Citation: Gumar, A.K.; Demir, F. Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks. Energies 2022, 15, 8669. https://doi.org/10.3390/en15228669 Academic Editors: Manolis Souliotis and Abu-Siada Ahmed Received: 18 October 2022 Accepted: 11 November 2022 Published: 18 November 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Article Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks Ali Kamil Gumar * and Funda Demir Department of Mechatronics Engineering, Faculty of Engineering, Karabuk University, 78050 Karabuk, Turkey * Correspondence: alikamilgumar@gmail.com Abstract: Solar photovoltaic technology is spreading extremely rapidly and is becoming an aiding tool in grid networks. The power of solar photovoltaics is not static all the time; it changes due to many variables. This paper presents a full implementation and comparison between three optimization methods—genetic algorithm, particle swarm optimization, and artificial bee colony—to optimize artificial neural network weights for predicting solar power. The built artificial neural network was used to predict photovoltaic power depending on the measured features. The data were collected and stored as structured data (Excel file). The results from using the three methods have shown that the optimization is very effective. The results showed that particle swarm optimization outperformed the genetic algorithm and artificial bee colony. Keywords: artificial neural network (ANN); artificial bee colony (ABC); genetic algorithm (GA); particle swarm optimization (PSO); solar photovoltaic (PV) 1. Introduction The progress and development of any city depends on the production, use, and safe storage of energy. Energy dependency is increasing more with the advancement of automation techniques and growth in industrial areas. The main source of electricity is fossil fuels, which are directly detrimental to the climate [1]. The use of fossil-based fuels in highly demanding energy production by the population causes many problems, such as global warming, which leads to undesired situations such as rising sea levels, drought in agricultural areas, changes in the global climate, and the scarcity of energy resources [2]. Previously, economists and environmentalists have supported climate agreements and adopted cleaner energy sources, which are greener, cheaper, and more efficient, to meet the growing global energy demand. Clean energy, which includes renewable energy, especially solar photovoltaic (PV) and wind energy, is a free energy fuel for the global energy market. PV systems convert solar energy into ready-to-use electrical energy, which is the reason for the great interest in them recently. Solar PV energy has become the most prominent concern globally because it represents a free fuel source for the global energy market. It is one of the solutions for avoiding the risks of climate change and global warming and to serve consumers [3]. It has been determined that the amount of solar energy available to the planet is larger than available oil and coal reserves [4]. Governments must support investments and provide the necessary incentives to improve PV usage and reduce any negative effects on the solar energy sector. Governments have also been called on to recognize that solar energy outside the system is an essential service [5]. Expectations indicate that electricity produced from solar energy will become cheaper than other energy sources by 2025 [6]. PV has some undesired points; PV energy exhibits great randomness due to weather conditions, which is mainly affecting the production of PV energy when it is extensively connected with electrical supplies and distribution networks. Therefore, accurate and reliable solar PV forecasting is a very important aspect of the safe and economical operation Energies 2022, 15, 8669. https://doi.org/10.3390/en15228669 https://www.mdpi.com/journal/energies