Energy-related environmental and economic performance analysis of two different types of electrically heated student residence halls

Amber, Khuram Pervez; Aslam, Muhammad Waqar

doi:10.1080/14786451.2016.1174703

Cited by 2 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of studies [7,[27][28][29][30][31][32][33][34][35] have investigated the effect of weather changes on building energy consumption. Amber, et al [6] investigated the effect of four weather variables, i.e., surrounding temperature, global irradiance, humidity and wind velocity on the electricity usage of different buildings.…”

Section: Weather Datamentioning

confidence: 99%

Energy Consumption Forecasting for University Sector Buildings

et al. 2017

Self Cite

View full text Add to dashboard Cite

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.

show abstract