Annual electricity consumption forecasting with least squares support vector machines

Y. Wang, Songqing Yu
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引用次数: 3

Abstract

In the electricity market, electricity consumption reflects the electric power usage of the whole society. As an important part of load forecasting, annual electricity consumption forecasting plays an important role for all the market participants, especially for the decision-makers establishing bidding strategies of generation companies and the planners of market investors. In this paper, factor selection on the inputs of electricity consumption forecasting is first performed to find proper features from the data in terms of their statistical information. After that, the regression LS-SVM is generalized for the forecasting modeling in this work that is built using the data after factor analysis. In this way, electricity consumption forecasting is accomplished, which not only overcomes the over-fitting of traditional forecasting methods, but also quickens the computation velocity of standard SVM by converting the quadratic optimization problem into the computation of the linear equations. In the end, the case studies are carried out to test the proposed model, which indicates that the built model is an accurate and quick method to predict the electricity consumption.
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基于最小二乘支持向量机的年用电量预测
在电力市场上,用电量反映了整个社会的用电情况。年度用电量预测作为负荷预测的重要组成部分,对所有市场参与者,特别是对发电公司制定竞价策略的决策者和市场投资者的规划者都具有重要的作用。本文首先对用电量预测的输入进行因子选择,从数据的统计信息中找到合适的特征。然后,利用因子分析后的数据,将回归LS-SVM推广到本文的预测建模中。这样既克服了传统预测方法的过拟合,又将二次优化问题转化为线性方程的计算,加快了标准支持向量机的计算速度。最后,通过实例对所建立的模型进行了验证,结果表明所建立的模型是一种准确、快速的预测用电量的方法。
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