Disaggregation of Heating and Cooling Energy Consumption via Maximum a Posteriori Estimation

Estimating energy use in heating and air conditioning systems is crucial for effective building energy management. This article introduces a new method combining the use of degree-days with the maximum a posteriori estimation statistical method to disaggregate heating and cooling energy consumption from other uses. Degree-days provide a reliable measure of the demand for energy needed to heat or cool a building, while a posteriori estimation offers a robust statistical approach to refine these estimates based on available data. A significant challenge addressed by this method is the need to accurately estimate the parameters of the model, which is achieved here by leveraging a comprehensive database. The method's efficacy is demonstrated through a case study of a building with one year of collected data, illustrating its practical application. Our findings underscore the method's potential to enhance energy management practices and guide future research in heating and cooling energy estimation.