基于时间卷积网络的短期电力负荷预测

doi:10.19595/j.cnki.1000-6753.tces.210223

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Abstract Accurate short-term electrical load forecasting plays an important role in ensuring the safe and stable operation of the power grid, optimizing energy management, improving the utilization rate of power generation equipment, and reducing operating costs. Owing to the incapability of traditional time series methods in dealing with data’s nonlinear characteristics, this article first compared the applicability and forecasting performance of classical machine learning methods in short-term electrical load forecasting. The adopted modeling methods include: support vector regression, Gaussian process regression and forward neural network; the values of metrics proved that the machine learning methods can obtain good prediction accuracy and are suitable for processing short-term load data with strong nonlinear characteristics; on this basis, a short-term electrical load forecasting method based on temporal convolutional network (TCN) deep learning method was proposed. This model has the ability to extract features from large samples of time series and realize prediction, and its structure can effectively solve the degenerate problem of deep network learning; finally, forecasting experiment was conducted on the real load data. The results showed that the TCN can acquire much higher accuracy, and the deep learning method outperforms the classical machine learning methods in non-linear feature extraction.

Key words： Short-term electrical load forecasting machine learning support vector regression Gaussian process regression temporal convolution network

Received: 22 February 2021

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TM743

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	Articles by authors
	Zhao Yang
	Wang Hanmo
	Kang Li
	Zhang Zhaoyun

Cite this article:

Zhao Yang,Wang Hanmo,Kang Li等. Temporal Convolution Network-Based Short-Term Electrical Load Forecasting[J]. Transactions of China Electrotechnical Society, 2022, 37(5): 1242-1251.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210223 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I5/1242

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