Intelligent Building Energy Management Considering Uncertainties and Variation of Day-Ahead and Real-Time Electricity Prices

This article proposes an intelligent building energy management (BEM) strategy based on two different time-scale neural networks (NNs) to minimize building electricity expense and maximize building user comfort considering load demand uncertainties and variation in day-ahead and real-time electricity prices. First, in terms of the day-ahead BEM, an intelligent and optimization problem on an hourly time scale is formulated to generate a day-ahead baseline BEM solution. Then, in terms of the real-time BEM, another intelligent and optimization problem in a 5-min time scale is proposed to balance the real-time deviations from the energy demand scheduled one day ahead based on the real-time electricity price. In this article, the NN models are developed to handle the energy management challenge of highly nonlinear building heating, ventilation, and air conditioning (HVAC) systems while physics-based models are developed for building non-HVAC loads. A joint particle swarm optimization and mixed-integer linear programming technique is developed to generate optimal scheduling for building HVAC and non-HVAC loads, in which the thermal coupling issues between HVAC and non-HVAC loads are solved through an iterative approach. Finally, the proposed strategy is evaluated for several different scenarios by considering uncertainties of customers’ comfort requirements and weather prediction. The results demonstrate that the proposed intelligent BEM is economical, efficient, and robust to address the challenge of demand uncertainty and day-ahead and real-time electricity price inconsistency.