Citation: Islam, M.S.S.; Ghosh, P.; Faruque, M.O.; Islam, M.R.; Hossain, M.A.; Alam, M.S.; Islam Sheikh, M.R. Optimizing Short-Term Photovoltaic Power Forecasting: A Novel Approach with Gaussian Process Regression and Bayesian Hyperparameter Tuning. Processes 2024, 12, 546. https:// doi.org/10.3390/pr12030546 Academic Editors: Mohan Lal Kolhe and Peng Li Received: 23 January 2024 Revised: 24 February 2024 Accepted: 4 March 2024 Published: 11 March 2024 Copyright: © 2024 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/). processes Article Optimizing Short-Term Photovoltaic Power Forecasting: A Novel Approach with Gaussian Process Regression and Bayesian Hyperparameter Tuning Md. Samin Safayat Islam 1 , Puja Ghosh 1 , Md. Omer Faruque 2 , Md. Rashidul Islam 1 , Md. Alamgir Hossain 3, * , Md. Shafiul Alam 4 and Md. Rafiqul Islam Sheikh 1 1 Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh; samintayafas@gmail.com (M.S.S.I.); pujaghosheee17@gmail.com (P.G.); rashidul@eee.ruet.ac.bd (M.R.I.); mri.sheikh@eee.ruet.ac.bd (M.R.I.S.) 2 Department of Electrical & Electronic Engineering, Dhaka University of Engineering & Technology, Gazipur 1707, Bangladesh; omerfaruque1501111@gmail.com 3 Queensland Micro- and Nanotechnology Centre, Griffith University, Nathan, QLD 4111, Australia 4 Applied Research Center for Environment & Marine Studies, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia; mdshafiul.alam@kfupm.edu.sa * Correspondence: mdalamgir.hossain@griffith.edu.au Abstract: The inherent volatility of PV power introduces unpredictability to the power system, necessitating accurate forecasting of power generation. In this study, a machine learning (ML) model based on Gaussian process regression (GPR) for short-term PV power output forecasting is proposed. With its benefits in handling nonlinear relationships, estimating uncertainty, and generating probabilistic forecasts, GPR is an appropriate approach for addressing the problems caused by PV power generation’s irregularity. Additionally, Bayesian optimization to identify optimal hyper- parameter combinations for the ML model is utilized. The research leverages solar radiation intensity data collected at 60-min and 30-min intervals over periods of 1 year and 6 months, respectively. Comparative analysis reveals that the data set with 60-min intervals performs slightly better than the 30-min intervals data set. The proposed GPR model, coupled with Bayesian optimization, demonstrates superior performance compared to contemporary ML models and traditional neural network models. This superiority is evident in 98% and 90% improvements in root mean square errors compared to feed-forward neural network and artificial neural network models, respectively. This research contributes to advancing accurate and efficient forecasting methods for PV power output, thereby enhancing the reliability and stability of power systems. Keywords: Bayesian optimization; PV power forecasting; Gaussian process regression; machine learning; solar radiation intensity 1. Introduction The global demand for energy has been steadily increasing over the last few decades, leading to substantial growth in electricity production worldwide [1]. Traditionally, con- ventional fossil fuels have served as the primary energy source for electricity generation. However, the sustainability of these traditional fossil fuel resources is now uncertain due to their rapid depletion [2]. Moreover, the combustion of fossil fuels is a major contributor to greenhouse gas emissions, exacerbating severe global warming and posing a threat to the planet’s health [1,3]. In response to these challenges, there has been a significant shift in focus from fossil fuels to renewable energy sources (RESs) for electricity generation. This transition involves harnessing solar energy, wind energy, tidal energy, and biomass energy [4,5]. In the context of growing energy development, the incorporation of renewable energy sources (RES) into contemporary power systems is increasingly reliant on hydrogen. Among the various Processes 2024, 12, 546. https://doi.org/10.3390/pr12030546 https://www.mdpi.com/journal/processes