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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摘要小时级的超短期负荷预测是调度部门制定日内滚动计划的基础。该文提出了一种基于优化稀疏编码的超短期负荷多步预测算法,对未来4h的负荷进行滚动预测。首先,将历史负荷功率序列组成具有时延的特征字典和目标字典,并利用稀疏编码算法建立多步预测模型;然后,基于负荷曲线的相似性特性,借助实时的负荷功率特征向量与特征字典中原子的拓展符号化距离对特征字典原子进行筛选,提高了稀疏编码的预测精度;最后,对预测结果进行误差分析。针对负荷爬坡时段误差偏大的问题,通过基于负荷增量预测值的修正模型进行优化,进一步提高了预测精度。采用实际负荷数据进行分析,验证了所提方法的有效性。

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	储晨阳
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关键词 ：超短期负荷预测, 滚动预测, 优化稀疏编码, 拓展符号化距离, 负荷增量预测

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

收稿日期: 2020-08-06

PACS:

TM715

基金资助:国家自然科学基金（51837004）和111引智计划（B14022）资助项目

通讯作者: 秦川男,1981年生,副教授,硕士研究生导师,研究方向为电力系统分析与控制。E-mail：cqin@hhu.edu.cn

作者简介: 储晨阳男,1996年生,硕士,研究方向为电力系统负荷预测。E-mail：ccy1996@hhu.edu.cn

引用本文:

储晨阳, 秦川, 鞠平, 赵静波, 赵健. 基于优化稀疏编码的超短期负荷滚动多步预测[J]. 电工技术学报, 2021, 36(19): 4050-4059. Chu Chenyang, Qin Chuan, Ju Ping, Zhao Jingbo, Zhao Jian. Multi-Step Rolling Ultra-Short-Term Load Forecasting Based on the Optimized Sparse Coding. Transactions of China Electrotechnical Society, 2021, 36(19): 4050-4059.

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