Citation: Jakopli´ c, A.; Frankovi´ c, D.; Havelka, J.; Bulat, H. Short-Term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning. Energies 2023, 16, 5428. https://doi.org/10.3390/ en16145428 Academic Editor: Guojiang Xiong Received: 15 June 2023 Revised: 7 July 2023 Accepted: 13 July 2023 Published: 17 July 2023 Copyright: © 2023 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 Short-Term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning Alen Jakopli´ c 1 , Dubravko Frankovi´ c 1, * , Juraj Havelka 2 and Hrvoje Bulat 3 1 Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; alen.jakoplic@riteh.hr 2 Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia; juraj.havelka@fer.hr 3 Croatian Transmission System Operator Ltd., Kupska 4, 10000 Zagreb, Croatia; hrvoje.bulat@hops.hr * Correspondence: dubravko.frankovic@riteh.hr Abstract: 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. Keywords: renewable energy; short-term forecasting; photovoltaic power plants; deep learning; sky image analysis 1. Introduction Photovoltaic (PV) power plants are among the most widely used types of renewable energy power plants [1]. Mainly because of their minimal impact on the environment, recent research has focused on improving solar cells in terms of efficiency, manufacturing cost, and durability. As a result, the presence of PV power plants in the power grid is increasing [2]. The unpredictable nature of power generation from renewable energy sources, in- cluding solar energy, results in voltage and frequency fluctuations within the power grid, posing challenges for system management and control [3]. Changes in the availability of renewable energy sources can occur rapidly, leaving conventional power plants with insufficient time to adjust their output. The delayed response of these conventional energy sources disrupts the balance between electrical energy generated and consumer demand. During periods when this balance is not achieved, voltage and frequency may deviate from their nominal values, resulting in a degradation of electrical power quality [4,5]. To mitigate the negative impact of renewable energy sources on the power system, it is critical to predict changes in the availability of these energy sources with reasonable accuracy. The highly dynamic nature of weather conditions makes accurate long-term Energies 2023, 16, 5428. https://doi.org/10.3390/en16145428 https://www.mdpi.com/journal/energies