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.

A new micro heat exchanger was analyzed using numerical formulation of conjugate heat transfer for single-phase fluid flow across copper microchannels. The flow across bent channels harnesses asymmetric laminar flow and dean vortices phenomena for heat transfer enhancement. The single-channel analysis was performed to select the bent channel aspect ratio by varying width and height between 35–300 μm for Reynolds number and base temperature magnitude range of 100–1000 and 320–370 K, respectively. The bent channel results demonstrate dean vortices phenomenon at the bend for Reynolds number of 500 and above. Thermal performance factor analysis shows an increase of 18% in comparison to straight channels of 200 μm width and height. Alumina nanoparticles at 1% and 3% concentration enhance the Nusselt number by an average of 10.4% and 23.7%, respectively, whereas zirconia enhances Nusselt number by 16% and 33.9% for same concentrations. On the other hand, thermal performance factor analysis shows a significant increase in pressure drop at high Reynolds number with 3% particle concentration. Using zirconia for nanofluid, Nusselt number of the bent multi-channel model is improved by an average of 18% for a 3% particle concentration as compared to bent channel with deionized water.

Vertical axis wind turbines (VAWT) are a source of renewable energy and are used for both industrial and domestic purposes. The study of noise characteristics of a VAWT is an important performance parameter for the turbine. This study focuses on the development of a linear microphone array and measuring acoustic signals on a cambered five-bladed 45 W VAWT in an anechoic chamber at different tip speed ratios. The sound pressure level spectrum of VAWT shows that tonal noises such as blade passing frequencies dominate at lower frequencies whereas broadband noise corresponds to all audible ranges of frequencies. This study shows that the major portion of noise from the source is dominated by aerodynamic noises generated due to vortex generation and trailing edge serrations. The research also predicts that dynamic stall is evident in the lower Tip speed ratio (TSR) region making smaller TSR values unsuitable for a quiet VAWT. This paper compares the results of linear aeroacoustic array with a 128-MEMS acoustic camera with higher resolution. The study depicts a 3 dB margin between two systems at lower TSR values. The research approves the usage of the 8 mic linear array for small radius rotary machinery considering the results comparison with a NORSONIC camera and its resolution. These observations serve as a basis for noise reduction and blade optimization techniques.