A pareto optimization approach of a Gaussian process ensemble for short-term load forecasting

M. Alamaniotis, A. Ikonomopoulos, L. Tsoukalas
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引用次数: 25

Abstract

Accurate prediction of load demand remains a challenge for efficient power distribution and becomes critical in the context of smart grid management when the presence of stochastic sources adds to the stochasticity of demand. Short-term load forecasting involving demand prediction in the range of hours or days is of special interest to generators and power customers. A number of methods has been developed for fast and accurate electric power forecasting. Among others, Gaussian process (GP) regression has been used for prediction in the nonlinear problems with promising results. On that direction, an ensemble of Gaussian process regressors modeled as kernel machines is proposed for load forecasting. The use of different kernels accommodates the construction of a group composed of different predictors and its evolution using genetic algorithms. The proposed approach takes the form of a multiobjective problem in which the objectives consist of a set of criteria. In order to optimize all the criteria it needs to use Pareto optimality to identify an accepted solution. The results obtained show that the ensemble of GP predictors outperforms each individual forecaster.
短期负荷预测的高斯过程集合pareto优化方法
负荷需求的准确预测是有效配电的一个挑战,在智能电网管理的背景下,当随机源的存在增加了需求的随机性时,负荷需求的准确预测变得至关重要。短期负荷预测涉及小时或天范围内的需求预测,这对发电机和电力客户特别感兴趣。为了实现快速、准确的电力预测,人们开发了许多方法。其中,高斯过程(GP)回归已被用于非线性问题的预测,并取得了良好的结果。在这个方向上,提出了一种以核机为模型的高斯过程回归量集合来进行负荷预测。不同核的使用适应了由不同预测因子组成的群体的构建及其使用遗传算法的进化。所建议的方法采用多目标问题的形式,其中目标由一套标准组成。为了优化所有的标准,它需要使用帕累托最优来确定一个可接受的解决方案。得到的结果表明,GP预测器的集合优于单个预测器。
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