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