Development of an efficiency equation for solar evaporator- collectors

Considering the growing energy needs and concern for environmental degradation, clean and inexhaustible energy sources, e.g solar energy are receiving greater attention for various applications. The use of solar energy systems for low temperature applications reduces the burden on conventional fossil fuels and has little or no harmful effects on the environment. The performance of a solar system depends to a great extent on the collector used for the conversion of solar radiant energy to thermal energy. A solar evaporatorcollector (SEC) is basically an unglazed flat plate collector where refrigerant, like R134a, is used as the working fluid. As the operating temperature of SEC is very low, it collects energy both from solar irradiation and ambient energy leading to a much higher efficiency than the conventional collectors. The capability of SEC to utilize ambient energy also enables the system to operate at night. Therefore it is not appropriate to use for the evaluation of performance of SEC by conventional efficiency equation where ambient energy and condensation is not considered as energy input in addition to irradiation. In the National University of Singapore, several Solar Assisted Heat Pump (SAHP) systems were built for the evaluation of performance under the metrological condition of Singapore for thermal applications of desalination and SEC was the main component to harness renewable energy. In this paper, the design and performance of SEC are explored. Furthermore, an attempt is made to develop an efficiency equation for SEC and maximum efficiency attained 98% under the meteorological condition of Singapore.

The Pandolf load carriage equation is a poor predictor of metabolic rate while wearing explosive ordnance disposal protective clothing

This investigation aimed to quantify metabolic rate when wearing an explosive ordnance disposal (EOD) ensemble (~33kg) during standing and locomotion; and determine whether the Pandolf load carriage equation accurately predicts metabolic rate when wearing an EOD ensemble during standing and locomotion. Ten males completed 8 trials with metabolic rate measured through indirect calorimetry. Walking in EOD at 2.5, 4.0 and 5.5km·h−1 was significantly (p < 0.05) greater than matched trials without the EOD ensemble by 49% (127W), 65% (213W) and 78% (345W), respectively. Mean bias (95% limits of agreement) between predicted and measured metabolism during standing, 2.5, 4 and 5.5km·h−1 were 47W (19 to 75W); −111W (−172 to −49W); −122W (−189 to −54W) and −158W (−245 to −72W), respectively. The Pandolf equation significantly underestimated measured metabolic rate during locomotion. These findings have practical implications for EOD technicians during training and operation and should be considered when developing maximum workload duration models and work-rest schedules.

Forecasting day-ahead electricity load using a multiple equation time series approach

The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models. The key ingredient of the success of this simple model is the effective use of lagged information by allowing for interaction between seasonal patterns and intra-day dependencies. Although the model is built using data for the Queensland region of Australia, the method is completely generic and applicable to any load forecasting problem. The model’s forecasting ability is assessed by means of the mean absolute percentage error (MAPE). For day-ahead forecast, the MAPE returned by the model over a period of 11 years is an impressive 1.36%. The forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the Australia Energy Market Operator (AEMO). The performance of the model developed in this paper is superior to all benchmarks and outperforms the AEMO forecasts by about a third in terms of the MAPE criterion.