用深度学习提升家庭用电监测与电动车充电效率，助力碳中和
Deep Learning Innovations for Energy Efficiency: Advances in Non-Intrusive Load Monitoring and EV Charging Optimization for a Sustainable Grid
- 用深度学习实现非侵入式电器识别，精准监控家庭能耗
- 通过深度强化学习优化电动车充电时序，降低电网峰值负荷
- 适合能源管理、智能电网与低碳交通研究者参考
全球能源格局正经历深刻转型，即能源转型，其驱动力来自应对气候变化、减少温室气体排放以及保障可持续能源供应的迫切需求。然而，可再生能源投资的复杂性及高碳排放能源的逐步淘汰，阻碍了能源转型的进程，并引发对可再生能源能否独立达成气候目标的质疑。这凸显了探索加速能源转型替代路径的必要性，即识别出能源消耗较高或过高的行为领域。在住宅能源消耗和道路交通这两个关键领域，存在显著的节能空间，可有效减少能源消耗与碳排放。本论文研究开发新型深度学习技术，旨在解决上述两个关键领域的挑战。通过非侵入式负载监测（NILM）技术，帮助用户实时了解家庭用电细节，从而实现节能；同时，采用深度强化学习优化电动汽车（EV）充电策略，提升电网适应性与运行效率，推动道路运输的脱碳进程。
原文摘要 · Abstract (English)
The global energy landscape is undergoing a profound transformation, often referred to as the energy transition, driven by the urgent need to mitigate climate change, reduce greenhouse gas emissions, and ensure sustainable energy supplies. However, the undoubted complexity of new investments in renewables, as well as the phase out of high CO2-emission energy sources, hampers the pace of the energy transition and raises doubts as to whether new renewable energy sources are capable of solely meeting the climate target goals. This highlights the need to investigate alternative pathways to accelerate the energy transition, by identifying human activity domains with higher/excessive energy demands. Two notable examples where there is room for improvement, in the sense of reducing energy consumption and consequently CO2 emissions, are residential energy consumption and road transport. This dissertation investigates the development of novel Deep Learning techniques to create tools which solve limitations in these two key energy domains. Reduction of residential energy consumption can be achieved by empowering end-users with the user of Non-Intrusive Load Monitoring, whereas optimization of EV charging with Deep Reinforcement Learning can tackle road transport decarbonization.
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