for approximately 6000 simulated PV plants throughout the United States. Finally, once the different models have been implemented, the results show that the technique which provide the best results is Linear Regression.
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8

Mohammed, Kadhim Nada. "Creating 3D city models from satellite imagery for integrated assessment and forecasting of solar energy." Thesis, Cardiff University, 2018. http://orca.cf.ac.uk/109232/.

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Анотація:
Buildings are the most prominent component in the urban environment. The geometric identification of urban buildings plays an important role in a range of urban applications, including 3D representations of buildings, energy consumption analysis, sustainable development, urban planning, risk assessment, and change detection. In particular, 3D building models can provide a comprehensive assessment of surfaces exposed to solar radiation. However, the identification of the available surfaces on urban structures and the actual locations which receive a sufficient amount of sunlight to increase installed power capacity (e.g. Photovoltaic systems) are crucial considerations for solar energy supply efficiency. Although considerable research has been devoted to detecting the rooftops of buildings, less attention has been paid to creating and completing 3D models of urban buildings. Therefore, there is a need to increase our understanding of the solar energy potential of the surfaces of building envelopes so we can formulate future adaptive energy policies for improving the sustainability of cities. The goal of this thesis was to develop a new approach to automatically model existing buildings for the exploitation of solar energy potential within an urban environment. By investigating building footprints and heights based on shadow information derived from satellite images, 3D city models were generated. Footprints were detected using a two level segmentation process: (1) the iterative graph cuts approach for determining building regions and (2) the active contour method and the adjusted-geometry parameters method for modifying the edges and shapes of the extracted building footprints. Building heights were estimated based on the simulation of artificial shadow regions using identified building footprints and solar information in the image metadata at pre-defined height increments. The difference between the actual and simulated shadow regions at every height increment was computed using the Jaccard similarity coefficient. The 3D models at the first level of detail were then obtained by extruding the building footprints based on their heights by creating image voxels and using the marching cube approach. In conclusion, 3D models of buildings can be generated solely from 2D data of the buildings’attributes in any selected urban area. The approach outperforms the past attempts, and mean error is reduced by at least 21%. Qualitative evaluations of the study illustrate that it is possible to achieve 3D building models based on satellite images with a mean error of less than 5 m. This comprehensive study allows for 3D city models to be generated in the absence of elevation attributes and additional data. Experiments revealed that this novel, automated method can be useful in a number of spatial analyses and urban sustainability applications.
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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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De, Jong Pieter. "Forecasting, integration, and storage of renewable energy generation in the Northeast of Brazil." Escola Politécnica, 2017. http://repositorio.ufba.br/ri/handle/ri/24167.

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As a result of global climate change, during the coming decades less rainfall and higher temperatures are projected for the Brazilian Northeast (NE). Consequently these regional climatic changes could severely impact hydroelectric generation in the NE as well as influence solar and wind power potential. The ongoing drought in the Brazilian NE region has caused hydroelectric generation to decline substantially during the last 5 years and in 2016 hydroelectricity only supplied 25% of the NE’s total demand. In contrast, wind power supplied 30% of demand and is expected to generate 55-60% of the NE’s electricity supply by 2020. Therefore, this paper is focused on both short term forecasting and long-term projections of renewable energy generation and resource availability. It also explores the economic, environmental and technical feasibility of renewable energy integration in the NE region of Brazil. First, the long-term impacts of climate change on the NE region’s hydroelectric and wind energy production are analysed. Particular attention is paid to the long-term projections of annual rainfall and streamflow in the São Francisco basin which could decline by approximately 47% and 80%, respectively, by 2050. On the other hand, wind energy potential is projected to increase substantially during the same period. This thesis also estimates the economic, social, and environmental viability of renewable and non-renewable generation technologies in Brazil. The Levelised Cost of Electricity (LCOE) including externalities is calculated for several different case study power plants, the majority of which are located in the Brazilian NE. It was found that wind power becomes the cheapest generation technology in the NE region, once all externality and transmission line costs are taken into consideration. The LCOE for the entire Northeast’s generation matrix is calculated for various configurations, including scenarios in which hydroelectric generation is restricted due to drought conditions. It was concluded that a generation mix in which wind power replaces all fossil fuel generation by 2020, could feasibly reduce the overall LCOE in the region by approximately 46% and substantially decrease CO2eq emissions. Two different methods are used to examine the limits of integrating high penetrations of variable renewable generation technologies into a power system with a large proportion of hydroelectric capacity. In the first method existing wind generation data from 16 wind farms is extrapolated in time and space, while the second method uses a numerical weather prediction model to simulate future wind energy generation in the NE region. Considering the minimum generation requirements of the São Francisco’s hydroelectric dams, the maximum wind energy penetration in the NE region is estimated to be approximately 50% before significant amounts of energy would need to be curtailed or exported to other Brazilian regions. Finally, this thesis reviews additional literature on energy storage and the impact of large scale variable renewable energy integration on grid stability and power quality. It was found that there are several existing technologies such as power factor and voltage regulation devices that can resolve these issues.
Como consequência da mudança climática global, nas próximas décadas menos precipitação e temperaturas mais altas são projetados para Nordeste (NE) do Brasil. Consequentemente, essas mudanças climáticas regionais podem afetar severamente a geração hidrelétrica no NE, bem como influenciar o potencial de energia solar e eólica. A seca atual nessa região do Brasil fez com que a geração hidrelétrica caísse substancialmente durante os últimos 5 anos e em 2016, as usinas hidrelétricas apenas forneceram 25% da demanda total do NE. Em contraste, a energia eólica forneceu 30% da demanda e deverá gerar 55-60% do fornecimento de energia elétrica do NE até 2020. Portanto, este trabalho está focado tanto na previsão a curto quanto projeções a longo prazo da geração de energia renovável e disponibilidade de recursos. Ele também explora a viabilidade econômica, ambiental e técnica da integração de energias renováveis na região NE. Primeiramente, os impactos de longo prazo das mudanças climáticas na produção hidrelétrica e eólica da região NE são analisados. Especial atenção é dada às projeções de longo prazo de precipitação anual e fluxo na bacia do São Francisco, que podem diminuir em aproximadamente 47% e 80%, respectivamente, até 2050. Por outro lado, prevê-se que o potencial da energia eólica aumente substancialmente durante o mesmo período. Esta tese também estima a viabilidade econômica, social e ambiental das tecnologias de geração renováveis e não-renováveis no Brasil. O custo nivelado de energia elétrica (LCOE), incluindo externalidades, é calculado para diversas usinas de estudo de caso, a maioria localizada no NE. Verificou-se que, a energia eólica se torna a tecnologia de geração mais barata na região NE, uma vez que todos os custos de externalidades e de linhas de transmissão são levados em consideração. O LCOE para a matriz de geração do Nordeste é calculado para várias configurações, incluindo cenários em que a geração hidrelétrica é restrita devido às condições de seca. Concluiu-se que, uma mistura de geração em que a energia eólica substitui toda a geração de combustíveis fósseis até 2020, poderia reduzir o LCOE na região em aproximadamente 46% e diminuir substancialmente as emissões de CO2eq. Dois métodos diferentes são usados para examinar os limites da integração de altas penetrações de tecnologias de geração renovável variáveis em um sistema de energia com uma grande proporção de capacidade hidrelétrica. No primeiro método, dados de geração eólica existentes de 16 parques eólicos são extrapolados no tempo e no espaço, enquanto o segundo método utiliza um modelo de previsão numérica de tempo para simular a futura geração de energia eólica na região NE. Considerando as exigências mínimas de geração das hidrelétricas do São Francisco, estima-se que a penetração máxima de energia eólica na região NE seja de aproximadamente 50% antes que quantidades significativas de energia precisem ser desperdiçadas ou exportadas para outras regiões brasileiras. Finalmente, esta tese examina literatura adicional sobre armazenamento de energia e o impacto da integração de energia renovável variável em larga escala na estabilidade da rede elétrica e na qualidade da energia. Verificou-se que existem várias tecnologias existentes, como dispositivos de regulação de fator de potência e tensão que podem resolver estes problemas.
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Книги з теми "SOLAR ENERGY FORECASTING"

1

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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Investing in solar stocks: An investor's guide to winning in the global renewable energy market. New York: McGraw-Hill, 2009.

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

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4

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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6

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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7

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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8

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

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9

Kleissl, Jan. Solar Energy Forecasting and Resource Assessment. Elsevier Science & Technology Books, 2013.

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10

Solar Energy Forecasting and Resource Assessment. Elsevier, 2013. http://dx.doi.org/10.1016/c2011-0-07022-9.

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Частини книг з теми "SOLAR ENERGY FORECASTING"

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Gupta, Anuj, Kapil Gupta, and Sumit Saroha. "Solar Energy Radiation Forecasting Method." In Smart Technologies for Energy and Environmental Sustainability, 105–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80702-3_7.

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2

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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3

Masoom, Akriti, Yashwant Kashyap, and Ankit Bansal. "Solar Radiation Assessment and Forecasting Using Satellite Data." In Energy, Environment, and Sustainability, 45–71. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3302-6_3.

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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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Sangle, Ashok S., and Prapti D. Deshmukh. "Forecasting Solar Energy on Time Frame: A Review." In Rising Threats in Expert Applications and Solutions, 427–37. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1122-4_45.

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Gandhi, R. R. Rubia, C. Kathirvel, R. Mohan Kumar, and M. Siva Ramkumar. "Solar energy forecasting architecture using deep learning models." In Machine Learning and the Internet of Things in Solar Power Generation, 105–21. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003302964-6.

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Manjarres, Diana, Ricardo Alonso, Sergio Gil-Lopez, and Itziar Landa-Torres. "Solar Energy Forecasting and Optimization System for Efficient Renewable Energy Integration." In Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy, 1–12. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71643-5_1.

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Badescu, Viorel. "Available Solar Energy and Weather Forecasting on Mars Surface." In Mars, 25–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03629-3_2.

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Şahinbaş, Kevser. "Comparative Study of the Forecasting Solar Energy Generation in Istanbul." In Circular Economy and the Energy Market, 185–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13146-2_15.

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Moradzadeh, Arash, Armin Hosseini Rezaei Asl, Morteza Nazari-Heris, Kazem Zare, and Behnam Mohammadi-Ivatloo. "Deep Learning-Assisted Solar Radiation Forecasting for Photovoltaic Power Generation Management in Buildings." In Renewable Energy for Buildings, 47–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08732-5_3.

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Тези доповідей конференцій з теми "SOLAR ENERGY FORECASTING"

1

"Multivariate forecasting of solar energy." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.g1.boland.

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"Probabilistic forecasting for solar energy." In 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.boland.

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3

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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Henze, Gregor P. "Parametric Study of a Simplified Ice Storage Model Operating Under Conventional and Optimal Control Strategies." In ASME Solar 2002: International Solar Energy Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/sed2002-1039.

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A simplified ice storage system model was developed in which the icemaking mode is reflected by a higher power consumption per unit cooling than in chilled-water mode. The performance of four control strategies for ice storage systems is evaluated. The four control strategies investigated are chiller-priority and constant-proportion as conventional, instantaneous controls, while storage-priority and optimal control represent sophisticated controls employing load forecasting. Six parameters were investigated with respect to their influence on the ice storage system performance: Storage losses, utility rate structures, rate periods, penalty for icemaking, storage capacity, and the impact of load forecasting. Optimal control was determined to provide maximal operating cost savings. The storage-priority control yields operating costs only slightly higher than those of optimal control. Chiller-priority control realized savings that were typically on the order of 50% of what is theoretically possible (optimal control). Constant-proportion control proved to be a simple control strategy yielding higher savings than chiller-priority, yet lower than storage-priority control.
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Kumar, Karan, and Nipun Batra. "Solar Energy Forecasting Using Machine Learning." In CoDS COMAD 2020: 7th ACM IKDD CoDS and 25th COMAD. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3371158.3371212.

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6

Marquez, Ricardo, and Carlos F. M. Coimbra. "A Novel Metric for Evaluation of Solar Forecasting Models." In ASME 2011 5th International Conference on Energy Sustainability. ASMEDC, 2011. http://dx.doi.org/10.1115/es2011-54519.

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This work presents an alternative, time-window invariant metric for evaluating the quality of solar forecasting models. Conventional approaches use statistical quantities such as the root-mean-square-error and/or the correlation coefficients to evaluate model quality. The straightforward use of statistical quantities to assign forecasting quality can be misleading because these metrics do not convey a measure of the variability of the time-series included in the solar irradiance data. In contrast, the quality metric proposed here, which is defined as the ratio of solar uncertainty to solar variability, compares forecasting error with solar variability directly. By making the forecasting error to variability comparisons for different time windows, we show that this ratio is essentially a statistical invariant for each forecasting model employed, i. e., the ratio is preserved for widely different time horizons.
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7

Manur, Anusha, Maitreyee Marathe, Ashray Manur, Abhishek Ramachandra, Shamsundar Subbarao, and Giri Venkataramanan. "Smart Solar Home System with Solar Forecasting." In 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE). IEEE, 2020. http://dx.doi.org/10.1109/pesgre45664.2020.9070340.

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8

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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9

Khoie, Rahim, and Antonio Calderon. "Forecasting Carbon Emissions in States of Hawaii, California, Colorado, and Florida; The Effects of States’ Renewable Portfolio Standards." In American Solar Energy Society National Solar Conference 2018. Freiburg, Germany: International Solar Energy Society, 2018. http://dx.doi.org/10.18086/solar.2018.01.04.

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Siddhant, Kumar, Harshit Garg, Ajay Saha, Nitin Singh, Niraj Kumar Choudhary, and Deepak Kumar Singh. "Solar Energy Forecasting using Artificial Neural Network." In 2022 IEEE Students Conference on Engineering and Systems (SCES). IEEE, 2022. http://dx.doi.org/10.1109/sces55490.2022.9887754.

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Звіти організацій з теми "SOLAR ENERGY FORECASTING"

1

Stoffel, Thomas. U.S. Department of Energy Workshop Report: Solar Resources and Forecasting. Office of Scientific and Technical Information (OSTI), June 2012. http://dx.doi.org/10.2172/1047954.

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Sengupta, Manajit, and Craig Turchi. Australian Solar Energy Forecasting System (ASEFS): Cooperative Research and Development Final Report, CRADA Number CRD-14-541. Office of Scientific and Technical Information (OSTI), August 2020. http://dx.doi.org/10.2172/1659797.

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3

Bryce, Richard, Grant Buster, Kate Doubleday, Cong Feng, Ross Ring-Jarvi, Michael Rossol, Flora Zhang, and Bri-Mathias Hodge. Solar PV, Wind Generation, and Load Forecasting Dataset for ERCOT 2018: Performance-Based Energy Resource Feedback, Optimization, and Risk Management (P.E.R.F.O.R.M.). Office of Scientific and Technical Information (OSTI), May 2023. http://dx.doi.org/10.2172/1972698.

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BARKHATOV, NIKOLAY, and SERGEY REVUNOV. A software-computational neural network tool for predicting the electromagnetic state of the polar magnetosphere, taking into account the process that simulates its slow loading by the kinetic energy of the solar wind. SIB-Expertise, December 2021. http://dx.doi.org/10.12731/er0519.07122021.

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The auroral activity indices AU, AL, AE, introduced into geophysics at the beginning of the space era, although they have certain drawbacks, are still widely used to monitor geomagnetic activity at high latitudes. The AU index reflects the intensity of the eastern electric jet, while the AL index is determined by the intensity of the western electric jet. There are many regression relationships linking the indices of magnetic activity with a wide range of phenomena observed in the Earth's magnetosphere and atmosphere. These relationships determine the importance of monitoring and predicting geomagnetic activity for research in various areas of solar-terrestrial physics. The most dramatic phenomena in the magnetosphere and high-latitude ionosphere occur during periods of magnetospheric substorms, a sensitive indicator of which is the time variation and value of the AL index. Currently, AL index forecasting is carried out by various methods using both dynamic systems and artificial intelligence. Forecasting is based on the close relationship between the state of the magnetosphere and the parameters of the solar wind and the interplanetary magnetic field (IMF). This application proposes an algorithm for describing the process of substorm formation using an instrument in the form of an Elman-type ANN by reconstructing the AL index using the dynamics of the new integral parameter we introduced. The use of an integral parameter at the input of the ANN makes it possible to simulate the structure and intellectual properties of the biological nervous system, since in this way an additional realization of the memory of the prehistory of the modeled process is provided.
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5

Werth, D., and R. Nichols. Advanced Cloud Forecasting for Solar Energy’s Impact on Grid Modernization. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1395968.

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