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

TL;DR

This paper reviews emerging paradigms in energy forecasting and system control, emphasizing machine learning and predictive control to enhance efficiency, sustainability, and resilience across various energy systems.

Contribution

It introduces integrated forecasting and optimisation strategies leveraging AI and MPC, demonstrating significant improvements in energy management at multiple scales.

Findings

Weather-informed demand forecasting enhances grid resilience.

Predictive analytics reduce building energy consumption.

Optimised heat networks lower costs and emissions.

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.