Bayesian energy consumption prediction model of campus buildings based on energy consumption monitoring data

For campus buildings with numerous types and complex energy use rules, establishing a regional energy consumption model is an efficient way to analyse their energy use. The bottom-up regional energy consumption model can not only predict the building energy consumption in the region, but also evaluate the energy conservation of new technology and new energy resources. At present, its modeling method is generally based on area expansion method. Proposes a Bayesian energy consumption prediction method based on energy consumption monitoring data. Establishes a simple area expansion prediction model and a Bayesian energy consumption prediction model for a campus building complex in cold zone. Taking the mixed scientific research buildings as an example, the monthly error range of the area expansion model is between -30% and -7%, while that of the Bayesian model is between -5% and 15%, and the absolute value of the monthly error is significantly reduced. The monthly error range of the Bayesian energy consumption prediction model for the total energy consumption of the campus building complex is between -2% and 6%, and the annual error is only 1.07%.