Forecasting energy production of photovoltaic systems is a hot Research Topic nowadays, when energy prices are one of the highest in history. The forecast of the photovoltaic power production can be on the long term from 1 month to 1 year, on medium-term from 1 week to 1 month, on short-term from 6 h to 72 h, and on very short-term from 15 min to 6 h.
It is crucial to forecast solar radiation and photovoltaic power production so as to control and manage the electrical stand-alone systems, smartgrids and grids, to ensure power demand can be met (). The forecast can be made using the following methods: numerical weather prediction models (NWP), physical techniques, linear statistical models, and machine learning techniques ().
The Research Topic aims to cover some of the forecast problems. It has eight accepted papers, that cover the following Research Topic: methods to maximize the output power of the photovoltaic system, solar radiation forecast, and power generation prediction.
There are several methods to maximize the energy output of the photovoltaic systems. Guo et al. use artificial intelligence to find the maximum power point (MPP) under partial shading conditions. They designed a fuzzy adaptive PSO-based MPP tracker.
The solar radiation is forecasted using several methods. Mohanty et al. used an adaptive neuro-fuzzy inference system, for different locations, which has three input parameters, such as: the duration of the sunshine, temperature, and humidity, and clearness index as the output parameter. Silva et al. used linear modeling ARX and ARMAX to forecast solar radiation with very good accuracy for the Brazilian locations. Belmahdi et al. predicted the daily global solar radiation for 25 different Moroccan cities by using feedforward neural backpropagation network method, FFNN-BP. The input parameters are solar radiation at the top outside atmosphere, clearness index, solar declination, latitude, longitude, altitude, day number, the length of the day, minimum and maximum temperature, mean temperature, temperature difference, ratio temperature, average relative humidity, and average wind speed. For different locations, different combinations of these parameters are used.
Yang et al. use the improved random forest method to predict the power generated by the photovoltaic system in ultra-short-term. This method improves the results for morning and evening prediction in comparison with other methods. Yu et al. propose a new method to predict the power generated by photovoltaic systems, which is a hybridization between the convolutional long short-term memory network model and the adaptive mutation particle swarm optimization. Nunes et al. study the influence of the partial shading on the photovoltaic power generation and introduced the hill climbing neural network algorithm to accurately extract the parameters of the photovoltaic module, for both models single and double diode, that allow a very acurrate prediction of the power generated in partial shading conditions. Using the approximate entropy algoritm the predictibility of the power generated is analysed. Yang et al. proposed a predictability coefficient. It can describe quantitatively the predictability of the power generated by photovoltaic systems.
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Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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References
1
KumarD. S.YagliG. M.KashyapM.SrinivasanD. (2020). Solar irradiance resource and forecasting: A comprehensive review. IET Renew. Power Gener.14 (10), 1641–1656. 10.1049/iet-rpg.2019.1227
2
Ramirez-VergaraJ.BosmanL. B.Leon-SalasW. D.WollegaE. (2021). Ambient temperature and solar irradiance forecasting prediction horizon sensitivity analysis. Mach. Learn. Appl.6, 100128. 10.1016/j.mlwa.2021.100128
Summary
Keywords
forecasting, solar radiation, photovoltaic, parameters, algorithm
Citation
Cotfas DT, Marzband M, Cotfas PA, Siroux M and Sera D (2022) Editorial: Forecasting solar radiation, photovoltaic power and thermal energy production applications. Front. Energy Res. 10:1115096. doi: 10.3389/fenrg.2022.1115096
Received
03 December 2022
Accepted
16 December 2022
Published
26 December 2022
Volume
10 - 2022
Edited and reviewed by
Michael Folsom Toney, University of Colorado Boulder, United States
Updates
Copyright
© 2022 Cotfas, Marzband, Cotfas, Siroux and Sera.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Daniel T. Cotfas, dtcotfas@unitbv.ro
This article was submitted to Solar Energy, a section of the journal Frontiers in Energy Research
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.