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Energies 2017, 10(10), 1579; doi:10.3390/en10101579

Energy Consumption Forecasting for University Sector Buildings

1
Department of Mechanical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan
2
Department of Computer System Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan
3
Department of Electrical (Power) Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan
*
Author to whom correspondence should be addressed.
Received: 22 August 2017 / Revised: 29 September 2017 / Accepted: 10 October 2017 / Published: 12 October 2017
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Abstract

Reliable energy forecasting helps managers to prepare future budgets for their buildings. Therefore, a simple, easier, less time consuming and reliable forecasting model which could be used for different types of buildings is desired. In this paper, we have presented a forecasting model based on five years of real data sets for one dependent variable (the daily electricity consumption) and six explanatory variables (ambient temperature, solar radiation, relative humidity, wind speed, weekday index and building type). A single mathematical equation for forecasting daily electricity usage of university buildings has been developed using the Multiple Regression (MR) technique. Data of two such buildings, located at the Southwark Campus of London South Bank University in London, have been used for this study. The predicted test results of MR model are examined and judged against real electricity consumption data of both buildings for year 2011. The results demonstrate that out of six explanatory variables, three variables; surrounding temperature, weekday index and building type have significant influence on buildings energy consumption. The results of this model are associated with a Normalized Root Mean Square Error (NRMSE) of 12% for the administrative building and 13% for the academic building. Finally, some limitations of this study have also been discussed. View Full-Text
Keywords: energy consumption; electricity forecasting; multiple regression; academic; administrative buildings energy consumption; electricity forecasting; multiple regression; academic; administrative buildings
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MDPI and ACS Style

Amber, K.P.; Aslam, M.W.; Mahmood, A.; Kousar, A.; Younis, M.Y.; Akbar, B.; Chaudhary, G.Q.; Hussain, S.K. Energy Consumption Forecasting for University Sector Buildings. Energies 2017, 10, 1579.

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