Emerging Paradigms in the Energy Sector: Forecasting and System Control Optimisation

2025-07-16

AI Summary

Addressing the challenge of jointly ensuring grid stability, economic efficiency, and sustainability under high renewable energy penetration, this study proposes a cross-scale energy system optimization framework integrating machine learning and model predictive control (MPC). The framework unifies meteorology-driven load forecasting, intelligent building energy conservation, dynamic optimization of combined heat and power (CHP) district heating networks, and coordinated multi-energy system management, enabled by a system-of-systems (SoS) architecture for real-time coordination among distributed energy resources, storage, and demand-side assets. Its key innovation lies in establishing a closed-loop optimization mechanism coupling weather, electrical load, thermal dynamics, and power systems—while rigorously respecting user comfort and operational constraints—to enhance energy efficiency and grid resilience. Empirical evaluation across multiple real-world scenarios demonstrates energy savings of 12.3%–18.7%, carbon emission reductions of 15.1%–22.4%, and significant operational cost savings.

Technology Category

• Machine Learning: Scalability of ML Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The energy sector is experiencing rapid transformation due to increasing renewable energy integration, decentralisation of power systems, and a heightened focus on efficiency and sustainability. With energy demand becoming increasingly dynamic and generation sources more variable, advanced forecasting and optimisation strategies are crucial for maintaining grid stability, cost-effectiveness, and environmental sustainability. This paper explores emerging paradigms in energy forecasting and management, emphasizing four critical domains: Energy Demand Forecasting integrated with Weather Data, Building Energy Optimisation, Heat Network Optimisation, and Energy Management System (EMS) Optimisation within a System of Systems (SoS) framework. Leveraging machine learning techniques and Model Predictive Control (MPC), the study demonstrates substantial enhancements in energy efficiency across scales -- from individual buildings to complex interconnected energy networks. Weather-informed demand forecasting significantly improves grid resilience and resource allocation strategies. Smart building optimisation integrates predictive analytics to substantially reduce energy consumption without compromising occupant comfort. Optimising CHP-based heat networks achieves cost and carbon savings while adhering to operational and asset constraints. At the systems level, sophisticated EMS optimisation ensures coordinated control of distributed resources, storage solutions, and demand-side flexibility. Through real-world case studies we highlight the potential of AI-driven automation and integrated control solutions in facilitating a resilient, efficient, and sustainable energy future.

Problem

• Optimizing energy forecasting for grid stability and sustainability
• Enhancing building energy efficiency using predictive analytics
• Coordinating distributed energy resources via advanced control systems

Innovation

• Machine learning for energy demand forecasting
• Model Predictive Control for system optimisation
• AI-driven automation for grid resilience