Short-Term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning

Alen Jakoplić, Dubravko Franković (), Juraj Havelka and Hrvoje Bulat
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Alen Jakoplić: Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Dubravko Franković: Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Juraj Havelka: Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia
Hrvoje Bulat: Croatian Transmission System Operator Ltd., Kupska 4, 10000 Zagreb, Croatia

Energies, 2023, vol. 16, issue 14, 1-18

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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