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.
Abstract:The building sector consumes about 40% of the world's primary energy. Seasonal climatic conditions have a significant effect on the energy consumption in buildings. One of the famous methods used for decoding this seasonal variation in buildings energy consumption is the "Degree Days Method". Data has been widely published for the heating and cooling degree days of different countries. Unfortunately, there is very limited and outdated published data for the heating and cooling degree-days of Pakistan. In this study, yearly average heating and cooling degree-days for different regions of Pakistan are established by using 30 year long-term measured data for different base temperatures. The data is presented in tables and figures whereas heating and cooling degree-day maps of Pakistan have been developed.
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