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Добірка наукової літератури з теми "Photovoltaic production forecasting"

Автор: Grafiati

Опубліковано: 28 лютого 2024

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Статті в журналах з теми "Photovoltaic production forecasting"

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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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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Picault, D., B. Raison, S. Bacha, J. de la Casa, and J. Aguilera. "Forecasting photovoltaic array power production subject to mismatch losses." Solar Energy 84, no. 7 (July 2010): 1301–9. http://dx.doi.org/10.1016/j.solener.2010.04.009.

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Agoua, Xwegnon Ghislain, Robin Girard, and George Kariniotakis. "Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production." IEEE Transactions on Sustainable Energy 9, no. 2 (April 2018): 538–46. http://dx.doi.org/10.1109/tste.2017.2747765.

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Milicevic, Marina, and Budimirka Marinovic. "Machine learning methods in forecasting solar photovoltaic energy production." Thermal Science, no. 00 (2023): 150. http://dx.doi.org/10.2298/tsci230402150m.

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Energy has an effective role in economic growth and development of societies. This paper is studying the impact of climate factors on performance of solar power plant using machine learning techniques for underlying relationship among factors that impact solar energy production and for forecasting monthly energy production. In this context this work provides two machine learning methods: Artificial Neural Network (ANN) for forecasting energy production and Decision Tree (DC) useful in understanding the relationships in energy production data. Both structures have horizontal irradiation, sunlight duration, average monthly air temperature, average maximal air temperature, average minimal air temperature and average monthly wind speed as inputs parameters and the energy production as output. Results have shown that used machine learning models perform effectively, ANN predicted the energy production of the PV power plant with a correlation coefficient (R) higher than 0.97. The results can help stakeholders in determining energy policy planning in order to overcome uncertainties associated with renewable energy resources.

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Castillo-Rojas, Wilson, Juan Bekios-Calfa, and César Hernández. "Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks." International Journal of Photoenergy 2023 (April 18, 2023): 1–19. http://dx.doi.org/10.1155/2023/2592405.

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In recent years, photovoltaic energy has become one of the most implemented electricity generation options to help reduce environmental pollution suffered by the planet. Accuracy in this photovoltaic energy forecasting is essential to increase the amount of renewable energy that can be introduced to existing electrical grid systems. The objective of this work is based on developing various computational models capable of making short-term forecasting about the generation of photovoltaic energy that is generated in a solar plant. For the implementation of these models, a hybrid architecture based on recurrent neural networks (RNN) with long short-term memory (LSTM) or gated recurrent units (GRU) structure, combined with shallow artificial neural networks (ANN) with multilayer perceptron (MLP) structure, is established. RNN models have a particular configuration that makes them efficient for processing ordered data in time series. The results of this work have been obtained through controlled experiments with different configurations of its hyperparameters for hybrid RNN-ANN models. From these, the three models with the best performance are selected, and after a comparative analysis between them, the forecasting of photovoltaic energy production for the next few hours can be determined with a determination coefficient of 0.97 and root mean square error (RMSE) of 0.17. It is concluded that the proposed and implemented models are functional and capable of predicting with a high level of accuracy the photovoltaic energy production of the solar plant, based on historical data on photovoltaic energy production.

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Jakoplić, A., S. Vlahinić, B. Dobraš, and D. Franković. "Sky Image Analysis and Solar Power Forecasting: A Convolutional Neural Network Approach." Renewable Energy and Power Quality Journal 21, no. 1 (July 2023): 456–61. http://dx.doi.org/10.24084/repqj21.355.

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Recently, the share of renewable sources in the energy mix of production units has been steadily increasing. The unpredictability of renewable sources leads to difficulties in planning, managing and controlling the electric energy system (EES). One of the ways to reduce the negative impact of unpredictable renewable sources is to predict the availability of these energy sources. Short-term forecasting of photovoltaic power plant production is one of the tools that enable greater integration of renewable energy sources into the EES. One way to gather information for the short-term forecast production model is to continuously photograph the hemisphere above the photovoltaic power plant. By processing the data contained within the images, parameters related to the current output power of the observed power plant are obtained. This paper presents a model that utilises a convolutional neural network to analyse images of the hemispherical sky above a power plant to predict the current output power of the power plant. Estimating current production is a crucial step in developing models for short-term solar forecasts. The model was specifically developed for photovoltaic power plants and is capable of achieving high accuracy in power prediction. The estimation of power production from photovoltaic power plants enables the use of next-frame prediction for short-term forecasting.

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Cordeiro-Costas, Moisés, Daniel Villanueva, Pablo Eguía-Oller, and Enrique Granada-Álvarez. "Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting." Applied Sciences 12, no. 17 (August 31, 2022): 8769. http://dx.doi.org/10.3390/app12178769.

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The increasing trend in energy demand is higher than the one from renewable generation, in the coming years. One of the greatest sources of consumption are buildings. The energy management of a building by means of the production of photovoltaic energy in situ is a common alternative to improve sustainability in this sector. An efficient trade-off of the photovoltaic source in the fields of Zero Energy Buildings (ZEB), nearly Zero Energy Buildings (nZEB) or MicroGrids (MG) requires an accurate forecast of photovoltaic production. These systems constantly generate data that are not used. Artificial Intelligence methods can take advantage of this missing information and provide accurate forecasts in real time. Thus, in this manuscript a comparative analysis is carried out to determine the most appropriate Artificial Intelligence methods to forecast photovoltaic production in buildings. On the one hand, the Machine Learning methods considered are Random Forest (RF), Extreme Gradient Boost (XGBoost), and Support Vector Regressor (SVR). On the other hand, Deep Learning techniques used are Standard Neural Network (SNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). The models are checked with data from a real building. The models are validated using normalized Mean Bias Error (nMBE), normalized Root Mean Squared Error (nRMSE), and the coefficient of variation (R2). Standard deviation is also used in conjunction with these metrics. The results show that the models forecast the test set with errors of less than 2.00% (nMBE) and 7.50% (nRMSE) in the case of considering nights, and 4.00% (nMBE) and 11.50% (nRMSE) if nights are not considered. In both situations, the R2 is greater than 0.85 in all models.

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Rangel-Heras, Eduardo, César Angeles-Camacho, Erasmo Cadenas-Calderón, and Rafael Campos-Amezcua. "Short-Term Forecasting of Energy Production for a Photovoltaic System Using a NARX-CVM Hybrid Model." Energies 15, no. 8 (April 13, 2022): 2842. http://dx.doi.org/10.3390/en15082842.

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In this paper, a methodology for short-term forecasting of power generated by a photovoltaic module is reported. The method incorporates a nonlinear autoregressive with exogenous inputs (NARX) fed by the solar radiation and temperature times series, as well as an estimation of power time series obtained by implementing an ideal single diode model. This synthetic time series was validated against an actual photovoltaic module. The NARX model has been implemented in conjunction with the corrective vector multiplier (CVM) technique, which uses solar radiation under clear sky conditions to adjust the forecasting results. In addition, collinearity and the Granger causality tests were used to choose the input variables. The forecasting horizon was 24-h-ahead. The hybrid NARX-CVM model was compared to a nonlinear autoregressive neural network and persistence model using the typic forecasting error measures such as the mean bias error, mean squared error, root mean squared error and forecast skill. The results showed that the forecasting skills of the hybrid model are about 34% against the NAR model and about 42% against the Persistence model. The model was validated by blind forecasting. The results demonstrated evidence of the quality of the conformed forecasting model and the convenience of its implementation and building.

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Sarmas, Elissaios, Sofoklis Strompolas, Vangelis Marinakis, Francesca Santori, Marco Antonio Bucarelli, and Haris Doukas. "An Incremental Learning Framework for Photovoltaic Production and Load Forecasting in Energy Microgrids." Electronics 11, no. 23 (November 29, 2022): 3962. http://dx.doi.org/10.3390/electronics11233962.

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Energy management is crucial for various activities in the energy sector, such as effective exploitation of energy resources, reliability in supply, energy conservation, and integrated energy systems. In this context, several machine learning and deep learning models have been developed during the last decades focusing on energy demand and renewable energy source (RES) production forecasting. However, most forecasting models are trained using batch learning, ingesting all data to build a model in a static fashion. The main drawback of models trained offline is that they tend to mis-calibrate after launch. In this study, we propose a novel, integrated online (or incremental) learning framework that recognizes the dynamic nature of learning environments in energy-related time-series forecasting problems. The proposed paradigm is applied to the problem of energy forecasting, resulting in the construction of models that dynamically adapt to new patterns of streaming data. The evaluation process is realized using a real use case consisting of an energy demand and a RES production forecasting problem. Experimental results indicate that online learning models outperform offline learning models by 8.6% in the case of energy demand and by 11.9% in the case of RES forecasting in terms of mean absolute error (MAE), highlighting the benefits of incremental learning.

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Bachici, Miroslav-Andrei, and Arpad Gellert. "Modeling Electricity Consumption and Production in Smart Homes using LSTM Networks." International Journal of Advanced Statistics and IT&C for Economics and Life Sciences 10, no. 1 (December 1, 2020): 80–89. http://dx.doi.org/10.2478/ijasitels-2020-0009.

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Abstract This paper presents a forecasting method of the electricity consumption and production in a household equipped with photovoltaic panels and a smart energy management system. The prediction is performed with a Long Short-Term Memory recurrent neural network. The datasets collected during five months in a household are used for the evaluations. The recurrent neural network is configured optimally to reduce the forecasting errors. The results show that the proposed method outperforms an earlier developed Multi-Layer Perceptron, as well as the Autoregressive Integrated Moving Average statistical forecasting algorithm.

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Більше джерел

Дисертації з теми "Photovoltaic production forecasting"

Swanepoel, Paul. "A forecasting model for photovoltaic module energy production." Thesis, Nelson Mandela Metropolitan University, 2011. http://hdl.handle.net/10948/1420.

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Energy is of concern for governments and economies all over the world. As conventional methods of energy production are facing the prospect of depleting fossil fuel reserves, economies are facing energy risks. With this tension, various threats arise in terms of energy supply security. A shift from intensive fossil fuel consumption to alternative energy consumption combined with the calculated use of fossil fuels needs to be implemented. Using the energy radiated from the sun and converted to electricity through photovoltaic energy conversion is one of the alternative and renewable sources to address the limited fossil fuel dilemma. South Africa receives an abundance of sunlight irradiance, but limited knowledge of the implementation and possible energy yield of photovoltaic energy production in South Africa is available. Photovoltaic energy yield knowledge is vital in applications for farms, rural areas and remote transmitting devices where the construction of electricity grids are not cost effective. In this study various meteorological and energy parameters about photovoltaics were captured in Port Elizabeth (South Africa) and analyzed, with data being recorded every few seconds. A model for mean daily photovoltaic power output was developed and the relationships between the independent variables analyzed. A model was developed that can forecast mean daily photovoltaic power output using only temperature derived variables and time. The mean daily photovoltaic power model can then easily be used to forecast daily photovoltaic energy output using the number of sunlight seconds in a given day.

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Carriere, Thomas. "Towards seamless value-oriented forecasting and data-driven market valorisation of photovoltaic production." Thesis, Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLM019.

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La décarbonation de la production d’électricité à échelle mondiale est un élément de réponse clé face aux pressions exercées par les différents enjeux environnementaux. Par ailleurs, la baisse des coûts de la filière photovoltaïque (PV) ouvre la voie à une augmentation significative de la production PV dans le monde. L’objectif principal de cette thèse est alors de maximiser le revenu d’un producteur d’énergie PV sous incertitude des prix de marché et de la production. Pour cela, un modèle de prévision probabiliste de la production PV à court (5 minutes) et moyen (24 heures) terme est proposé. Ce modèle est couplé à une méthode de participation au marché maximisant l’espérance du revenu. Dans un second temps, le couplage entre une centrale PV et une batterie est étudié, et une analyse de sensibilité des résultats est réalisée pour étudier la rentabilité et le dimensionnement de tels systèmes. Une méthode de participation alternative est proposée, pour lequel un réseau de neurones artificiel apprend à participer avec ou sans batterie au marché de l’électricité, ce qui permet de simplifier le processus de valorisation de l'énergie PV en diminuant le nombre de modèles requis
The decarbonation of electricity production on a global scale is a key element in responding to the pressures of different environmental issues. In addition, the decrease in the costs of the photovoltaic (PV) sector is paving the way for a significant increase in PV production worldwide. The main objective of this thesis is then to maximize the income of a PV energy producer under uncertainty of market prices and production. For this purpose, a probabilistic forecast model of short (5 minutes) and medium (24 hours) term PV production is proposed. This model is coupled with a market participation method that maximizes income expectation. In a second step, the coupling between a PV plant and a battery is studied, and a sensitivity analysis of the results is carried out to study the profitability and sizing of such systems. An alternative participation method is proposed, for which an artificial neural network learns to participate with or without batteries in the electricity market, thus simplifying the process of PV energy valuation by reducing the number of models required

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Thorey, Jean. "Prévision d’ensemble par agrégation séquentielle appliquée à la prévision de production d’énergie photovoltaïque." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066526/document.

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Notre principal objectif est d'améliorer la qualité des prévisions de production d'énergie photovoltaïque (PV). Ces prévisions sont imparfaites à cause des incertitudes météorologiques et de l'imprécision des modèles statistiques convertissant les prévisions météorologiques en prévisions de production d'énergie. Grâce à une ou plusieurs prévisions météorologiques, nous générons de multiples prévisions de production PV et nous construisons une combinaison linéaire de ces prévisions de production. La minimisation du Continuous Ranked Probability Score (CRPS) permet de calibrer statistiquement la combinaison de ces prévisions, et délivre une prévision probabiliste sous la forme d'une fonction de répartition empirique pondérée.Dans ce contexte, nous proposons une étude du biais du CRPS et une étude des propriétés des scores propres pouvant se décomposer en somme de scores pondérés par seuil ou en somme de scores pondérés par quantile. Des techniques d'apprentissage séquentiel sont mises en oeuvre pour réaliser cette minimisation. Ces techniques fournissent des garanties théoriques de robustesse en termes de qualité de prévision, sous des hypothèses minimes. Ces méthodes sont appliquées à la prévision d'ensoleillement et à la prévision de production PV, fondée sur des prévisions météorologiques à haute résolution et sur des ensembles de prévisions classiques
Our main objective is to improve the quality of photovoltaic power forecasts deriving from weather forecasts. Such forecasts are imperfect due to meteorological uncertainties and statistical modeling inaccuracies in the conversion of weather forecasts to power forecasts. First we gather several weather forecasts, secondly we generate multiple photovoltaic power forecasts, and finally we build linear combinations of the power forecasts. The minimization of the Continuous Ranked Probability Score (CRPS) allows to statistically calibrate the combination of these forecasts, and provides probabilistic forecasts under the form of a weighted empirical distribution function. We investigate the CRPS bias in this context and several properties of scoring rules which can be seen as a sum of quantile-weighted losses or a sum of threshold-weighted losses. The minimization procedure is achieved with online learning techniques. Such techniques come with theoretical guarantees of robustness on the predictive power of the combination of the forecasts. Essentially no assumptions are needed for the theoretical guarantees to hold. The proposed methods are applied to the forecast of solar radiation using satellite data, and the forecast of photovoltaic power based on high-resolution weather forecasts and standard ensembles of forecasts

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Ouedraogo, Sarah. "Développement de Stratégies Optimisées de Gestion de l’Energie Intermittente dans un Micro Réseau Photovoltaïque avec Stockage." Electronic Thesis or Diss., Corte, 2023. http://www.theses.fr/2023CORT0008.

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Les micro-réseaux sont considérés comme l'avenir de la production d'énergie et de la distribution dans les réseaux électriques. Beaucoup d’entre eux comportent une production photovoltaïque et un stockage, le plus souvent sous forme de batteries, pour alimenter différentes charges. Cette thèse a pour objectif principal de proposer des stratégies de gestion de l'énergie visant à optimiser les coûts de fonctionnement d'un micro-réseau photovoltaïque avec batterie, tout en respectant des contraintes spécifiques. Ce micro-réseau alimente des logements ainsi que des véhicules électriques.Pour ce faire, cinq stratégies de gestion de l'énergie basées sur des règles, avec une complexité croissante, ont été développées. Ces stratégies ont été comparées à une stratégie d'optimisation par programmation linéaire en termes de performances énergétiques et économiques. Les résultats obtenus indiquent que la stratégie la plus optimale atteint un niveau de performance proche de la stratégie par programmation linéaire, considérée comme « optimale ». Cependant, certaines limitations ont été observées pour les premières stratégies avec notamment la présence de coupures d'électricité dont nous ne pouvons pas nous satisfaire. Pour améliorer ces stratégies, l'effet saisonnier, particulièrement au niveau de la production solaire, a été pris en compte éliminant ainsi les coupures d'électricité. Selon la stratégie choisie, nous avons également observé que les batteries sont plus au moins sollicitées, il convenait donc de considérer ce vieillissement plus au moins important des batteries au niveau des performances. Des modèles de vieillissement adaptés ont ainsi été mis en œuvre. Les résultats ont montré que la rentabilité des batteries dépend du coût d'installation et qu’elles restent économiquement intéressantes pour des coûts inférieurs à environ 175 €/kWh. La stratégie de contrôle basée sur les règles la plus performante intègre la variation du coût du coût de l'électricité, la prévision de la production photovoltaïque, la variation saisonnière de la production PV et la dégradation de la batterie dans son processus de décision. Cette stratégie améliore le gain financier d'environ 68 % par rapport à la stratégie basée sur les règles la plus simple, proche d’une stratégie d’autoconsommationUne analyse de l'influence dans les simulations de différents paramètres tels que le tarif d’achat de l'électricité, la capacité de la batterie, les puissances échangées avec le réseau principal et le profil de consommation a été réalisée. Il a été constaté que le modèle de tarification de l'électricité a un effet important sur la répartition des flux d'énergie ainsi que le gain financier. L'influence de la taille de la batterie, de la limitation de la puissance échangeable avec le réseau principal et du profil de consommation dépend fortement de la stratégie utilisée mais aussi du modèle de tarification de l'électricité.Ce travail met en évidence l'importance d'intégrer les caractéristiques de l'énergie photovoltaïque dans les stratégies de gestion de l'énergie en utilisant différents outils tels que la prévision de la production photovoltaïque. Ces informations sont précieuses pour les décisions d'investissement et d'exploitation
Microgrids are considered as the future of energy production and distribution in electrical grid. Many of them incorporate photovoltaic generation and storage, mostly in the form of batteries, to power various loads. The main objective of this thesis is to propose energy management strategies designed to optimize the operating costs of a photovoltaic microgrid with battery while respecting specific constraints. This microgrid powers residential buildings and electric vehicles.To achieve this, five energy management strategies based on rules, with increasing complexity, were developed. These strategies were compared to an optimization using linear programming in terms of energy and economic performance. The results indicate that the most optimal strategy achieved a performance level close to the linear programming, which is considered "optimal." However, some limitations were observed for the initial strategies, including power cuts, which are not acceptable. To improve these strategies, the seasonal effect, particularly in photovoltaic production, was taken into account, eliminating power cuts. Depending on the chosen strategy, the batteries are more or less stressed, so it was necessary to consider the varying battery aging and its impact on performance. Suitable battery aging models were thus implemented. The results showed that the profitability of batteries depends on their installation cost and they remain economically viable for costs below approximately 175 €/kWh. The most effective rule-based control strategy considers variations in electricity costs, photovoltaic production forecasting, seasonal variation in PV production, and battery degradation in its decision-making process. This strategy improves financial gain by approximately 68 % compared to the simplest rule-based strategy, which is similar to a self-consumption strategy.An analysis of the influence of different parameters, such as electricity purchase tariffs, battery capacity, power exchanged with the main grid and consumption profiles was conducted through simulations. It was found that the electricity pricing model has a significant effect on energy distribution and financial gain. The influence of battery size, limitation of power exchange with the main grid, and consumption profile strongly depends on the strategy used, as well as the electricity pricing model.This work highlights the importance of integrating the characteristics of photovoltaic energy into energy management strategies through the use of various tools such as photovoltaic production forecasting. This information is valuable for investment and operational decision-making

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Agoua, Xwégnon. "Développement de méthodes spatio-temporelles pour la prévision à court terme de la production photovoltaïque." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEM066/document.

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L’évolution du contexte énergétique mondial et la lutte contre le changement climatique ont conduit à l’accroissement des capacités de production d’énergie renouvelable. Les énergies renouvelables sont caractérisées par une forte variabilité due à leur dépendance aux conditions météorologiques. La maîtrise de cette variabilité constitue un enjeu important pour les opérateurs du système électrique, mais aussi pour l’atteinte des objectifs européens de réduction des émissions de gaz à effet de serre, d’amélioration de l’efficacité énergétique et de l’augmentation de la part des énergies renouvelables. Dans le cas du photovoltaïque(PV), la maîtrise de la variabilité de la production passe par la mise en place d’outils qui permettent de prévoir la production future des centrales. Ces prévisions contribuent entre autres à l’augmentation du niveau de pénétration du PV,à l’intégration optimale dans le réseau électrique, à l’amélioration de la gestion des centrales PV et à la participation aux marchés de l’électricité. L’objectif de cette thèse est de contribuer à l’amélioration de la prédictibilité à court-terme (moins de 6 heures) de la production PV. Dans un premier temps, nous analysons la variabilité spatio-temporelle de la production PV et proposons une méthode de réduction de la non-stationnarité des séries de production. Nous proposons ensuite un modèle spatio-temporel de prévision déterministe qui exploite les corrélations spatio-temporelles entre les centrales réparties sur une région. Les centrales sont utilisées comme un réseau de capteurs qui permettent d’anticiper les sources de variabilité. Nous proposons aussi une méthode automatique de sélection des variables qui permet de résoudre les problèmes de dimension et de parcimonie du modèle spatio-temporel. Un modèle spatio-temporel probabiliste a aussi été développé aux fins de produire des prévisions performantes non seulement du niveau moyen de la production future mais de toute sa distribution. Enfin nous proposons, un modèle qui exploite les observations d’images satellites pour améliorer la prévision court-terme de la production et une comparaison de l’apport de différentes sources de données sur les performances de prévision
The evolution of the global energy context and the challenges of climate change have led to anincrease in the production capacity of renewable energy. Renewable energies are characterized byhigh variability due to their dependence on meteorological conditions. Controlling this variabilityis an important challenge for the operators of the electricity systems, but also for achieving the Europeanobjectives of reducing greenhouse gas emissions, improving energy efficiency and increasing the share of renewable energies in EU energy consumption. In the case of photovoltaics (PV), the control of the variability of the production requires to predict with minimum errors the future production of the power stations. These forecasts contribute to increasing the level of PV penetration and optimal integration in the power grid, improving PV plant management and participating in electricity markets. The objective of this thesis is to contribute to the improvement of the short-term predictability (less than 6 hours) of PV production. First, we analyze the spatio-temporal variability of PV production and propose a method to reduce the nonstationarity of the production series. We then propose a deterministic prediction model that exploits the spatio-temporal correlations between the power plants of a spatial grid. The power stationsare used as a network of sensors to anticipate sources of variability. We also propose an automaticmethod for selecting variables to solve the dimensionality and sparsity problems of the space-time model. A probabilistic spatio-temporal model has also been developed to produce efficient forecasts not only of the average level of future production but of its entire distribution. Finally, we propose a model that exploits observations of satellite images to improve short-term forecasting of PV production

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Книги з теми "Photovoltaic production forecasting"

Large Scale Grid Integration of Renewable Energy Sources. Institution of Engineering & Technology, 2017.

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Large Scale Grid Integration of Renewable Energy Sources. Institution of Engineering & Technology, 2017.

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Частини книг з теми "Photovoltaic production forecasting"

El-Hammouchi, Azeddine, Mohammed Bouafia, Nabil El Akchioui, and Amine El Fathi. "Artificial Intelligence for Forecasting the Photovoltaic Energy Production." In The Proceedings of the International Conference on Electrical Systems & Automation, 47–58. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0035-8_4.

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El Aouni, Abdelaziz, and Salah Eddine Naimi. "Time Series Forecasting of a Photovoltaic Panel Energy Production." In Lecture Notes in Electrical Engineering, 933–41. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6223-3_96.

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Agga, Ali, Ahmed Abbou, and Moussa Labbadi. "Day-Ahead Photovoltaic Power Production Forecasting Following Traditional and Hierarchical Approach." In Digital Technologies and Applications, 172–80. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02447-4_18.

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Harrou, Fouzi, Farid Kadri, and Ying Sun. "Forecasting of Photovoltaic Solar Power Production Using LSTM Approach." In Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.91248.

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Sansa, Ines, and Najiba Mrabet Bellaaj. "Forecasting and Modelling of Solar Radiation for Photovoltaic (PV) Systems." In Solar Radiation - Measurements, Modeling and Forecasting for Photovoltaic Solar Energy Applications [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99499.

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Solar radiation is characterized by its fluctuation because it depends to different factors such as the day hour, the speed wind, the cloud cover and some other weather conditions. Certainly, this fluctuation can affect the PV power production and then its integration on the electrical micro grid. An accurate forecasting of solar radiation is so important to avoid these problems. In this chapter, the solar radiation is treated as time series and it is predicted using the Auto Regressive and Moving Average (ARMA) model. Based on the solar radiation forecasting results, the photovoltaic (PV) power is then forecasted. The choice of ARMA model has been carried out in order to exploit its own strength. This model is characterized by its flexibility and its ability to extract the useful statistical properties, for time series predictions, it is among the most used models. In this work, ARMA model is used to forecast the solar radiation one year in advance considering the weekly radiation averages. Simulation results have proven the effectiveness of ARMA model to forecast the small solar radiation fluctuations.

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Gorbachev, Boris Gusev, Victor Kuzin, Shengli Xie, and Dong Yue. "Preface." In Hybrid Methods of Big Data Analysis and Applications, ix—xviii. Creosar Publishing, 2022. http://dx.doi.org/10.57118/creosar/978-1-915740-01-4_0.

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This volume describes the solution of problems of Big Data intellectual analysis, control, design and optimization. Big Data - technologies that extract maximum benefit from big data, which are widespread in all spheres. Starting with an official introduction to the basics of algorithm hybridization, this book combines many different aspects of current research on hybrid technologies, such as deep neural networks, fuzzy neural networks, multi-MISO ANFIS, fuzzy C-means, conditional disentangled networks, generative adversarial networks, finite difference method and enthalpy method. The book also covers a wide range of applications and implementation problems, from pattern recognition and image generation to intelligent forecasting problems, automation of production in technical applications (3D-analysis, forecasting of distributed photovoltaic systems and loads) with due attention to modeling. It covers a wide range of applications in the field of Big Data analysis, as well as Data Mining. In addition to the traditional tasks of classification, clustering, forecasting, it also discusses original approaches to hybrid optimization and control in the tasks of multi-object optimization for smart grid, natural gas hydrate wellbore, parallel search for optimal technological parameters of the non-consumable electrode welding. The articles are arranged in five thematic topics, I) Strongly coupled (functional) hybrid methods (articles 1-3); II) Loosely coupled (functional) hybrid methods (articles 4-5); III) Transformational hybrid methods (articles 6-7); IV) Integrated methods (articles 8-12) and V) Distributed hybrid intelligent methods (article 13).

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Тези доповідей конференцій з теми "Photovoltaic production forecasting"

Ribeiro, Diogo, Adelaide Cerveira, E. J. Solteiro Pires, and José Baptista. "Modeling and Forecasting Photovoltaic Power Production." In 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2023. http://dx.doi.org/10.1109/icecet58911.2023.10389358.

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Rashkovska, Aleksandra, Jost Novljan, Miha Smolnikar, Mihael Mohorcic, and Carolina Fortuna. "Online short-term forecasting of photovoltaic energy production." In 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2015. http://dx.doi.org/10.1109/isgt.2015.7131880.

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Ciabattoni, Lucio, Massimo Grisostomi, Gianluca Ippoliti, Sauro Longhi, and Emanuele Mainardi. "Online tuned neural networks for PV plant production forecasting." In 2012 IEEE 38th Photovoltaic Specialists Conference (PVSC). IEEE, 2012. http://dx.doi.org/10.1109/pvsc.2012.6318197.

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D'Andrea, Eleonora, and Beatrice Lazzerini. "Fuzzy forecasting of energy production in solar photovoltaic installations." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251161.

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GONçALVES, NAIRON AUGUSTO MONARI, ANTONIO CESAR GERMANO MARTINS, and NICOLAS FOURMAUX. "Photovoltaic Energy Production Forecasting Using LSTM and Cross-Validation." In Congresso Brasileiro de Geração Distribuída (CBGD 2023). sepocb, 2023. http://dx.doi.org/10.53316/cbgd2023.026.

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Aillaud, Pierre, Jeremie Lequeux, Johan Mathe, Laurent Huet, Caroline Lallemand, Olivier Liandrat, Nicolas Sebastien, Frederik Kurzrock, and Nicolas Schmutz. "Day-ahead forecasting of regional photovoltaic production using deep learning." In 2020 IEEE 47th Photovoltaic Specialists Conference (PVSC). IEEE, 2020. http://dx.doi.org/10.1109/pvsc45281.2020.9300538.

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Zhang, Yue, Marc Beaudin, Hamidreza Zareipour, and David Wood. "Forecasting Solar Photovoltaic power production at the aggregated system level." In 2014 North American Power Symposium (NAPS). IEEE, 2014. http://dx.doi.org/10.1109/naps.2014.6965389.

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Voicu, Vladimir, Dorin Petreus, Emil Cebuc, and Radu Etz. "Data Acquisition System for Forecasting Applications of Photovoltaic Power Production." In 2023 22nd RoEduNet Conference: Networking in Education and Research (RoEduNet). IEEE, 2023. http://dx.doi.org/10.1109/roedunet60162.2023.10274911.

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Theocharides, Spyros, Georgios Tziolis, Javier Lopez-Lorente, George Makrides, and George E. Georghiou. "Impact of Data Quality on Day-ahead Photovoltaic Power Production Forecasting." In 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC). IEEE, 2021. http://dx.doi.org/10.1109/pvsc43889.2021.9518471.

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Pelisson, Angelo, Thiago Covoes, Anderson Spengler, and Pablo Jaskowiak. "Comparative Study of Photovoltaic Power Forecasting Methods." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/eniac.2020.12159.

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Electricity consumption is growing rapidly worldwide. Renewable energy resources, such as solar energy, play a crucial role in this scenario, contributing to satisfy demand sustainability. Although the share of Photovoltaic (PV) power generation has increased in the past years, PV systems are quite sensitive to climatic and meteorological conditions, leading to undesirable power production variability. In order to improve energy grid stability, reliability, and management, accurate forecasting models that relate operational conditions to power output are needed. In this work we evaluate the performance of regression methods applied to forecast short term (next day) energy production of a PV Plant. Specifically, we consider five regression methods and different configurations of feature sets. Our results suggest that MLP and SVR provide the best forecasting results, in general. Also, although features based on different solar irradiance levels play a key role in predicting power generation, the use of additional features can improve prediction results.

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