基于优化稀疏编码的超短期负荷滚动多步预测

doi:10.19595/j.cnki.1000-6753.tces.200983

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Abstract Ultra-short-term load forecasting is the basis of intra-day rolling scheduling for the dispatching departments. An optimized sparse coding based multi-step load forecasting method is proposed to make rolling forecast of the load power in the next 4 hours. Firstly, the historical load power time series data are used to create the predictor/response dictionaries pair with time-lag, then the multi-step load forecasting model can be built via sparse coding. Secondly, considering the similarity of the load power time series data, the atoms of the dictionaries pair are filtered according to the extended symbolic aggregate approximation distance between the real-time load power data and the vectors of the dictionaries, which improves the load forecasting accuracy. Finally, error analysis is performed. It is found that the forecast errors during the period of load ramping up are always larger than that of the other periods. Therefore, the error correction model based on the load increment forecasting is proposed to further improve the prediction accuracy. The effectiveness of the proposed method is verified by the case of real-world load power dataset.

Key words： Ultra-short-term load forecasting rolling forecasting optimized sparse coding extended symbolized aggregate approximation distance load increment forecasting

Received: 06 August 2020

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TM715

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	Articles by authors
	Chu Chenyang
	Qin Chuan
	Ju Ping
	Zhao Jingbo
	Zhao Jian

Cite this article:

Chu Chenyang,Qin Chuan,Ju Ping等. Multi-Step Rolling Ultra-Short-Term Load Forecasting Based on the Optimized Sparse Coding[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4050-4059.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.200983 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I19/4050

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