An integrated modular architecture for intelligent energy management systems: Forecasting, optimization, and demand response

This paper presents a comprehensive modular architecture for an Intelligent Energy Management System (IEMS). The proposed system is structured into four core modules: (1) Forecasting Module, (2) Optimization Module, (3) Demand Management Module, and (4) Dashboard & Reporting Module. Each module is mathematically formulated and designed to operate both independently and in an integrated manner. The Forecasting Module utilizes deep learning models (CNN-BiLSTM with attention mechanisms) for multi-horizon prediction of renewable generation and consumption, incorporating spatiotemporal correlations to enhance accuracy. The Optimization Module solves a multi-objective unit commitment and economic dispatch problem considering fuel costs, emissions, and grid reliability. The Demand Management Module implements dynamic pricing and incentive-based load control mechanisms, integrating self-time and cross-time elasticity models for more precise demand response. Finally, the Dashboard Module provides real-time visualization, what-if analysis, and automated reporting. Simulation results on real data from Iran’s power grid demonstrate the effectiveness of the modular approach, with particular emphasis on the integration of meteorological data and cross-sector coordination.