Adaptive Load Forecasting in Smart Grids Using Deep Learning and Edge Computing

Abstract

With the development of smart grid infrastructures to accommodate distributed energy resources, electric vehicles, and dynamic demand-side operations, responsive and precise load forecasting becomes more essential than ever. The problems associated with remote servers and constant flow of data lead to latency, scalability and privacy issues experienced by traditional centralized forecasting models. This paper presents an adaptive load forecasting system based on deep learning (long short-term memory (LSTM) networks) dusted to edge computing devices. The system uses real-time data, provided by smart meters or environmental sensors to provide localized, on-device predictions with minimal network dependency. Real-world data experiments reveal that the Edge-LSTM model has a better predictive performance, as well as much less inference latency, and greater adaptation ability than centralized LSTM, ARIMA, and feedforward neural networks. The findings also indicate a better utilization of network bottlenecked conditions and energy efficiency in resilience conditions which demonstrates the scalability of deep learning making use of edge intelligence to ensure decentralized operations in terms of smart grid. It is potentially an advance in the direction of real-time, privacy-preserving, and energy-conscious forecasts in future power networks.

Keywords- Smart grid, load forecasting, edge computing, deep learning, LSTM, real-time prediction, adaptive models, decentralized energy systems, energy efficiency, intelligent grid.