基于GA理论与QPSO-ELM结合的短期负荷预测方法

doi:10.3969/j.issn.1008-0198.2022.01.013

摘要/Abstract

摘要： 为了更快地挖掘海量负荷数据中的非线性关系,提高短期负荷预测模型精度,针对量子粒子群算法(QPSO)在处理复杂高维参数优化问题时能力不足的缺点,结合遗传算法(GA)中的杂交进化思想对其进行改进,提出遗传量子粒子群算法(GAQPSO)来优化正则极限学习机的输入权重和隐藏层偏差,形成混合短期负荷预测GAQPSO-ELM模型。同时,在输入特征选取时,充分考虑历史负荷、温度、时刻以及工休日等相关因素的影响,进一步提高短期负荷预测模型的准确性。实验结果表明,短期负荷预测模型相对于QPSO-ELM模型和普通ELM模型具有更高的精度,并且更能反映日负荷曲线的变化趋势,验证了新提出预测模型的有效性。

关键词: 遗传算法, 量子粒子群算法, 极限学习机, 短期负荷预测

Abstract: In order to screen out the nonlinear relationship in massive load data more quickly and improve the accuracy of short-term load forecasting model, this paper focuses on the shortcomings of quantum particle swarm optimization algorithm (QPSO) in dealing with complex high-dimensional parameter optimization problems, and the genetic quantum particle swarm optimization algorithm (GAQPSO) combined with hybrid evolutionary theory of genetic algorithm (GA) is proposed to optimize the input weight and hidden layer deviation of the regular extreme learning machine, which forms a hybrid short-term load forecasting GAQPSO-ELM model. Meanwhile, when the input features are selected, the influences of historical load, temperature, time interval and type of date are fully considered, so the accuracy of the short-term load forecasting model is further improved. The experimental result shows that the proposed short-term load forecasting model has a higher accuracy than the QPSO-ELM model and the common ELM model, and it can better reflect the change trend of the daily load curve, which verifies the effectiveness of the proposed predictive model.

Key words: genetic algorithm, quantum particle swarm optimization algorithm, extreme learning machine, short-term load forecasting

中图分类号:

TM714

陈永龙, 石麒, 王二庆. 基于GA理论与QPSO-ELM结合的短期负荷预测方法[J]. 湖南电力, 2022, 42(1): 64-70.

CHEN Yonglong, SHI Qi, WANG Erqing. Short-Term Load Forecasting Method Based on QPSO-ELM Combined with GA Theory[J]. Hunan Electric Power, 2022, 42(1): 64-70.

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