Improving sustainable office building operation by using historical data and linear models to predict energy usage. (February 2017) - Safa, Majeed; Safa, Mahdi; Allen, Jeremy; Shahi, Arash; Haas, Carl T.
Highlights: The paper proposes a method that applies multiple linear regression (MLR) and artificial neural network (ANN) models to predict energy usage based on weather conditions and occupancy; thus enabling a comparison of the use of these two types of modelling methods. The models were developed based on the monthly outside temperatures and the number of full-time employees (FTEs). A comparison of the actual and predicted energy consumption revealed that the models can predict energy usage within an acceptable error range. The results also demonstrated that each building should be investigated as an individual unit. Abstract: Offices and retail outlets represent the most intensive energy consumers in the non-residential building sector and have been estimated to account for more than 50% of a building's energy usage. Accurate predictions of office building energy usage can provide potential energy savings and significantly enhance the efficient energy management of office buildings. This paper proposes a method that applies multiple linear regression (MLR) and artificial neural network (ANN) models to predict energy consumption based on weather conditions and occupancy; thus, enabling a comparison of the use of these two types of modelling methods. In this study, four models of office sites at research institutions in different New Zealand regions were developed to investigate the ability of simple models to reduce margins of error in energy auditing projects. The models were developed based on the monthly average outside temperature and the number of full-time employees (FTEs). A comparison of the actual and predicted energy usage revealed that the models can predict energy usage within an acceptable error range. The results also demonstrated that each building should be investigated as an individual unit.