Energy management issues are interconnected with estimated energy consumption reductions, management of building energy system and decision approaching sustainable development planning. Systematic energy management is important for campus sustainability objectives due to the escalation of energy costs and decreasing budgets for energy management. Student residential colleges are an important measurement variable for university sustainable development goals and should be seen as core contributors to the reduction of energy consumption. The objective of the study is to measure the level of energy consumption and energy-saving awareness among students at residential colleges, Universiti Teknologi Malaysia (UTM), to improve the management facilities and services and at the same time to identify the aspects of building energy efficiency for potential energy saving. Ten student residential colleges in UTM were investigated. Data were gathered through a self-administered survey, and area and facility observations. Statistical Package for the Social Sciences (SPSS) version 16.0 was used for analysis. Findings reveal that building energy consumption reanalysis and demographic overview provide a conceptual framework to understand and manage basic parameters concerning various factors that reflected changing trends of energy consumption over the years. The proposed framework will lead to the adoption of other building energy efficiency and energy-saving practices.
Synthetic hydrological series is useful for evaluating water supply management decision and reservoir design. This paper examines stochastic disaggregation models that are capable of reproducing statistical characteristics especially mean and standard deviation of historical data series. Simulation was carried out on both transformed and untransformed streamflow and rainfall series of Sungai Muar. The Synthetic Streamflow Generation Software Package (SPIGOT) model was found to be the most robust for streamflow simulation. On the other hand, the Valencia-Schaake (VLSH) model is more superior for generating rainfall series.
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