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首页> 外文会议>Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion >Day-ahead Electricity Price Forecasting using Optimized Multiple-Regression of Relevance Vector Machines
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Day-ahead Electricity Price Forecasting using Optimized Multiple-Regression of Relevance Vector Machines

机译：使用优化的多重回归相关性矢量机器的日前电价预测

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摘要

In deregulated, auction-based, electricity markets price forecasting is an essential participant tool for developing bidding strategies. In this paper, a day-ahead intelligent forecasting method for electricity prices is presented. The proposed approach is comprised of two steps. In the first step, a set of two relevance vector machines (RVM) is employed where each one provides next day predictions for the price evolution. In the second step, a multiple regression model comprised of the two relevance vector machines is built and the regression coefficients are computed using genetic based optimization. The performance of the proposed approach is tested on a set of electricity price hourly data from four different seasons and compared to those obtained by each of the relevance vector machines. The results clearly demonstrate, in terms of mean square error, the superiority of the proposed method over each individual RVM.
机译：在解除管制，拍卖的基础上，电力市场价格预测是开发竞标策略的重要参与工具。本文介绍了一天的智能预测方法，供电。该方法由两个步骤组成。在第一步中，采用一组两个相关的向量机（RVM），其中每个将第二天提供价格进化的第二天预测。在第二步中，构建了由两个相关矢量机器组成的多元回归模型，并且使用基于遗传优化来计算回归系数。所提出的方法的性能在四个不同季节的一组电汇数据上进行测试，并与每个相关矢量机器获得的那些。结果清楚地证明了平均方误差，所提出的方法在每个单独的RVM上的优越性。

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