Short-Term Forecasting of Energy Consumption and Production in Local Energy Communities

Natalia Selini Hadjidimitriou, Marco Mamei, Marco Lippi, Raffaele Nastro, Thorsten Koch

Abstract: Local Energy Communities are becoming key actors in the panorama of sustainable development. One of the biggest challenges for such communities is to become self-efficient, thanks to an efficient management of the balancing between produced and consumed energy. In order to achieve this goal, it is necessary to design and implement forecasting models that can provide accurate estimates to be subsequently used by optimization and planning algorithms. In this work, we show how neural networks, and in particular long short-term memory networks, can be used to this aim, highlighting an interesting trade-off between the computational requirements and the forecasting accuracy induced by learning different models for clusters of users.