Journal articles: 'Photovoltaic production forecasting'

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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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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 (July 17, 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 help predict future photovoltaic power generation. A hybrid model that integrates both a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) for short-term production forecasting was developed and tested. This model effectively detects spatial and temporal patterns from images and power output data, displaying considerable prediction accuracy. In particular, a 74% correlation was found between the model’s predictions and actual future production values, demonstrating the model’s efficiency. The results of this paper suggest that the hybrid CNN-LSTM model offers an improvement in prediction accuracy and practicality compared to traditional forecasting methods. This paper highlights the potential of Deep Learning in improving renewable energy practices, particularly in power prediction, contributing to the overall sustainability of power systems.

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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 (November 20, 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 energy. Real data from a solar farm in Scotland was utilized in this study. Finally, the models were able to accurately predict the energy to be produced in the next hour using historical information as predictor variables.

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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 (February 18, 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 employing the same dataset for training and performance verification, thus enabling a valid comparison. The training regime analysis demonstrated that the performance of the investigated models was strongly dependent on the timeframe of the train set, training data sequence, and application of irradiance condition filters. Furthermore, accurate results were obtained utilizing only the measured power output and other calculated parameters for training. Consequently, useful information is provided for establishing a robust day-ahead forecasting methodology that utilizes calculated input parameters and an optimal supervised learning approach. Finally, the obtained results demonstrated that the optimally constructed BNN outperformed all other machine learning models achieving forecasting accuracies lower than 5%.

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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 (September 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 photovoltaic power generation data, but also ensure that the data can be encrypted and modeled, thus solving the “digital island” problem. The model evaluation shows that the method in this paper performs well in short-term photovoltaic power generation prediction, and it can predict the short-term power generation of different photovoltaic power stations with high prediction accuracy. This method is of great significance to improve the management and scheduling ability and energy utilization rate of distributed photovoltaic power generation systems.

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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 (September 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 production attenuation index. The MPVF model consists of an artificial neural network based model (selected among a large set of ANN optimized with genetic algorithms, GAs). The two models use forecasts from the same NWP tool as inputs. The APVF and MPVF models have been applied to a real-life case study of a grid-connected PV plant using the same data. Despite the fact that both models are quite different, they achieve very similar results, with forecast horizons covering all the daylight hours of the following day, which give a good perspective of their applicability for PV electric production sale bids to electricity markets.

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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 (October 18, 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 feature introduction, a factor obtained as a result of feature engineering procedure, characterizing the relationship between photovoltaic power plant energy production and solar irradiation on a horizontal surface, thus taking into account the impacts of atmospheric and electrical nature. The article scrutinizes the application of different machine learning algorithms, including Random Forest regressor, Gradient Boosting Regressor, Linear Regression and Decision Trees regression, to the remotely obtained data. As a result of the application of the aforementioned approaches together with hyperparameters, tuning and pipelining of the algorithms, the optimal structure, parameters and the application sphere of different regressors were identified for various testing samples. The mathematical model developed within the framework of the study gave us the opportunity to provide robust photovoltaic energy forecasting results with mean accuracy over 92% for mostly-sunny sample days and over 83% for mostly cloudy days with different types of precipitation.

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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 forecast, as well as energy production prediction and forecasting based on ARIMA and ANN models for two case studies: (a) laboratory BIPV system developed at the Polytechnic University of Bucharest and (b) large PV park placed in a specific site of the south of Romania. A variability index of solar radiation was introduced for the model improvement; (2) comparison between the ARIMA and ANN results to highlight the ARIMA model which is more efficient than the ANN one; (3) optimized method defined by the GMDH model (Group Method of Data Handling) proposed to provide a software program for calculation of the PV energy production.

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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 (May 15, 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, random forest, support vector regressor, multi-layer perceptron, and LSTM-based tuned models, and introduces the ConvLSTM1D approach for this task. These models were evaluated on the univariate time-series prediction of low-volume residential PV production data across various forecast horizons. The proposed benchmarking and analysis approach considers technical and economic impacts, which can provide valuable insights for decision-making tools with these resources. The results indicate that the random forest and ConvLSTM1D model approaches yielded the most accurate forecasting performance, as demonstrated by the lowest RMSE, MAPE, and MAE across the different scenarios proposed.

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Dawan, Promphak, Kobsak Sriprapha, Songkiate Kittisontirak, Terapong Boonraksa, Nitikorn Junhuathon, Wisut Titiroongruang, and Surasak Niemcharoen. "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 (January 10, 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 models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.

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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 (January 23, 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 information accurately and reliably is of primary importance for the appropriate management of an SG and for making decisions relative to the energy market. Several forecasting methods have been proposed, and many indices have been used to quantify the accuracy of the forecasts of PV power production. Unfortunately, the indices that have been used have deficiencies and usually do not directly account for the economic consequences of forecasting errors in the framework of liberalized electricity markets. In this paper, advanced, more accurate indices are proposed that account directly for the economic consequences of forecasting errors. The proposed indices also were compared to the most frequently used indices in order to demonstrate their different, improved capability. The comparisons were based on the results obtained using a forecasting method based on an artificial neural network. This method was chosen because it was deemed to be one of the most promising methods available due to its capability for forecasting PV power. Numerical applications also are presented that considered an actual PV plant to provide evidence of the forecasting performances of all of the indices that were considered.

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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 (September 1, 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 France as well as an over-cost due to the island situation. For some strategies, a forecasting tool was introduced and its influence on the performances of the microgrid was discussed. It appears that the performance of the strategy increased with its complexity and the use of PV forecasting.

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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 (September 2, 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 solar irradiance as an input feature to the model. In addition, a few metrics were selected to determine the quality of the forecasts. The preparation of the dataset for constructing the forecasting models was discussed, and some ways for improving the metrics were given. Furthermore, comparative analyses were performed, which showed that the MLP neural networks used in the regression problem provided better results than the MLP classifier models. The Diebold–Mariano (DM) test was applied in this study to distinguish the significant differences in the forecasting accuracy between the individual models. Compared to KNN (k-nearest neighbors) or ARIMA models, the best results were obtained for the simple linear regression, MLPRegressor, and CatBoostRegressor models in each of the investigated photovoltaic systems. The R-squared value for the MLPRegressor model was around 0.6, and it exceeded 0.8 when the dataset was split and separated into months.

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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 (September 19, 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 process of learning the models. The models were built based on different time periods: year-round, semi-annual, and seasonal. The models were developed separately for monocrystalline and amorphous photovoltaic modules. The study compared the models with the predicted and measured insolation energy. In addition, complex forecasting models were developed for the photovoltaic system, which could forecast DC and AC energy simultaneously. The complex models were developed according to the rules of global and local modeling. The forecast errors of the developed models were included. The smallest values of the DC energy forecast errors were achieved for the models designed for summer forecasts. The percentage forecast error was 1.95% using directly measured solar irradiance and 5. 57% using predicted solar irradiance. The complex model for summer forecasted the AC energy with an error of 1.86%.

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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 dispersion between the actors that we have observed.

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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 (November 25, 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-connected plants (a 243 kW parking lot canopy array in the US and a 9 MW PV system in Algeria) are employed to show the investigated deep learning models’ performance. Specifically, the forecasting outputs of the proposed VAE-based forecasting method have been compared with seven deep learning methods, namely recurrent neural network, Long short-term memory (LSTM), Bidirectional LSTM, Convolutional LSTM network, Gated recurrent units, stacked autoencoder, and restricted Boltzmann machine, and two commonly used machine learning methods, namely logistic regression and support vector regression. The results of this investigation demonstrate the satisfying performance of deep learning techniques to forecast solar power and point out that the VAE consistently performed better than the other methods. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models.

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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 consumption matching at MG. Furthermore, it ensures effective planning, operation, and acquisition from the main grid in the case of superior or inferior amounts of energy, respectively. Therefore, in this work, we develop an end-to-end hybrid network for automatic PV power forecasting, comprising three basic steps. Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. These features are then fed to fully connected layers for optimal PV power forecasting. In the third step, the proposed model is evaluated on publicly available PV power generation datasets, where its performance reveals lower error rates when compared to state-of-the-art methods.

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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 models. The performance of the proposed forecasting model is tested, in a case study, with the time series of hourly mean power production in a PV plant with 1.9 MW capacity. The results show an improvement with respect to the reference probabilistic PV power forecasting models reported in the literature.

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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 (February 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 learning system along with the records’ time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.</p>

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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 (August 5, 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 comprehensive meteorological conditions, including different types of season and weather conditions, were considered in the model, and parameters of LSTM models were investigated simultaneously. Analysis of prediction result reveals that the proposed model leads to a superior prediction performance compared with traditional PV output power predictions. The accuracy of output power prediction is enhanced by 3.46–13.46%.

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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 (May 31, 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 order to improve the accuracy of distributed photovoltaic power generation prediction, this paper proposes a new distributed photovoltaic power generation prediction model: ROLL-GNN, which is defined as a prediction model based on rolling prediction of the graph neural network. The ROLL-GNN uses the perspective of graph signal processing to model distributed generation production timeseries data as signals on graphs. In the model, the similarity of data is used to capture their spatio-temporal dependencies to achieve improved prediction accuracy.

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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 Center Lab). The investigation was performed using a backpropagation ANN. Different learning algorithms were used: Levenberg-Marquardt, Bayesian regularization, resilient backpropagation, and scaled conjugate gradient. Seven atmospheric condition parameters were adopted (humidity, temperature, pressure, wind velocity and direction, solar radiation, and rain), to calculate the receiver temperature as an output. Bayesian regularization was found to be the optimal model for CSP energy production. The results of this investigation suggest that the ANNs are a strong, reliable, and useful tool for predicting temperature in a CSP receiver that can be of great value in the forecasting of energy production. The outcome of this investigation can simplify energy production forecasting using readily available meteorological data.

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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 (January 18, 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 factor to support reliable and cost-effective grid operation and control. In this paper, a stacked long short-term memory network, which is a significant component of the deep recurrent neural network, is considered for the prediction of PV power output for 1.5 h ahead. Historical data of PV power output from a PV plant in Nicosia, Cyprus, were used as input to the forecasting model. Once the model was defined and trained, the model performance was assessed qualitative (by graphical tools) and quantitative (by calculating the Root Mean Square Error (RMSE) and by applying the k-fold cross-validation method). The results showed that our model can predict well, since the RMSE gives a value of 0.11368, whereas when applying the k-fold cross-validation, the mean of the resulting RMSE values is 0.09394 with a standard deviation 0.01616.

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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 (December 23, 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 address this challenge. The paper provides a discussion about building a solar forecasting model, including evaluation measures and machine learning method selection through analysed literature. Given that machine learning is data-driven, the focus of this review has been placed on data sources referenced in the literature. Open-access data sources have been compiled and explored. The main contribution of this paper is the establishment of a benchmark for assessing the performance of solar forecasting models. This benchmark utilizes the mentioned open-source datasets, offering a standardized platform for future research. It serves the crucial purpose of streamlining investigations and facilitating direct comparisons among different forecasting methodologies in the field of solar forecasting.

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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 (July 22, 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 production for a system in Medellín, Colombia. Four forecasting models were generated from techniques compatible with Machine Learning and Artificial Intelligence methods: K-Nearest Neighbors (KNN), Linear Regression (LR), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The results obtained indicate that the four methods produced adequate estimations of photovoltaic energy generation. However, the best estimate according to RMSE and MAE is the ANN forecasting model. The proposed Machine Learning-based models were demonstrated to be practical and effective solutions to forecast PV power generation in Medellin.

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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 (January 19, 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 (September 20, 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 their operation. These threats result, among others, from the fact that photovoltaics are classified as unstable sources and the expected production of electricity from such installations is primarily associated with highly variable weather conditions and is only dependent on people to a minor extent. Currently, there is a rapid development of topics related to forecasting the volume of energy production from unstable sources such as wind and photovoltaic power plants. This issue is being actively developed by research units around the world. Scientists use a whole range of tools and models related to forecasting techniques, from physical models to artificial intelligence. According to our findings, the use of machine learning models has the greatest chance of obtaining positive prognostic effects for small, widely distributed prosumer installations. The present paper presents the research results of two energy balance prediction algorithms based on machine learning models. For forecasting, we proposed two regression models, i.e., regularized LASSO regression and random forests. The work analyzed scenarios taking into account both endogenous and exogenous variables as well as direct multi-step forecasting and recursive multi-step forecasting. The training was carried out on real data obtained from a prosumer micro-installation. Finally, it was found that the best forecasting results are obtained with the use of a random forest model trained using a recursive multi-step method and an exogenous scenario.

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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 (September 9, 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. Specifically, a local weather forecasting algorithm has been used for estimating the daily time evolution of DNI, air Temperature (T), Wind Speed (WS), and Air Mass (AM). These data are subsequently processed by means of an accurate HCPV model for the estimation of one day-ahead daily power production profile. The processing of HCPV forecasted generation by means of a properly tuned filter-based algorithm allows one day-ahead the definition of power profiles of ESS and power plant respectively, considering also the ESS constraints and the characteristic of the implemented real-time control algorithm. The effectiveness of the proposed forecasting model and control algorithm is verified through a simulation study referring to the solar power plant constituted by HCPV and ESS installed in Ottana, Italy. The results highlight that the application of the proposed approach lessens the power fluctuation effect caused by HCPV generation preserving the batteries at the same time. The feasibility and advantages of the proposed approach are finally presented.

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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 (May 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, thereby obtaining more accurate forecasting results than traditional forecasting methods, while utilizing knowledge learned from PV systems with sufficient historical data to assist PV systems with limited historical data in establishing a digital twin of power generation forecasting model, not only can obtain accurate prediction results but also save training time for the model. In this study, the PV historical data of three distinct sites from the open source websites of Queensland University and Shanxi Jinneng Clean Energy Company are used to validate the validity of the suggested technique.

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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 (October 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 solved was the improvement in the accuracy of the existing electricity demand for forecasting models. This was explained through the relationship between the power demand and the number of specific words in the paper that use crawling by utilizing big data. The next problem arose because the current electricity production and supply system stores the amount of new renewable energy by changing the form of energy that is produced through ESS or that is pumped through water power generation without taking the amount of new renewable energy that is generated from sources such as thermal power, nuclear power, and hydropower into consideration. This occurs due to the difficulty of predicting power production using new renewable energy and the absence of a prediction system, which is a problem due to the inefficiency of changing energy types. Therefore, using game theory, the theoretical foundation of a power demand forecasting model based on big data-based renewable energy production forecasting was prepared.

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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 (February 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 NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.

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Lobato-Nostroza, Oscar, Gerardo Marx Chávez-Campos, Antony Morales-Cervantes, Yvo Marcelo Chiaradia-Masselli, Rafael Lara-Hernández, Adriana del Carmen Téllez-Anguiano, and Miguelangel Fraga-Aguilar. "Predictive Modeling of Photovoltaic Panel Power Production through On-Site Environmental and Electrical Measurements Using Artificial Neural Networks." Metrology 3, no. 4 (October 30, 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 lighting, temperature, and humidity in the system’s proximity. The measurements were then subjected to correlation analysis, and various artificial neural networks (ANNs) were implemented to develop energy estimations and forecasting models. The most effective model utilizes lighting, temperature, and humidity. The model achieves a root mean squared error (RMSE) of 0.255326464. The ANN models are compared to an MLR model using the same data. Using previous power measurements and actual weather data, a non-autoregressive neural network (Non-AR-NN) model forecasts future output power values. The best Non-AR-NN model produces an RMSE of 0.1160, resulting in accurate predictions based on the IoT device.

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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 (October 24, 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 precision of solar PV production predictions. Tests were conducted on the suggested model on four separate datasets of solar power in order to provide a full evaluation. To give more information on how the system learns, this study also used the shapely additive explanation framework. Comparing the predicted outcomes allowed for an evaluation of the suggested model's efficacy. of the model to those of individual KNN, RF, and Bagging. The recommended ensemble method offers the most consistency and stability across several case studies and surpasses existing models by 10% to 12% in terms of performance despite weather variations of R2.

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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 (December 29, 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 variances is proposed for training and validation. The experimental results show that using Smart Persistence as a Machine Learning input greatly improves the accuracy of short-term forecasts, achieving an NRMSE of 0.25 on the best panels at short horizons and 0.33 on a 6 h horizon.

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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 (February 2, 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 multimodel approach and referred to several configurations of the Analog Ensemble method, using the weather forecast of four numerical weather prediction models. The very-short-term consists of an Auto-Regressive Integrated Moving Average Model with eXogenous input (ARIMAX) that uses the short-term power forecast and the irradiance from satellite elaborations as exogenous variables. The methods, applied for one year to four small-scale grid-connected plants in Italy, have obtained promising improvements with respect to refence methods. The time horizon after which the short-term was able to outperform the very-short-term has also been analyzed. The study also revealed the usefulness of satellite data on cloudiness to properly interpret the results of the performance analysis.

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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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Journal articles on the topic 'Photovoltaic production forecasting'

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