Forecast of Short-term Electricity Price of Construction Engineering Based on Grey Wavelet Neural Network
周理翔 ZHOU Li-xiang
（上海勘测设计研究院有限公司，上海 200335）
（Shanghai Survey and Design Research Institute Co.，Ltd.，Shanghai 200335，China）
摘要： 为适应我国电力交易市场化改革战略，提出准确适用的电价预测方法能够有效规避交易市场风险以达到经济效应最大化。本文基于水电工程电力消耗特征，考虑影响水电工程电价影响因素，采用灰色关联度评价方法确定重要预测特征，基于小波神经网络构建水电工程短期电价预测算法。运用MATLAB软件针对算例数据仿真求解，预测水电工程短期电价。结果表明，本文提出的水电工程短期电价预测算法能够有效的泛化水电工程特征影响因素，与传统模型相比具有较高的预测精度。
Abstract: To adapt to the market-oriented reform strategy of China's electricity trading, an accurate and applicable electricity price forecasting method is proposed to effectively avoid trading market risks to maximize economic effects. Based on the power consumption characteristics of construction projects, this paper considers the factors affecting the electricity price of construction projects, adopts the gray correlation evaluation method to determine the important forecasting characteristics, and builds short-term electricity price forecasting algorithms for construction projects based on wavelet neural network. Use MATLAB software to simulate and solve the calculation example data to predict the short-term electricity price of construction projects. The results show that the short-term electricity price prediction algorithm for construction projects proposed in this paper can effectively generalize the influencing factors of construction engineering characteristics, and has higher prediction accuracy compared with traditional models.
关键词： 水电工程；电价预测；模糊理论；小波神经网络
Key words: construction engineering；electricity price forecast；fuzzy theory；wavelet neural network
中图分类号：TM715 文献标识码：A 文章编号：1006-4311（2022）21-029-05
DOI:10.3969/j.issn.1006-4311.2022.21.009.
文章出处：周理翔. 基于Morlet-Maxican Hat小波神经网络的水电工程短期电价预测研究[J]. 价值工程,2022,41(21):29-33.
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