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Factors affecting mini hydro power production efficiency: A case study in Malaysia

机译:影响小型水力发电效率的因素:以马来西亚为例

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Energy consumption is expected to increase by 50 per cent between 2005 and 2030. Therefore, Malaysian National Renewable Energy Policy and Action Plan is introduced in 2010 to increase the use of renewable energy due to the growing concerns about the pollution from energy sources that come from fossil fuels such as oil, coal and natural gas. In this paper, our focus is more on mini hydropower since it is the most cost effective energy technologies to be developed for rural area. Mini hydropower depends a lot on weather conditions, such as rainfall, temperature, humidity and others besides the system itself. To study how these factors affecting the power generation by mini hydro power plant in east coast region, data sets are collected from Meteorological Department Malaysia from January 2010 until December 2015. A statistical analysis is conducted to see the correlation of these factors with the power generation by mini hydro power. From the analysis, it can be concluded that humidity and rainfall have significant effect on power generation by mini hydro. These two variables can be used to predict energy production by mini hydro power. For future research, machine learning method such as support vector machine, artificial neural network and others can be used to predict the energy production by mini hydro power plant.
机译:预计从2005年到2030年,能源消耗将增加50%。因此,由于人们越来越担心来自能源的污染,马来西亚于2010年推出了《马来西亚国家可再生能源政策和行动计划》,以增加可再生能源的使用。化石燃料,例如石油,煤炭和天然气。在本文中,我们将重点更多地放在微型水力发电上,因为它是针对农村地区开发的最具成本效益的能源技术。微型水力发电在很大程度上取决于天气状况,例如降雨,温度,湿度以及系统本身以外的其他状况。为了研究这些因素如何影响东海岸地区小型水力发电厂的发电,2010年1月至2015年12月从马来西亚气象局收集了数据集。进行了统计分析,以查看这些因素与发电量的相关性。通过小型水力发电。从分析中可以得出结论,湿度和降雨对微型水力发电有重大影响。这两个变量可用于预测微型水力发电量。为了将来的研究,可以使用支持向量机,人工神经网络等机器学习方法来预测小型水力发电厂的发电量。

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