Using artificial neural networks to assess HVAC related energy saving in retrofitted office buildings. (15th March 2018)

• Record Type: Journal Article
• Authors: Deb, Chirag; Lee, Siew Eang; Santamouris, Mattheos
• Is Part Of: Solar energy. Volume 163(2018)
• Page Start: 32
• Page End: 44
• Publication Date: 2018-03-15
• Subjects: Artificial Neural Network (ANN) -- Energy saving -- Building retrofit -- Variable selection; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2018.01.075
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: Pre- and post-retrofit energy consumption data for 56 office buildings is analyzed. These are studied against 14 building and system level variables. Prediction models using MLR and ANN are developed for change in energy consumption from pre- and post-retrofit condition. A robust methodology to identify the appropriate variables is presented. Abstract: This study aims to develop prediction models for HVAC related energy saving in office buildings. The data-driven modelling makes use of data gathered from several energy audit reports. These reports entail building and energy consumption data for 56 office buildings in Singapore. The two models are developed using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). The methodology to select the most appropriate input variables forms the essence of this study. This variable selection procedure involves 819, 150 iterations, taking all possible combinations of the 14 input variables to determine the most accurate model. The dependent variable is taken as the change in energy use intensity (EUI, measured in kWh/m 2 .year) between pre- and post-retrofit conditions. The results show that the ANN model is more accurate with a mean absolute percentage error (MAPE) of 14.8%. The best combination of variables to achieve this comprises of gross floor area (GFA), air-conditioning energy consumption, operational hours and chiller plant efficiency. The information on these four variables, along with the prediction model can be used to predict HVAC related energy savings in office buildings to be retrofitted.