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首页> 外文期刊>IEEE Transactions on Power Systems >Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks
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Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks

机译：小波在基于PSO的神经网络短期负荷预测中的应用

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The paper addresses the problem of predicting hourly load demand using adaptive artificial neural networks (ANNs). A particle swarm optimization (PSO) algorithm is employed to adjust the network''s weights in the training phase of the ANNs. The advantage of using a PSO algorithm over other conventional training algorithms such as the back-propagation (BP) is that potential solutions will be flown through the problem hyperspace with accelerated movement towards the best solution. Thus the training phase should result in obtaining the weights configuration associated with the minimum output error. Data are wavelet transformed during the preprocessing stage and then inserted into the neural network to extract redundant information from the load curve. This results in better load characterization which creates a more reliable forecasting model. The transformed data of historical load and weather information were trained and tested over various periods of time. The generalized error estimation is done by using the reverse part of the data as a “test” set. The results were compared with traditional BP algorithm and offered a high forecasting precision.
机译：本文解决了使用自适应人工神经网络（ANN）预测小时负荷需求的问题。在人工神经网络的训练阶段，采用粒子群优化（PSO）算法来调整网络的权重。与其他传统训练算法（例如，反向传播（BP））相比，使用PSO算法的优势在于，潜在的解决方案将通过问题超空间流动，并朝着最佳解决方案加速移动。因此，训练阶段应导致获得与最小输出误差相关的权重配置。在预处理阶段对数据进行小波变换，然后将其插入神经网络以从负载曲线中提取冗余信息。这样可以更好地进行负载表征，从而创建更可靠的预测模型。在不同的时间段内对经过转换的历史负荷和天气信息数据进行了培训和测试。通用误差估计是通过使用数据的相反部分作为“测试”集来完成的。结果与传统的BP算法进行比较，具有较高的预测精度。

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