Building Energy Use Prediction Owing to Climate Change: A Case Study of a University Campus

Global warming is expected to increase of 1.5°C between 2030 and 2052. This may lead to an increase in building energy consumption. With the changing climate, cities, communities, and university campuses need a more dynamic forecasting model to predict their future energy demands to mitigate risks. Although many building energy prediction models have been developed, only a few have focused on climate change and its impacts. This paper discusses the development of a regression-based forecasting model for a university campus energy use under changing climate. The forecasting model development follows a four-step process: (1) data frame setting, (2) descriptive statistics, (3) statistical regression modeling, and (4) validation and prediction. The independent numeric variables used as inputs are building characteristics (gross square feet, lighting power density, equipment power density; U factor of roof, wall, and windows; building age; years after building renovation; and window-to-wall ratio) and weather data (temperature and humidity). The outputs are electricity and chilled water for 2054. Prior to modeling, matrix plots and histograms are used to identify correlations between variables. This step is followed by normalization of independent variables to check their impact. Finally, multiple linear regression model for electricity and Lasso regression models for chilled water estimations are developed. For the purpose of predicting energy consumption owing to climate change, we used weather data that represents 2054. The equipment power density was the most important factor for electricity consumption and temperature was the most important one for chilled water consumption. The prediction models give an insight of which factors remain essential and applicable to campus building policy to prevent wasting energy in buildings, as a result of climate change.