A Compressive Receding Horizon Approach for Smart Home Energy Management

Current increases in the demand for electricity require sustainable energy management measures and have promoted the adoption of clean and renewable sources, particularly at the residential building level. Active demand management is usually carried out through load shifting based on specific techniques, such as optimisation, heuristics, model-based predictive control and machine learning methodologies. This work addresses the problem of residential load scheduling via optimisation techniques. A compressive receding horizon strategy is proposed for week-ahead load shifting, and the selection is driven by traditional receding horizon and day-ahead allocation strategy misalignment, with weekly household appliance usage patterns. The proposed approach is compared with receding horizon and day-ahead scheduling techniques over 30 different weeks for a prototypical smart home with non-controllable demand, which is representative of a four-resident family and includes micro power generation and battery storage. The simulation results confirm the validity of the proposed strategy in the context of household appliance scheduling problems and show competitive electricity costs and resident discomfort performance compared to state-of-the-art approaches. Furthermore, the proposed compressive receding horizon strategy fully exploits weather and photovoltaic generation forecasts to promote self-consumption and grid demand stress reduction while providing environmental gains and financial benefits to the utility service and consumers, particularly in the case of simultaneously scheduling a huge number of households.