Regression models for predicting UK office building energy consumption from heating and cooling demands

This paper described the development of regression models which are able to predict office building annual heating, cooling and auxiliary energy requirements for different HVAC systems as a function of office building heating and cooling demands. In order to represent the office building stock, a large number of building parameters were explored such as built forms, fabrics, glazing levels and orientation. Selected parameters were combined into a large set of office building models (3840 in total). As different HVAC systems have different energy requirements when responding to same building demands, each of the 3840 models were further coupled with five HVAC systems: VAV, CAV, fan-coil system with dedicated air (FC), and two chilled ceiling systems with dedicated air, radiator heating and either embedded pipes (EMB) or exposed aluminium panels (ALU). In total 23,040 possible scenarios were created and simulated using EnergyPlus software. The annual heating and cooling demands and their HVAC system's heating, cooling and auxiliary energy requirements were normalised per floor area and fitted to two groups of statistical models. Outputs from the regression analysis were evaluated by inspecting models best fit parameter values and goodness of fit. Based on the described analysis, the specific regression models were recommended.

► Regression models able to predict different HVAC systems energy requirements are developed.
► Regression models are function of office building heating and cooling demands.
► Models were developed from a large pre-calculated dataset composed of 23,040 possible scenarios.
► Statistical analysis proved that energy requirements can be predicted with high accuracy.
► Developed models allow more rapid determination of HVAC systems energy requirements.