Modeling and stochastic control for Home Energy Management

The problem of modeling and control for Home Energy Management (HEM) is considered. A first order thermal dynamic model is considered and its parameters are extracted using real measurements over a period of three summer months. The identified model is validated using separate data sets. The extracted model shows certain nonstationarity and non-Gaussianity. However, local approximations using a stationary model are shown to have relatively small modeling and prediction errors. The extracted model is then used for developing a multi-scale multi-stage stochastic optimization framework for the control of the Heating, Ventilation, and Air Conditioning (HVAC) unit, the charging of Plug-in Hybrid Electric Vehicle (PHEV), and the scheduling of deferrable load such as washer/dryer operations. A two time scale Model Predictive Control (MPC) strategy is proposed that minimizes the discomfort level subject to power and budget constraints: at the slow time scale, a power budget is allocated across different appliances at the hourly level; at the fast time scale, sensor measurements are used for the scheduling and control of different loads. Using parameters extracted from the real data, the proposed approach is compared with the simple rule based control strategy typically used in HVAC controllers.