Forecasting photovoltaic production with neural networks and weather features

In this paper, we address the refinement of solar energy forecasting within a 2-day window by integrating weather forecast data and strategically employing entity embedding, with a specific focus on the Multilayer Perceptron (MLP) algorithm. Through the analysis of two years of hourly solar energy production data from 16 power plants in Northern Italy (2020-2021), our research underscores the substantial impact of weather variables on solar energy production. Notably, we explore the augmentation of forecasting models by incorporating entity embedding, with a particular emphasis on embedding techniques for both general weather descriptors and individual power plants. By highlighting the nuanced integration of entity embedding within the MLP algorithm, our study reveals a significant enhancement in forecasting accuracy compared to popular machine learning algorithms like XGBoost and LGBM, showcasing the potential of this approach for more precise solar energy forecasts.