湖南工业大学学报2026，Vol.40Issue(2)：25-33,9.DOI:10.20271/j.cnki.1673-9833.2026.2004
基于体感温度和IFLA优化CNN-BiLSTM模型的短期电力负荷预测
Short-Term Power Load Forecasting Based on Perceived Temperature and IFLA Optimized CNN-BiLSTM
摘要
Abstract
In view of an accurate prediction of the impact of power load on optimizing power generation and scheduling plans,as well as an improvement of economic efficiency,so as to ensure safe operation of the power grid,a short-term power load forecasting model has thus been proposed based on perceived temperature and improved Fick's law algorithm(IFLA)optimized CNN BiLSTM.Logistic mapping,Cauchy Gaussian mutation strategy,spiral wave search,and other techniques are used to improve FLA.Firstly,the features of meteorological data are amplified by adopting the somatosensory temperature formula.Secondly,the CNN BiLSTM network is subjected to hyperparameter optimization using IFLA.Finally,the CNN BiLSTM performs feature extraction on the data and outputs prediction results.On the basis of simulation experiments on the residential load dataset of a certain location in Hunan Province in March 2022,the experimental results show that the IFLA-CNN BiLSTM prediction model outputs root mean square error,average absolute error,average absolute percentage error,and coefficient of determination of 1.305,0.882,2.558%,and 0.989,respectively,verifying the generalization and reliability of the IFLA-CNN-BiLSTM model in practical environmental applications.关键词
短期电力负荷预测/体感温度/改进菲克定律优化算法Key words
short-term power load forecasting/perceived temperature/improved Fick's law algorithm分类
信息技术与安全科学引用本文复制引用
赵文川,于惠钧,陈刚,徐银凤,邹海,辜海缤..基于体感温度和IFLA优化CNN-BiLSTM模型的短期电力负荷预测[J].湖南工业大学学报,2026,40(2):25-33,9.基金项目
国家重点研发计划基金资助项目(2022YFE0105200) （2022YFE0105200）
湖南省株洲市电网有限公司科技基金资助项目(SGHNZZ00DKWT2400646) （SGHNZZ00DKWT2400646）
