Electricity consumption forecasting models for administration buildings of the UK higher education sector

• Forecasting of daily electricity consumption in the administration buildings.
• Selection of important variables which affect the electricity consumption of buildings.
• Development of multiple regression (MR) model for electricity consumption forecasting.
• Development of genetic programming (GP) model for electricity consumption forecasting.
• Performance comparison and analysis of MR and GP models.

Electricity consumption in the administration buildings of a typical higher education campus in the UK accounts for 26% of the campus annual electricity consumption. A reliable forecast of electricity consumption helps energy managers in numerous ways such as in preparing future energy budgets and setting up energy consumption targets. In this paper, we developed two models, a multiple regression (MR) model and a genetic programming (GP) model to forecast daily electricity consumption of an administration building located at the Southwark campus of London South Bank University in London. Both models integrate five important independent variables, i.e. ambient temperature, solar radiation, relative humidity, wind speed and weekday index. Daily values of these variables were collected from year 2007 to year 2013. The data sets from year 2007 to 2012 are used for training the models while 2013 data set is used for testing the models. The predicted test results for both the models are analyzed and compared with actual electricity consumption. At the end, some conclusions are drawn about the performance of both models regarding their forecasting capabilities. The results demonstrate that the GP model performs better with a Total Absolute Error (TAE) of 6% compared to TAE of 7% for MR model.