Energy optimization is crucial for environmental sustainability, as it reduces resource consumption, minimizes greenhouse gas emissions, and promotes the use of renewable energy. Efficient energy use helps combat climate change and preserves natural ecosystems for future generations. In this paper, a system to support the distribution of photovoltaic energy for Emilia Romagna Energy Communities is proposed. The system will manage and integrate large amounts of data and offer innovative services based on them for calculating climate and energy forecasts. To enable more reliable production estimates and efficient energy storage and distribution, the system will use a platform for managing and integrating data from Regional Climate Models. It will incorporate Machine Learning and Deep Learning models for accurate climate forecasts and optimize energy flows by considering consumption profiles, production forecasts, and storage characteristics. The application background, the proposed methodology, and the current challenges related to the domain will be discussed, with a particular focus on data sources and management operations.

This paper introduces a novel deep learning approach for predicting global solar radiation and temperature. We propose an architecture based on Gated Recurrent Unit (GRU) neural networks able to refine weather predictions returned by the MM5 Regional Climate Model. Measured values from a weather station and outputs from the MM5 system are used to train and validate the model. The forecasting capability is assessed for three-day estimations. The results demonstrate that the model, by correcting MM5’s periodic tendencies to underestimate or overestimate the outputs, leads to a more accurate forecasting of the weather variables.These more precise predictions are then adopted for calculating the electrical energy production of a photovoltaic cell. Also in this case, the proposed model allows better results than the traditional MM5 system, enabling adaptive adjustments in intelligent energy systems.