Forecasting Energy Availability in Local Energy Communities via LSTM Federated Learning

2026-01-31

AI Summary

This study addresses the challenge of achieving accurate energy availability forecasting in local energy communities, where privacy concerns often restrict access to individual electricity consumption data, thereby hindering the deployment of high-precision predictive models. To overcome this limitation, the work proposes a novel decentralized forecasting framework that integrates Long Short-Term Memory (LSTM) networks with federated learning, enabling collaborative model training without sharing raw user data. Experimental results demonstrate that the proposed approach effectively preserves user privacy while still delivering prediction accuracy sufficient for practical applications. This method offers a viable and innovative technical pathway for intelligent energy management in privacy-sensitive environments.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Local Search
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Privacy-enhancing technologies

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.

Problem

• Local Energy Communities
• Energy Forecasting
• Privacy Constraints
• Self-sufficiency
• Data Sharing

Innovation

• Federated Learning
• LSTM
• Energy Forecasting
• Privacy Preservation
• Local Energy Communities