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Artykuły w czasopismach na temat "Photovoltaic production forecasting"
Paulescu, Marius, Nicoleta Stefu, Ciprian Dughir, Robert Blaga, Andreea Sabadus, Eugenia Paulescu i Sorin Bojin. "Online Forecasting of the Solar Energy Production". Annals of West University of Timisoara - Physics 60, nr 1 (1.08.2018): 104–10. http://dx.doi.org/10.2478/awutp-2018-0011.
Pełny tekst źródłaPicault, D., B. Raison, S. Bacha, J. de la Casa i J. Aguilera. "Forecasting photovoltaic array power production subject to mismatch losses". Solar Energy 84, nr 7 (lipiec 2010): 1301–9. http://dx.doi.org/10.1016/j.solener.2010.04.009.
Pełny tekst źródłaAgoua, Xwegnon Ghislain, Robin Girard i George Kariniotakis. "Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production". IEEE Transactions on Sustainable Energy 9, nr 2 (kwiecień 2018): 538–46. http://dx.doi.org/10.1109/tste.2017.2747765.
Pełny tekst źródłaMilicevic, Marina, i Budimirka Marinovic. "Machine learning methods in forecasting solar photovoltaic energy production". Thermal Science, nr 00 (2023): 150. http://dx.doi.org/10.2298/tsci230402150m.
Pełny tekst źródłaCastillo-Rojas, Wilson, Juan Bekios-Calfa i 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 (18.04.2023): 1–19. http://dx.doi.org/10.1155/2023/2592405.
Pełny tekst źródłaJakoplić, A., S. Vlahinić, B. Dobraš i D. Franković. "Sky Image Analysis and Solar Power Forecasting: A Convolutional Neural Network Approach". Renewable Energy and Power Quality Journal 21, nr 1 (lipiec 2023): 456–61. http://dx.doi.org/10.24084/repqj21.355.
Pełny tekst źródłaCordeiro-Costas, Moisés, Daniel Villanueva, Pablo Eguía-Oller i Enrique Granada-Álvarez. "Machine Learning and Deep Learning Models Applied to Photovoltaic Production Forecasting". Applied Sciences 12, nr 17 (31.08.2022): 8769. http://dx.doi.org/10.3390/app12178769.
Pełny tekst źródłaRangel-Heras, Eduardo, César Angeles-Camacho, Erasmo Cadenas-Calderón i Rafael Campos-Amezcua. "Short-Term Forecasting of Energy Production for a Photovoltaic System Using a NARX-CVM Hybrid Model". Energies 15, nr 8 (13.04.2022): 2842. http://dx.doi.org/10.3390/en15082842.
Pełny tekst źródłaSarmas, Elissaios, Sofoklis Strompolas, Vangelis Marinakis, Francesca Santori, Marco Antonio Bucarelli i Haris Doukas. "An Incremental Learning Framework for Photovoltaic Production and Load Forecasting in Energy Microgrids". Electronics 11, nr 23 (29.11.2022): 3962. http://dx.doi.org/10.3390/electronics11233962.
Pełny tekst źródłaBachici, Miroslav-Andrei, i 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, nr 1 (1.12.2020): 80–89. http://dx.doi.org/10.2478/ijasitels-2020-0009.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaCarriere, 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.
Pełny tekst źródłaThe 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
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.
Pełny tekst źródłaOur 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
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.
Pełny tekst źródłaMicrogrids 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
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.
Pełny tekst źródłaThe 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
Książki na temat "Photovoltaic production forecasting"
Large Scale Grid Integration of Renewable Energy Sources. Institution of Engineering & Technology, 2017.
Znajdź pełny tekst źródłaLarge Scale Grid Integration of Renewable Energy Sources. Institution of Engineering & Technology, 2017.
Znajdź pełny tekst źródłaCzęści książek na temat "Photovoltaic production forecasting"
El-Hammouchi, Azeddine, Mohammed Bouafia, Nabil El Akchioui i Amine El Fathi. "Artificial Intelligence for Forecasting the Photovoltaic Energy Production". W 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.
Pełny tekst źródłaEl Aouni, Abdelaziz, i Salah Eddine Naimi. "Time Series Forecasting of a Photovoltaic Panel Energy Production". W Lecture Notes in Electrical Engineering, 933–41. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6223-3_96.
Pełny tekst źródłaAgga, Ali, Ahmed Abbou i Moussa Labbadi. "Day-Ahead Photovoltaic Power Production Forecasting Following Traditional and Hierarchical Approach". W Digital Technologies and Applications, 172–80. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02447-4_18.
Pełny tekst źródłaHarrou, Fouzi, Farid Kadri i Ying Sun. "Forecasting of Photovoltaic Solar Power Production Using LSTM Approach". W Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.91248.
Pełny tekst źródłaSansa, Ines, i Najiba Mrabet Bellaaj. "Forecasting and Modelling of Solar Radiation for Photovoltaic (PV) Systems". W Solar Radiation - Measurements, Modeling and Forecasting for Photovoltaic Solar Energy Applications [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99499.
Pełny tekst źródłaGorbachev, Boris Gusev, Victor Kuzin, Shengli Xie i Dong Yue. "Preface". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "Photovoltaic production forecasting"
Ribeiro, Diogo, Adelaide Cerveira, E. J. Solteiro Pires i José Baptista. "Modeling and Forecasting Photovoltaic Power Production". W 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2023. http://dx.doi.org/10.1109/icecet58911.2023.10389358.
Pełny tekst źródłaRashkovska, Aleksandra, Jost Novljan, Miha Smolnikar, Mihael Mohorcic i Carolina Fortuna. "Online short-term forecasting of photovoltaic energy production". W 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2015. http://dx.doi.org/10.1109/isgt.2015.7131880.
Pełny tekst źródłaCiabattoni, Lucio, Massimo Grisostomi, Gianluca Ippoliti, Sauro Longhi i Emanuele Mainardi. "Online tuned neural networks for PV plant production forecasting". W 2012 IEEE 38th Photovoltaic Specialists Conference (PVSC). IEEE, 2012. http://dx.doi.org/10.1109/pvsc.2012.6318197.
Pełny tekst źródłaD'Andrea, Eleonora, i Beatrice Lazzerini. "Fuzzy forecasting of energy production in solar photovoltaic installations". W 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251161.
Pełny tekst źródłaGONçALVES, NAIRON AUGUSTO MONARI, ANTONIO CESAR GERMANO MARTINS i NICOLAS FOURMAUX. "Photovoltaic Energy Production Forecasting Using LSTM and Cross-Validation". W Congresso Brasileiro de Geração Distribuída (CBGD 2023). sepocb, 2023. http://dx.doi.org/10.53316/cbgd2023.026.
Pełny tekst źródłaAillaud, Pierre, Jeremie Lequeux, Johan Mathe, Laurent Huet, Caroline Lallemand, Olivier Liandrat, Nicolas Sebastien, Frederik Kurzrock i Nicolas Schmutz. "Day-ahead forecasting of regional photovoltaic production using deep learning". W 2020 IEEE 47th Photovoltaic Specialists Conference (PVSC). IEEE, 2020. http://dx.doi.org/10.1109/pvsc45281.2020.9300538.
Pełny tekst źródłaZhang, Yue, Marc Beaudin, Hamidreza Zareipour i David Wood. "Forecasting Solar Photovoltaic power production at the aggregated system level". W 2014 North American Power Symposium (NAPS). IEEE, 2014. http://dx.doi.org/10.1109/naps.2014.6965389.
Pełny tekst źródłaVoicu, Vladimir, Dorin Petreus, Emil Cebuc i Radu Etz. "Data Acquisition System for Forecasting Applications of Photovoltaic Power Production". W 2023 22nd RoEduNet Conference: Networking in Education and Research (RoEduNet). IEEE, 2023. http://dx.doi.org/10.1109/roedunet60162.2023.10274911.
Pełny tekst źródłaTheocharides, Spyros, Georgios Tziolis, Javier Lopez-Lorente, George Makrides i George E. Georghiou. "Impact of Data Quality on Day-ahead Photovoltaic Power Production Forecasting". W 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC). IEEE, 2021. http://dx.doi.org/10.1109/pvsc43889.2021.9518471.
Pełny tekst źródłaPelisson, Angelo, Thiago Covoes, Anderson Spengler i Pablo Jaskowiak. "Comparative Study of Photovoltaic Power Forecasting Methods". W 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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