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.