基于多模型融合神经网络的短期负荷预测
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摘要
短期负荷预测在为电力系统制定经济、可靠和安全的运行策略中起着关键作用。为了提高预测精度,提出了一种基于多模型融合神经网络的短期负荷预测方法。首先将三种不同的神经网络单独训练:再将单独模型的输出作为输入,训练一个顶层全连接神经网络;最后并行排列三种单独模型,再将3个模型的输出层组合作为顶层全连接神经网络的输入层,使4个模型融合为一个模型,并进行精调训练。短期负荷预测的实验结果表明,该方法的精度优于单个全连接神经网络、长短期记忆网络或残差网络。说明该方法具有良好的实用价值。
Power industry requires accurate short-term load forecasting to provide precise load requirements for power system control and scheduling. In order to improve the accuracy of short-term power load forecasting, a method based on FFT optimized ResNet model is proposed. The model first defines power load forecasting as a time series problem, then introduces one-dimensional ResNet for power load regression prediction, and uses FFT to optimize ResNet. The FFT transform of a layer of convolution results gives the model the ability to extract periodic features in the data. Experiments show that the prediction accuracy of FFT-ResNet is better than several benchmark models in 6-hour power load forecasting, which indicates that this method has a good application prospect in power load forecasting.
Power industry requires accurate short-term load forecasting to provide precise load requirements for power system control and scheduling. In order to improve the accuracy of short-term power load forecasting, a method based on FFT optimized ResNet model is proposed. The model first defines power load forecasting as a time series problem, then introduces one-dimensional ResNet for power load regression prediction, and uses FFT to optimize ResNet. The FFT transform of a layer of convolution results gives the model the ability to extract periodic features in the data. Experiments show that the prediction accuracy of FFT-ResNet is better than several benchmark models in 6-hour power load forecasting, which indicates that this method has a good application prospect in power load forecasting.
引文
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[9]李冬辉,尹海燕,郑博文.基于MFOA-GRNN模型的年电力负荷预测[J].电网技术,2018(2).Li D H, Yin H Y, Zheng B W. Annual power load forecast based on MFOA-GRNN model[J]. Grid technology, 2018(2).
[10] Khwaja A S, Zhang X,Anpalagan A, et al. Boosted neural networks for improved short-term electric load forecasting[J]. Electric Power Systems Research, 2017,143:431-437.
[11] Jurado S, Angela Nebot, Mugica F, et al. Hybrid methodologies for electricity load forecasting:Entropy-based feature selection with machine learning and soft computing techniques[J]. Energy, 2015,86:276-291.
[12] Ba J, Hinton G E, Mnih V, et al. Using fast weights to attend to the recent past[C]//Advances in Neural Information Processing Systems.2016:4331-4339.
[2] Ho K L,Hsu Y Y,Yang C C. Short Term Load Forecasting Using a Multilayer Neural Network with an Adaptive Learning Algorithm[J].1992, 7(1):141-149.
[3] Chen S T, Yu D C, Moghaddamjo A R. Weather sensitive short-term load forecasting using nonfully connected artificial neural network[J].IEEE Transactions on Power Systems, 1992,7(3):1098-1105.
[4] Taylor J W, Buizza R. Neural network load forecasting with weather ensemble predictions[J]. IEEE Transactions on Power systems, 2002,17(3):626-632.
[5] Hippert H S, Pedreira C E, Souza R C. Neural networks for short-term load forecasting:A review and evaluation[J]. IEEE Transactions on power systems, 2001,16(1):44-55.
[6] Vermaak J, Botha E C. Recurrent neural networks for short-term load forecasting[J]. IEEE Transactions on Power Systems, 1998, 13(1):126-132.
[7] Marino D L, Amarasinghe K, Manic M. Building energy load forecasting using deep neural networks[C]//Industrial Electronics Society, IECON 2016-42nd Annual Conference of the IEEE. IEEE,2016:7046-7051.
[8] Dong X, Qian L, Huang L. Short-term load forecasting in smart grid:A combined CNN and K-means clustering approach[C]//Big Data and Smart Computing(BigComp), 2017 IEEE International Conference on.IEEE, 2017:119-125.
[9]李冬辉,尹海燕,郑博文.基于MFOA-GRNN模型的年电力负荷预测[J].电网技术,2018(2).Li D H, Yin H Y, Zheng B W. Annual power load forecast based on MFOA-GRNN model[J]. Grid technology, 2018(2).
[10] Khwaja A S, Zhang X,Anpalagan A, et al. Boosted neural networks for improved short-term electric load forecasting[J]. Electric Power Systems Research, 2017,143:431-437.
[11] Jurado S, Angela Nebot, Mugica F, et al. Hybrid methodologies for electricity load forecasting:Entropy-based feature selection with machine learning and soft computing techniques[J]. Energy, 2015,86:276-291.
[12] Ba J, Hinton G E, Mnih V, et al. Using fast weights to attend to the recent past[C]//Advances in Neural Information Processing Systems.2016:4331-4339.