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.
The current study presents a numerical and real-time performance analysis of a parabolic trough collector (PTC) system designed for solar air-conditioning applications. Initially, a thermodynamic model of PTC is developed using engineering equation solver (EES) having a capacity of around 3 kW. Then, an experimental PTC system setup is established with a concentration ratio of 9.93 using evacuated tube receivers. The experimental study is conducted under the climate of Taxila, Pakistan in accordance with ASHRAE 93-1986 standard. Furthermore, PTC system is integrated with a solid desiccant dehumidifier (SDD) to study the effect of various operating parameters such as direct solar radiation and inlet fluid temperature and its impact on dehumidification share. The experimental maximum temperature gain is around 5.2°C, with the peak efficiency of 62% on a sunny day. Similarly, maximum thermal energy gain on sunny and cloudy days is 3.07 kW and 2.33 kW, respectively. Afterwards, same comprehensive EES model of PTC with some modifications is used for annual transient analysis in TRNSYS for five different climates of Pakistan. Quetta revealed peak solar insolation of 656 W/m 2 and peak thermal energy 1139 MJ with 46% efficiency. The comparison shows good agreement between simulated and experimental results with root mean square error of around 9%.
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