Online Learning for Commercial Buildings

There is increasing interest in designing optimization-based techniques for the control of building heating, ventilation, and air-conditioning (HVAC) systems for either improving the energy efficiency of buildings or providing ancillary services to the electric grid. The performance of such prediction-based control techniques relies heavily on models of a building's thermal dynamics. However, the development of high-fidelity building thermal dynamic models is challenging, given the presence of large uncertainties that affect thermal loads in buildings, such as building envelope performance, thermal mass, internal heat gains, and occupant behavior. In this paper, we propose a method to identify both a resistive-capacitive parametric model and nonparametric load uncertainties using measured input-output data. The parametric model is obtained using semi-parametric regression, whereas the nonparametric terms are based on the Random Forest algorithm in which regression trees are used to derive the dependency of nonparametric terms on both building operation parameters and ambient temperature. The effectiveness of the method is evaluated using experimental data collected from an office building at the Pacific Northwest National Laboratory (PNNL) campus. The proposed methodology was observed to provide improved accuracy over appropriate baseline strategies in predicting indoor air temperatures.