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An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting

机译：短期电负荷预测全小波包变换和神经网络的集合方法

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摘要

Due to high penetration of distributed energy resources, integration of intermittent renewable energy resources and deployment of demand-side management, highly accurate short-term load forecasting becomes increasingly important. This paper proposes a full wavelet neural network approach for short-term load forecasting, which is an ensemble method of full wavelet packet transform and neural networks. The full wavelet packet transform model is used to decompose the load profile and various features into several components with different frequencies and these components are used to train the neural networks. To perform load forecasting, the full wavelet packet transform model decomposes features into various components that are fed into the trained neural networks, and the outputs of the neural networks are constructed as the forecasted load. The proposed model is applied for load prediction in the electric market of Ontario, Canada. Simulation results show that the proposed approach reduces the mean absolute percentage error (MAPE) by 20% in comparison with the traditional neural network method. The proposed approach can be used by utilities and system operators to forecast electricity consumption with high accuracy, which is highly demanded for renewable energy integration, demand-side management and power system operation.
机译：由于分布式能源资源的高渗透，间歇性可再生能源资源的整合和需求侧管理部署，高度准确的短期负荷预测变得越来越重要。本文提出了一种全小波神经网络方法，用于短期负荷预测，是全小波包变换和神经网络的集合方法。全小波分组变换模型用于将负载简档和各种特征分解为具有不同频率的多个组件，并且这些组件用于训练神经网络。为了执行负载预测，全小波分组变换模型将特征分解成馈送到培训的神经网络中的各种组件，并且神经网络的输出被构造为预测负载。拟议的模型用于加拿大安大略省电力市场的负载预测。仿真结果表明，与传统的神经网络方法相比，该拟议方法将平均绝对百分比误差（MAPE）降低20％。该建议的方法可以由公用事业和系统运营商使用，以预测高精度的电力消耗，这对于可再生能源集成，需求侧管理和电力系统操作非常有要求。

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