Predictive Modelling of Renewable Energy Sources: A Comprehensive Analysis Using Gradient Boosted Decision Trees

This paper presents an investigation into the predictive modelling of renewable energy sources, with a specific focus on solar energy. The study employs a versatile machine learning model to forecast future trends and values of times series data. The imperative for accurate predictions stems from the dynamic and intermittent nature of solar energy, necessitating reliable forecasting for effective energy management and grid integration. The methodology used within this study involves leveraging various Python software libraries for the model creation and forecasting process. The time series data used herein is sourced from the publicly available Photovoltaic Geographical Information System. Key findings indicate gradient-boosted decision tree (GBDT) models, such as XGBoost or LightGBM, are incredibly efficient at processing large quantities of data, can robustly handle outlier data and can train models and produce accurate forecasts with relatively little configuration required. The insights derived from this study hold significant relevance for policymakers, energy planners, industry stakeholders, and home-scale renewable energy system users seeking to navigate the transition towards a more sustainable and resilient energy infrastructure.