基于改进Apriori关联分析及MFOLSTM算法的短期负荷预测

王凌云; 林跃涵; 童华敏; 李黄强; 张 涛

引用本文:	王凌云,林跃涵,童华敏,等.基于改进Apriori关联分析及MFOLSTM算法的短期负荷预测[J].电力系统保护与控制,2021,49(20):74-81.
	WANG Lingyun,LIN Yuehan,TONG Huamin,et al.Short-term load forecasting based on improved Apriori correlation[J].Power System Protection and Control,2021,49(20):74-81

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基于改进Apriori关联分析及MFOLSTM算法的短期负荷预测
王凌云，林跃涵，童华敏，李黄强，张涛
(1.三峡大学电气与新能源学院，湖北宜昌 443002；2.国网宜昌供电公司，湖北宜昌 443002)

摘要:

电力负荷预测结果的准确性对电力系统安全稳定运行具有重要意义。针对多气象因素影响下的短期负荷预测任务，提出改进Apriori关联度分析及飞蛾火焰优化的长短时记忆神经网络算法的电力负荷短期预测新方法。首先，提出改进Apriori算法分析气象因素与负荷之间的关联程度。依据分析结果除去非必要气象影响因素，并在此基础上引入人体舒适度评价指标。其次，将降维后气象数据结合地区负荷数据作为模型输入。最后，基于长短时记忆神经网络进行短期负荷预测建模，并结合飞蛾火焰优化算法的全局寻优能力来优化模型。通过对某地区负荷数据协同气象数据进行对比预测试验，测试结果表明该负荷预测模型能有效提升地区电网短期负荷预测性能。

关键词: 短期负荷预测 Apriori关联分析飞蛾火焰算法长短时记忆神经网络

DOI：DOI: 10.19783/j.cnki.pspc.210026

分类号:

基金项目:国家自然科学基金项目资助(61603212)

Short-term load forecasting based on improved Apriori correlation

WANG Lingyun, LIN Yuehan, TONG Huamin, LI Huangqiang, ZHANG Tao

(1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China;2. State Grid Yichang Power Supply Company, Yichang 443002, China)

Abstract:

The accuracy of power load forecasting results is of great significance to the safe and stable operation of a power system. Considering short-term load forecasting under the influence of multiple meteorological factors, a new short-term power load forecasting method based on an improved Apriori correlation analysis and moth-flame optimization is proposed. First, an improved Apriori algorithm is used to analyze the correlation between meteorological factors and load. Unnecessary meteorological factors are removed from the results, and a human comfort evaluation index is introduced. Secondly, the reduced meteorological data combined with regional load data are used as input to the model. Finally, the short-term load forecasting model is established based on a long short-term memory neural network, and the global optimization model is combined with the moth-flame optimization algorithm. The results show that the load forecasting model can effectively improve the short-term load forecasting performance of a regional power grid.

Key words: short term load forecasting Apriori correlation analysis moth-flame algorithm long short-term memory neural network

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