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首页> 外文期刊>Electric power systems research >A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid
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A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid

机译：基于智能电网聚类技术的ANN，WNN和KF的建议智能短期负荷预测混合模型

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摘要

Smart grid is one of the most important topics to be covered with the increasing penetration of renewable energy in the power system grid to improve grid energy efficiency by managing the relationship between the demand and the generation. Load forecasting is playing a crucial role in this process as well as the output power generation from different renewable energy resources. The accuracy of the forecasting models is very important to deal with the new energy generation and consumption. Conventional approaches used in the literature work done for load forecasting can not handle the requirements of new generation of renewable energy and their uncertainties. This paper is proposing a novel technique for short-term load forecasting based on hybrid of different models and using clustering techniques to improve the overall system performance and quality. These models involve different combinations of Kalman filtering (KF), Wavelet and Artificial Neural Network (WNN and ANN) schemes. Six different models are proposed based on the clustering techniques. Simulations proved higher performance of the proposed models. The data used is commercial data, so it is scaled in this paper. The proposed work is validated by using different dataset for two different locations in Egypt and Canada.
机译：智能电网是在电力系统网格中越来越多的可再生能源的渗透来覆盖最重要的主题之一，以通过管理需求与生成之间的关系来提高电网能效。负载预测在该过程中发挥着至关重要的作用，以及不同可再生能源资源的输出发电。预测模型的准确性对于应对新能源生成和消费非常重要。用于负载预测的文献工作中使用的常规方法无法处理新一代可再生能源及其不确定性的要求。本文提出了一种基于不同模型混合的短期负荷预测的新技术，并使用聚类技术提高整体系统性能和质量。这些模型涉及卡尔曼滤波（KF），小波和人工神经网络（WNN和ANN）方案的不同组合。基于聚类技术提出了六种不同的型号。模拟证明了拟议模型的更高性能。使用的数据是商业数据，因此它在本文中进行了缩放。拟议的工作是通过在埃及和加拿大的两个不同地点使用不同的数据集进行验证。

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