Energy Consumption Forecasting for University Sector Buildings

Khuram Pervez Amber, Muhammad Waqar Aslam, Anzar Mahmood, Anila Kousar, Muhammad Yamin Younis, Bilal Akbar, Ghulam Qadar Chaudhary and Syed Kashif Hussain
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Khuram Pervez Amber: Department of Mechanical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan
Muhammad Waqar Aslam: Department of Computer System Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan
Anzar Mahmood: Department of Electrical (Power) Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan
Anila Kousar: Department of Electrical (Power) Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan
Muhammad Yamin Younis: Department of Mechanical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan
Bilal Akbar: Department of Mechanical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan
Ghulam Qadar Chaudhary: Department of Mechanical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan
Syed Kashif Hussain: Department of Mechanical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250 (AJK), Pakistan

Energies, 2017, vol. 10, issue 10, 1-18

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.

Keywords: energy consumption; electricity forecasting; multiple regression; academic; administrative buildings (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (26)

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