Forecasting Energy Availability in Local Energy Communities via LSTM Federated Learning

TL;DR

This paper presents a federated learning approach using LSTM networks to accurately forecast energy availability in local energy communities while preserving user privacy.

Contribution

It introduces a novel combination of federated learning and LSTM for energy forecasting in local communities, addressing privacy concerns.

Findings

Federated LSTM models achieve competitive accuracy without sharing sensitive data.

Trade-offs between data sharing and forecasting precision are analyzed.

The approach supports privacy-preserving energy management in local communities.

Abstract

Local Energy Communities are emerging as crucial players in the landscape of sustainable development. A significant challenge for these communities is achieving self-sufficiency through effective management of the balance between energy production and consumption. To meet this challenge, it is essential to develop and implement forecasting models that deliver accurate predictions, which can then be utilized by optimization and planning algorithms. However, the application of forecasting solutions is often hindered by privacy constrains and regulations as the users participating in the Local Energy Community can be (rightfully) reluctant sharing their consumption patterns with others. In this context, the use of Federated Learning (FL) can be a viable solution as it allows to create a forecasting model without the need to share privacy sensitive information among the users. In this study, we demonstrate how FL and long short-term memory (LSTM) networks can be employed to achieve this objective, highlighting the trade-off between data sharing and forecasting accuracy.