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Domestic Building Energy Prediction in Design Stage Utilizing Large-Scale Consumption Data from Realized Projects

This paper suggests the use of consumption data of realized apartment buildings for estimation of energy use in future projects. This is relevant in the case of project developers that want to estimate average consumption of projects under development. A major unknown in the design prediction is the role of occupants in the energy use of the apartments. Previous research has shown that the variability of occupancy in terms of schedule, set points and appliance use is very large. We propose to capture this variability from an analysis of the consumption data of similar apartments. The prediction method uses a simple yet accurate building energy model in which the occupancy variability is added based on the observed variability in the monitored buildings. The cumulative effects of schedule, household composition, and issues of occupant behavior such as the tendency to rely on natural ventilation versus the use of the mechanical cooling systems, are rolled up into one factor, referred to as the lifestyle factor. This factor is a stochastic variable representing the variability of lifestyle of households for a given demographics. The hypothesis of our approach is that by using the lifestyle factor as the input parameter in the apartment energy prediction model we capture all aspects of occupancy variability in an apartment complex. Cross-validation results show that the proposed method accurately estimates the full range of building energy outcomes for a proposed apartment design.