Data-driven predictive models for daily electricity consumption of academic buildings

Abstract

Academic buildings in a typical university campus occupy 42% of the total space and are responsible for nearly 50 percent of the total energy use and carbon emissions of the campus. Forecasting of energy consumption in this energy intensive building category could help higher education institutions in taking energy saving initiatives and in revising their building operating strategies. Reliable predictive techniques does not only help in forecasting a building’ energy consumption, but also help in identifying a variety of factors affecting the energy consumption of that building. This study attempts to forecast and benchmark the daily electricity consumption of an academic building situated in London, United Kingdom using two different data-driven modeling techniques, i.e., Multiple Regression and Artificial Neural Network. Hourly dataset for the electricity consumption was available for the period 2007 to 2011 from the smart meter whereas hourly data of different factors such as ambient temperature, relative humidity, wind speed and solar radiation were downloaded from the website of environmental research group of Kings College London. The performances of the two predictive models have been critically analyzed by comparing their predicted consumption with a real dataset of the same building for the year 2012. A comparison shows that both Multiple Regression (MR) and Artificial Neural Network (ANN) perform reasonably well with a Mean Absolute Percentage Error (MAPE) of 3.34% and 2.44% for working days and 5.12% and 4.59% for non-working days respectively. ANN performs slightly better than MR. This energy consumption forecasting approach can easily be adapted for predicting energy use of similar buildings.