Forecasting Electricity Load Demand- An Power System Planning

Elektrotechnik Berg
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引用次数: 2

Abstract

Moving holiday electricity load demand forecasting is one of the most challenging topics in the forecasting area. Forecasting electricity load demand is essential because it involves projecting the peak demand level. Overestimation of future loads results in excess supply. Wastage of this load is not welcome by the international energy network. An underestimation of load leads to failure in providing adequate reserve, implying high costs. Many factors can influence the electricity load demand, such as previous load demand, type of the day, coincidence with other holidays and the impact of major events. Hence, 12 independent variables were considered in constructing the regression model to forecast moving holiday electricity load demand. This study investigates Malaysia’s daily electricity load demand data using multiple linear regression to forecast electricity load demand on moving holidays, such as Hari Raya AidilFitri, Chinese New Year, Hari Raya AidilAdha, and Deepavali from September 2016 to October 2017. The result shows six independent variables are significant from the several method variables selections. Overall, the constructed models from this study give promising results and can forecast for next year’s moving holiday electricity load demand with a sample forecasting error of 3.7% on the day of the moving holiday.
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电力负荷需求预测——电力系统规划
移动假日电力负荷需求预测是预测领域最具挑战性的课题之一。预测电力负荷需求是必不可少的,因为它涉及到预测峰值需求水平。对未来负荷的过高估计会导致供应过剩。这种负荷的浪费是不受国际能源网络欢迎的。对负荷的低估导致无法提供足够的储备,这意味着高成本。影响电力负荷需求的因素很多,如以前的负荷需求、当天的类型、与其他假日的巧合以及重大事件的影响。因此,在构建回归模型时,考虑了12个自变量来预测移动假日电力负荷需求。本研究调查了马来西亚2016年9月至2017年10月的每日电力负荷需求数据,使用多元线性回归预测了移动假期的电力负荷需求,如开斋节、中国新年、开斋节和排妖节。结果表明,从选取的几个方法变量来看,有6个自变量是显著的。总体而言,本研究构建的模型给出了令人满意的结果,可以预测明年搬家假期当天的电力负荷需求,样本预测误差为3.7%。
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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