Automated Demand Side Management in Buildings

The built environment is responsible for more than a third of total energy consumption and greenhouse gas emissions in both Europe and North America. Electrification of heat and transport, as well as decarbonization through efficiency improvements and distributed energy resources is paving the way for a more sustainable built environment. Today, in many parts of the world, favourable policy regimes and technological advances are further accelerating this transition. However, these rapid changes have also spawned a number of issues, most notably in the form of increased electricity usage, new peak loads, and reverse power flows into the grid. These changes affect not just the distribution grid, but also the transmission grid through increased demand and steep ramp rate requirements due to the intermittency of renewable energy sources. Demand side management algorithms, often powered by the latest advances in artificial intelligence, offer a potential solution to this problem. However, these solutions are marred by data and computational requirements, as well as privacy concerns. Transfer learning has recently been shown to help avoid the requirement of copious amounts of data required to learn a model necessary for optimization. Likewise, federated learning is one potential solution to addressing user privacy concerns by learning from data in a distributed manner. Finally, reinforcement learning can do away with a number of lingering issues in classical model predictive control, especially enabling services which require fast response times such as frequency regulation. These advances cover the entire spectrum of data-driven demand side management offering, which will form the basis for not just more sustainable buildings but also a smarter energy grid in the future.