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Journal articles on the topic 'Photovoltaic production forecasting'

Author: Grafiati

Published: 28 February 2024

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1

Paulescu, Marius, Nicoleta Stefu, Ciprian Dughir, et al. "Online Forecasting of the Solar Energy Production." Annals of West University of Timisoara - Physics 60, no. 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 betwee

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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 (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 (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, sunlig

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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 bas

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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 (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 prod

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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 (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. Artifi

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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 (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

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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 (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 a

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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 (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 stat

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Rogus, Radomir, Maciej Sołtysik, and Rafał Czapaj. "Application of similarity analysis in PV sources generation forecasting for energy clusters." E3S Web of Conferences 84 (2019): 01009. http://dx.doi.org/10.1051/e3sconf/20198401009.

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The resultant photovoltaic installation powers significantly affect the process of cluster coordination in terms of balancing, which is associated with the need for the most accurate forecast of photovoltaic generation. This article describes the application of similarity analysis in order to use commonly available meteorological data for predicting generation level from photovoltaic sources on the example of several selected installations and their corresponding real production profiles.

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Jakoplić, Alen, Dubravko Franković, Juraj Havelka, and Hrvoje Bulat. "Short-Term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning." Energies 16, no. 14 (2023): 5428. http://dx.doi.org/10.3390/en16145428.

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With the steady increase in the use of renewable energy sources in the energy sector, new challenges arise, especially the unpredictability of these energy sources. This uncertainty complicates the management, planning, and development of energy systems. An effective solution to these challenges is short-term forecasting of the output of photovoltaic power plants. In this paper, a novel method for short-term production prediction was explored which involves continuous photography of the sky above the photovoltaic power plant. By analyzing a series of sky images, patterns can be identified to h

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Cabezón, L., L. G. B. Ruiz, D. Criado-Ramón, E. J. Gago, and M. C. Pegalajar. "Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study." Energies 15, no. 22 (2022): 8732. http://dx.doi.org/10.3390/en15228732.

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Photovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic systems. This research addresses this problem by implementing intelligent models to predict the production of solar ener

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Theocharides, Spyros, Marios Theristis, George Makrides, Marios Kynigos, Chrysovalantis Spanias, and George E. Georghiou. "Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting." Energies 14, no. 4 (2021): 1081. http://dx.doi.org/10.3390/en14041081.

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A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges by evaluating the day-ahead PV production forecasting performance of different machine learning models under different supervised learning regimes and minimal input features. Specifically, the day-ahead forecasting capability of Bayesian neural network (BNN), support vector regression (SVR), and regression tree (RT) models was investigated by empl

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Yang, Huixuan, Ming Su, Xin Li, Ruizhao Zhang, and Jinhui Liu. "Distributed Energy Grid-Connected Dense Data Forecasting Technology Based on Federated Learning." Journal of Physics: Conference Series 2592, no. 1 (2023): 012013. http://dx.doi.org/10.1088/1742-6596/2592/1/012013.

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Abstract Photovoltaic power generation system is one of the main clean energy power generation systems at present, which plays an important role in daily production and life. However, the photovoltaic power generation system is easily affected by various factors, and the output power will be unstable in the practical application process, which will affect the power generation efficiency. In this paper, a prediction method of distributed energy grid-connected dense data based on federated learning is constructed. This method can not only realize the short-term prediction of distributed photovol

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Oneto, Luca, Federica Laureri, Michela Robba, Federico Delfino, and Davide Anguita. "Data-Driven Photovoltaic Power Production Nowcasting and Forecasting for Polygeneration Microgrids." IEEE Systems Journal 12, no. 3 (2018): 2842–53. http://dx.doi.org/10.1109/jsyst.2017.2688359.

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van der Meer, D. W., J. Widén, and J. Munkhammar. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption." Renewable and Sustainable Energy Reviews 81 (January 2018): 1484–512. http://dx.doi.org/10.1016/j.rser.2017.05.212.

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Monteiro, Claudio, L. Alfredo Fernandez-Jimenez, Ignacio J. Ramirez-Rosado, Andres Muñoz-Jimenez, and Pedro M. Lara-Santillan. "Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/767284.

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We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV) plants: the analytical PV power forecasting model (APVF) and the multiplayer perceptron PV forecasting model (MPVF). Both models use forecasts from numerical weather prediction (NWP) tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adjusting irradiation data of clear sky by an irradiation attenuation index, combined with a PV power prod

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Khalyasmaa, Alexandra I., Stanislav A. Eroshenko, Valeriy A. Tashchilin, Hariprakash Ramachandran, Teja Piepur Chakravarthi, and Denis N. Butusov. "Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning." Remote Sensing 12, no. 20 (2020): 3420. http://dx.doi.org/10.3390/rs12203420.

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This article highlights the industry experience of the development and practical implementation of a short-term photovoltaic forecasting system based on machine learning methods for a real industry-scale photovoltaic power plant implemented in a Russian power system using remote data acquisition. One of the goals of the study is to improve photovoltaic power plants generation forecasting accuracy based on open-source meteorological data, which is provided in regular weather forecasts. In order to improve the robustness of the system in terms of the forecasting accuracy, we apply newly derived

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Fara, Laurentiu, Alexandru Diaconu, Dan Craciunescu, and Silvian Fara. "Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models." International Journal of Photoenergy 2021 (August 3, 2021): 1–19. http://dx.doi.org/10.1155/2021/6777488.

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Accurate forecasting of solar energy is essential for photovoltaic (PV) plants, to facilitate their participation in the energy market and for efficient resource planning. This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. The main contributions of the authors could be synthetized as follows: (1) analysis and discussion of the experimental and simulated results regarding solar radiation

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Cantillo-Luna, Sergio, Ricardo Moreno-Chuquen, David Celeita, and George Anders. "Deep and Machine Learning Models to Forecast Photovoltaic Power Generation." Energies 16, no. 10 (2023): 4097. http://dx.doi.org/10.3390/en16104097.

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The integration and management of distributed energy resources (DERs), including residential photovoltaic (PV) production, coupled with the widespread use of enabling technologies such as artificial intelligence, have led to the emergence of new tools, market models, and business opportunities. The accurate forecasting of these resources has become crucial to decision making, despite data availability and reliability issues in some parts of the world. To address these challenges, this paper proposes a deep and machine learning-based methodology for PV power forecasting, which includes XGBoost,

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Dawan, Promphak, Kobsak Sriprapha, Songkiate Kittisontirak, et al. "Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model." Energies 13, no. 2 (2020): 351. http://dx.doi.org/10.3390/en13020351.

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The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN mo

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Bracale, Antonio, Guido Carpinelli, Annarita Di Fazio, and Shahab Khormali. "Advanced, Cost-Based Indices for Forecasting the Generation of Photovoltaic Power." International Journal of Emerging Electric Power Systems 15, no. 1 (2014): 77–91. http://dx.doi.org/10.1515/ijeeps-2013-0131.

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Abstract Distribution systems are undergoing significant changes as they evolve toward the grids of the future, which are known as smart grids (SGs). The perspective of SGs is to facilitate large-scale penetration of distributed generation using renewable energy sources (RESs), encourage the efficient use of energy, reduce systems’ losses, and improve the quality of power. Photovoltaic (PV) systems have become one of the most promising RESs due to the expected cost reduction and the increased efficiency of PV panels and interfacing converters. The ability to forecast power-production informati

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Ouédraogo, Sarah, Ghjuvan Antone Faggianelli, Guillaume Pigelet, Jean Laurent Duchaud, and Gilles Notton. "Application of Optimal Energy Management Strategies for a Building Powered by PV/Battery System in Corsica Island." Energies 13, no. 17 (2020): 4510. http://dx.doi.org/10.3390/en13174510.

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The use of renewable energy sources, and in particular photovoltaics, can effectively reduce the supply of household energy from the main grid, contributing to a more sustainable community. In this paper, several energy management strategies were applied to an existing microgrid with photovoltaic (PV) production and battery storage in view to supply in electricity a building and an electric vehicle located in Ajaccio, France. The purpose was to determine how the choice of a management strategy can impact the cost and the energy share in the microgrid, using the actual electricity tariff in Fra

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Sumorek, Mateusz, and Adam Idzkowski. "Time Series Forecasting for Energy Production in Stand-Alone and Tracking Photovoltaic Systems Based on Historical Measurement Data." Energies 16, no. 17 (2023): 6367. http://dx.doi.org/10.3390/en16176367.

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This article presents a time series analysis for predicting energy production in photovoltaic (PV) power plant systems, namely fixed and solar-tracking ones, which were located in the north-east of Poland. The purpose of one-day forecasts is to determine the effectiveness of preventive actions and manage power systems effectively. The impact of climate variables affecting the production of electricity in the photovoltaic systems was analyzed. Forecasting models based on traditional machine learning (ML) techniques and multi-layer perceptron (MLP) neural networks were created without using sola

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Drałus, Grzegorz, Damian Mazur, Jacek Kusznier, and Jakub Drałus. "Application of Artificial Intelligence Algorithms in Multilayer Perceptron and Elman Networks to Predict Photovoltaic Power Plant Generation." Energies 16, no. 18 (2023): 6697. http://dx.doi.org/10.3390/en16186697.

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This paper presents the models developed for the short-term forecasting of energy production by photovoltaic panels. An analysis of a set of weather factors influencing daily energy production is presented. Determining the correlation between the produced direct current (DC) energy and the individual weather parameters allowed the selection of the potentially best explanatory factors, which served as input data for the neural networks. The forecasting models were based on MLP and Elman-type networks. An appropriate selection of structures and learning parameters was carried out, as well as the

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Lehmann, Jonathan, Christian Koessler, Lina Ruiz Gomez, and Stijn Scheerlinck. "Benchmark of eight commercial solutions for deterministic intra-day solar forecast." EPJ Photovoltaics 14 (2023): 15. http://dx.doi.org/10.1051/epjpv/2023006.

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Continuous increase of the production of photovoltaic energy requires precise forecasting to ensure grid stability. This paper presents a detailed benchmark of eight commercial forecasting solutions for intra-day solar forecasts. The comparison was carried out on a period of six months, from November to May 2021, on seven different PV plants located in different countries of the northern hemisphere. Performance evaluation metrics MAE, RMSE and MBE are used in order to analyze the forecasting precision. It is shown that forecasting solar power remains challenging, as shown by the important disp

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Dairi, Abdelkader, Fouzi Harrou, Ying Sun, and Sofiane Khadraoui. "Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach." Applied Sciences 10, no. 23 (2020): 8400. http://dx.doi.org/10.3390/app10238400.

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The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar power production using Variational AutoEncoder (VAE) model. Adopting the VAE-driven deep learning model is expected to improve forecasting accuracy because of its suitable performance in time-series modeling and flexible nonlinear approximation. Both single- and multi-step-ahead forecasts are investigated in this work. Data from two grid-connect

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Hussain, Altaf, Zulfiqar Ahmad Khan, Tanveer Hussain, Fath U. Min Ullah, Seungmin Rho, and Sung Wook Baik. "A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting." Complexity 2022 (October 5, 2022): 1–12. http://dx.doi.org/10.1155/2022/7040601.

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For efficient energy distribution, microgrids (MG) provide significant assistance to main grids and act as a bridge between the power generation and consumption. Renewable energy generation resources, particularly photovoltaics (PVs), are considered as a clean source of energy but are highly complex, volatile, and intermittent in nature making their forecasting challenging. Thus, a reliable, optimized, and a robust forecasting method deployed at MG objectifies these challenges by providing accurate renewable energy production forecasting and establishing a precise power generation and consumpt

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Fernandez-Jimenez, L. Alfredo, Sonia Terreros-Olarte, Alberto Falces, Pedro M. Lara-Santillan, Enrique Zorzano-Alba, and Pedro J. Zorzano-Santamaria. "Probabilistic reference model for hourly PV power generation forecasting." E3S Web of Conferences 152 (2020): 01002. http://dx.doi.org/10.1051/e3sconf/202015201002.

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This paper presents a new probabilistic forecasting model of the hourly mean power production in a Photovoltaic (PV) plant. It uses the minimal information and it can provide probabilistic forecasts in the form of quantiles for the desired horizon, which ranges from the next hours to any day in the future. The proposed model only needs a time series of hourly mean power production in the PV plant, and it is intended to fill a gap in international literature where hardly any model has been proposed as a reference for comparison or benchmarking purposes with other probabilistic forecasting model

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Alomari, Mohammad H., Jehad Adeeb, and Ola Younis. "PVPF tool: an automatedWeb application for real-time photovoltaic power forecasting." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 34. http://dx.doi.org/10.11591/ijece.v9i1.pp34-41.

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<p>In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine lea

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Sinkovics, B., and B. Hartmann. "Analysing Effect of Solar Photovoltaic Production on Load Curves and their Forecasting." Renewable Energy and Power Quality Journal 1 (April 2018): 760–65. http://dx.doi.org/10.24084/repqj16.462.

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Mellit, A., A. Massi Pavan, and V. Lughi. "Short-term forecasting of power production in a large-scale photovoltaic plant." Solar Energy 105 (July 2014): 401–13. http://dx.doi.org/10.1016/j.solener.2014.03.018.

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Gao, Li, Hong, and Long. "Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM." Applied Sciences 9, no. 15 (2019): 3192. http://dx.doi.org/10.3390/app9153192.

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Photovoltaic (PV) power is attracting more and more concerns. Power output prediction, as a necessary technical requirement of PV plants, closely relates to the rationality of power grid dispatch. If the accuracy of power prediction in PV plants can be further enhanced by forecasting, stability of power grid will be improved. Therefore, a 1-h-ahead power output forecasting based on long-short-term memory (LSTM) networks is proposed. The forecasting output of the model is based on the time series of 1-h-ahead numerical weather prediction to reveal the spatio-temporal characteristic. The compreh

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Xue, Jizhong, Zaohui Kang, Chun Sing Lai, Yu Wang, Fangyuan Xu, and Haoliang Yuan. "Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)." Energies 16, no. 11 (2023): 4436. http://dx.doi.org/10.3390/en16114436.

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The future power grid will have more distributed energy sources, and the widespread access of distributed energy sources has the potential to improve the energy efficiency, resilience, and sustainability of the system. However, distributed energy, mainly wind power generation and photovoltaic power generation, has the characteristics of intermittency and strong randomness, which will bring challenges to the safe operation of the power grid. Accurate prediction of solar power generation with high spatial and temporal resolution is very important for the normal operation of the power grid. In or

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Ricci, Leonardo, and Davide Papurello. "A Prediction Model for Energy Production in a Solar Concentrator Using Artificial Neural Networks." International Journal of Energy Research 2023 (July 27, 2023): 1–20. http://dx.doi.org/10.1155/2023/9196506.

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Solar energy is widely adopted today and produced by photovoltaic or concentrator solar power (CSP). Photovoltaic technology is the most prevalent, thanks to its well-established technology and low costs. CSP technology, on the other hand, has received less attention and interest, as it requires larger investments and a considerable surface. A relevant difficulty connected to the CSP is decoupling solar randomness and energy production. This paper proposes an artificial neural network (ANN) which foresees energy production using a solar parabolic dish installed at Politecnico di Torino (Energy

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Konstantinou, Maria, Stefani Peratikou, and Alexandros G. Charalambides. "Solar Photovoltaic Forecasting of Power Output Using LSTM Networks." Atmosphere 12, no. 1 (2021): 124. http://dx.doi.org/10.3390/atmos12010124.

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The penetration of renewable energies has increased during the last decades since it has become an effective solution to the world’s energy challenges. Among all renewable energy sources, photovoltaic (PV) technology is the most immediate way to convert solar radiation into electricity. Nevertheless, PV power output is affected by several factors, such as location, clouds, etc. As PV plants proliferate and represent significant contributors to grid electricity production, it becomes increasingly important to manage their inherent alterability. Therefore, solar PV forecasting is a pivotal facto

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Pandžić, Franko, and Tomislav Capuder. "Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources." Energies 17, no. 1 (2023): 97. http://dx.doi.org/10.3390/en17010097.

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Solar forecasting is becoming increasingly important due to the exponential growth in total global solar capacity each year. More photovoltaic (PV) penetration in the grid poses problems for grid stability due to the inherent intermittent and variable nature of PV power production. Therefore, forecasting of solar quantities becomes increasingly important to grid operators and market participants. This review presents the most recent relevant studies focusing on short-term forecasting of solar irradiance and PV power production. Recent research has increasingly turned to machine learning to add

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Gutiérrez, Leidy, Julian Patiño, and Eduardo Duque-Grisales. "A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction." Energies 14, no. 15 (2021): 4424. http://dx.doi.org/10.3390/en14154424.

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Science seeks strategies to mitigate global warming and reduce the negative impacts of the long-term use of fossil fuels for power generation. In this sense, implementing and promoting renewable energy in different ways becomes one of the most effective solutions. The inaccuracy in the prediction of power generation from photovoltaic (PV) systems is a significant concern for the planning and operational stages of interconnected electric networks and the promotion of large-scale PV installations. This study proposes the use of Machine Learning techniques to model the photovoltaic power producti

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Li, Zhaoxuan, SM Rahman, Rolando Vega, and Bing Dong. "A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting." Energies 9, no. 1 (2016): 55. http://dx.doi.org/10.3390/en9010055.

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Popławski, Tomasz, Sebastian Dudzik, and Piotr Szeląg. "Forecasting of Energy Balance in Prosumer Micro-Installations Using Machine Learning Models." Energies 16, no. 18 (2023): 6726. http://dx.doi.org/10.3390/en16186726.

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It is indisputable that power systems are being transformed around the world to increase the use of RES and reduce the use of fossil fuels in overall electricity production. This year, the EU Parliament adopted the Fit for 55 package, which should significantly reduce the use of fossil fuels in the energy balance of EU countries while increasing the use of RES. At the end of 2022, the total number of prosumer installations in Poland amounted to about one million two hundred thousand. Such a high saturation of prosumer micro-installations in the power system causes many threats resulting from t

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Salimbeni, Andrea, Mario Porru, Luca Massidda, and Alfonso Damiano. "A Forecasting-Based Control Algorithm for Improving Energy Managment in High Concentrator Photovoltaic Power Plant Integrated with Energy Storage Systems." Energies 13, no. 18 (2020): 4697. http://dx.doi.org/10.3390/en13184697.

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The High Concentrator Photovoltaic (HCPV) technology, due to its high efficiency, is considered one of the most promising solutions for the exploitation of sun-irradiation-based Renewable Energy Sources (RES). Nevertheless, the HCPV production is strictly connected to the Direct Normal Irradiation (DNI) making this photovoltaic technology more sensible to cloudiness than traditional ones. In order to mitigate the power intermittence and improve production programmability, the integration between Energy Storage Systems (ESSs) and HCPV, resorting to forecasting algorithms, has been investigated.

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Yang, Heng, and Weisong Wang. "Prediction of photovoltaic power generation based on LSTM and transfer learning digital twin." Journal of Physics: Conference Series 2467, no. 1 (2023): 012015. http://dx.doi.org/10.1088/1742-6596/2467/1/012015.

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Abstract This research offers a digital twin model for solar power production power prediction based on long short term memory network (LSTM), and then applies this model to other models with limited operational time and inadequate data through transfer learning. The prediction for the solar system’s electrical output. Due to the effect of sun irradiation, temperature, and other random elements, photovoltaic power output is very intermittent and fluctuating, making it impossible to anticipate photovoltaic power with precision. Synchronization and real-time updating of physical entities, thereb

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Je, Seung-Mo, Hyeyoung Ko, and Jun-Ho Huh. "Accurate Demand Forecasting: A Flexible and Balanced Electric Power Production Big Data Virtualization Based on Photovoltaic Power Plant." Energies 14, no. 21 (2021): 6915. http://dx.doi.org/10.3390/en14216915.

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This paper has tried to execute accurate demand forecasting by utilizing big data visualization and proposes a flexible and balanced electric power production big data virtualization based on a photovoltaic power plant. First of all, this paper has tried to align electricity demand and supply as much as possible using big data. Second, by using big data to predict the supply of new renewable energy, an attempt was made to incorporate new and renewable energy into the current power supply system and to recommend an efficient energy distribution method. The first presented problem that had to be

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Alomari, Mohammad H., Jehad Adeeb, and Ola Younis. "Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural Networks." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 1 (2018): 497. http://dx.doi.org/10.11591/ijece.v8i1.pp497-504.

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In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum N

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Lobato-Nostroza, Oscar, Gerardo Marx Chávez-Campos, Antony Morales-Cervantes, et al. "Predictive Modeling of Photovoltaic Panel Power Production through On-Site Environmental and Electrical Measurements Using Artificial Neural Networks." Metrology 3, no. 4 (2023): 347–64. http://dx.doi.org/10.3390/metrology3040021.

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Weather disturbances pose a significant challenge when estimating the energy production of photovoltaic panel systems. Energy production and forecasting models have recently been used to improve energy estimations and maintenance tasks. However, these models often rely on environmental measurements from meteorological units far from the photovoltaic systems. To enhance the accuracy of the developed model, a measurement Internet of Things (IoT) prototype was developed in this study, which collects on-site voltage and current measurements from the panel, as well as the environmental factors of l

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Aatif Mohi Ud Din, Vivek Gupta. "Forecasting and Prediction of Solar Energy in Solar Photovoltaic Plants." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 1457–69. http://dx.doi.org/10.52783/tjjpt.v44.i4.1080.

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An accurate solar energy projection is essential for a better the degree to which renewable energy is integrated into the functioning of the present power system. Due to the availability of data at previously unheard-of granularities, data-driven algorithms may be utilised to improve solar energy forecasts. In this study, two deep learning algorithms—the k Nearest Neighbor and Random Forest—are presented as the foundational models for the improved, globally applicable stackable ensemble technique. The results the core models are merged with a significant gradient boost technique, improving the

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Huertas Tato, Javier, and Miguel Centeno Brito. "Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production." Energies 12, no. 1 (2018): 100. http://dx.doi.org/10.3390/en12010100.

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Solar energy forecasting is an active research problem and a key issue to increase the competitiveness of solar power plants in the energy market. However, using meteorological, production, or irradiance data from the past is not enough to produce accurate forecasts. This article aims to integrate a prediction algorithm (Smart Persistence), irradiance, and past production data, using a state-of-the-art machine learning technique (Random Forests). Three years of data from six solar PV modules at Faro (Portugal) are analyzed. A set of features that combines past data, predictions, averages, and

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Collino, Elena, and Dario Ronzio. "Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System." Energies 14, no. 3 (2021): 789. http://dx.doi.org/10.3390/en14030789.

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The relentless spread of photovoltaic production drives searches of smart approaches to mitigate unbalances in power demand and supply, instability on the grid and ensuring stable revenues to the producer. Because of the development of energy markets with multiple time sessions, there is a growing need of power forecasting for multiple time steps, from fifteen minutes up to days ahead. To address this issue, in this study both a short-term-horizon of three days and a very-short-term-horizon of three hours photovoltaic production forecasting methods are presented. The short-term is based on a m

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Bugała, Artur, and Karol Bednarek. "The use of computer simulations and measurements in determining the energy efficiency of photovoltaic installations." ITM Web of Conferences 19 (2018): 01021. http://dx.doi.org/10.1051/itmconf/20181901021.

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The paper presents an analysis of the energy efficiency of a photovoltaic installation connected to the AC power grid. Forecasting of energy yield was carried out in two ways: with the use of PVSol software, as well as physical measurements of daily electricity production. On that basis, a statistical correlation between the results obtained from measurements and computer calculations was determined.

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