标题： 能源领域的新兴范式：预测与系统控制优化 显示英文标题

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

摘要： 能源部门由于可再生能源的日益整合、电力系统的去中心化以及对效率和可持续性的更高关注，正在经历快速转型。 随着能源需求变得越来越动态，发电来源也更加多变，先进的预测和优化策略对于保持电网稳定性、成本效益和环境可持续性至关重要。 本文探讨了能源预测和管理中的新兴范式，重点强调四个关键领域：与天气数据集成的能源需求预测，建筑能源优化，热力网络优化，以及系统体系（SoS）框架内的能源管理系统（EMS）优化。 利用机器学习技术和模型预测控制（MPC），该研究展示了在不同规模上能源效率的显著提升——从单个建筑到复杂的互联能源网络。 基于天气的需求预测显著提高了电网的弹性和资源分配策略。 智能建筑优化结合预测分析，大幅降低了能源消耗，同时不影响居住者的舒适度。 优化基于联合循环发电的热力网络实现了成本和碳排放的节约，同时遵守运营和资产限制。 在系统层面，复杂的EMS优化确保了分布式资源、存储解决方案和需求侧灵活性的协调控制。 通过实际案例研究，我们突出了人工智能驱动的自动化和集成控制解决方案在促进弹性、高效和可持续能源未来方面的潜力。 显示英文摘要

摘要： 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.