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

M. K. A. Hamid, N. Ramli, Siti Nor Baizura Mat Napiah
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引用次数: 2

Abstract

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.
影响小型水力发电效率的因素:以马来西亚为例
在2005年至2030年期间,能源消耗预计将增加50%。因此,马来西亚国家可再生能源政策和行动计划于2010年推出,以增加可再生能源的使用,因为人们越来越担心来自石油、煤炭和天然气等化石燃料的能源污染。在本文中,我们的重点是小水电,因为它是农村地区最具成本效益的能源技术。小型水力发电在很大程度上取决于天气条件,如降雨、温度、湿度和系统本身以外的其他因素。为了研究这些因素对东部沿海地区小型水力发电厂发电的影响,我们收集了马来西亚气象局2010年1月至2015年12月的数据集。统计分析了这些因素与微型水力发电的相关性。通过分析可知,湿度和降雨量对微型水力发电有显著影响。这两个变量可以用来预测微型水力发电的发电量。在未来的研究中,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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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