A Bayesian Approach to Maximise Photovoltaic System Output

Abstract

This thesis addresses the critical issue of optimising photovoltaic (PV) system output, an essential objective in the pursuit of efficient and scalable renewable energy solutions. As global energy demands rise and concerns over climate change intensify, solar power has emerged as a leading solution for sustainable electricity generation. However, the performance of PV systems is highly sensitive to environmental factors which can vary significantly across seasons and geographical locations. These fluctuations create a complex optimisation problem in determining the most effective system configuration that can dynamically adapt to seasonal and regional variations in solar potential. Traditional approaches often rely on fixed or rule-based models that do not adequately account for these variations, leading to suboptimal energy yields and the inefficient use of solar infrastructure. In this research, a Bayesian Network model is developed to learn the conditional dependencies between meteorological variables (such as solar irradiance, temperature, and wind speed) and PV system configuration parameters (tilt angle, orientation, inverter properties). By using a Bayesian approach, the developed model accommodates uncertainty and dynamically adjusts PV system tilt configurations to weather variations, aiming to maximise PV output. Score-based methods are employed to construct the network structure, and Maximum Likelihood Estimation (MLE) to determine the Conditional Probability Distributions (CPDs) of the network. Additionally, Maximum a Posteriori (MAP) estimation is applied to identify the optimal seasonal PV system tilt configurations in light of specific weather conditions. Key findings demonstrate the effectiveness of the model in optimising PV output by offering adaptive configuration strategies that respond to local seasonal meteorological patterns. This includes the superior performance of the Hill Climb Search algorithm compared to Simulated Annealing for structure learning, the utilisation of MAP Estimation for identifying optimal PV system tilt configurations under varying meteorological conditions, and the statistically significant advantage of dynamic configurations over fixed installations for enhancing PV system output. These results underscore the potential of Bayesian approaches for data-driven optimisation in renewable energy systems. This research provides a robust framework that enhances PV system performance and contributes to the growing body of knowledge on renewable energy optimisation through probabilistic modelling. Ultimately, this research presents a novel, data-driven methodology which informs the design and operation of more efficient PV systems, answering both the “so what” and “now what” in the context of sustainable energy advancements.