基于ISSD-GRU模型的台区售电量预测方法
ISSD-GRU Model-based Electricity Sale Prediction
刘成1
作者信息
- 1. 国网徐州市铜山区供电公司,江苏 徐州 221018
- 折叠
摘要
针对台区售电量不确定影响因素多、预测精度不高的问题,提出了一种基于改进ISSD优化GRU神经网络的台区售电量预测方法,利用反向学习提高SSD算法对最优参数的搜索效率.以某地台区历史售电量、温度、工作日类型和节假日类型作为影响因素对GRU模型进行训练,利用ISSD算法实现对GRU隐藏层神经元个数和学习率超参数的寻优,构建用于台区售电量预测的ISSD-GRU模型.算例分析表明,ISSD-GRU模型在台区售电量预测结果上精度更高.
Abstract
The present work made an attempt on addressing problems of excessive uncertainties-induced low accuracy in station area electricity sale prediction,and proposed a prediction method based on modified ISSD optimized GRU neural network which uses reverse learning to improve searching efficiency of the SSD algorithm for optimal parameters.The GRU model was trained with influences data such as historical electricity sales,temperature,working day type and holi-day type of a certain distribution station,and ISSD optimization algorithm was used to realize optimal searching of number of hidden layer neurons and hyperparameters of learning rate,thereby an ISSD-GRU model for electricity sale prediction was established.The proposed ISSD-GRU model was indicated by case analysis to have improved accuracy for predicting station area electricity sales.
关键词
反向学习/SSD算法/GRU神经网络/售电量预测/时间序列/预测精度Key words
OBL/SSD algorithm/GRU neural network/electricity sale prediction/time sequence/prediction accuracy引用本文复制引用
基金项目
国家电网公司总部科技项目(5108-202218280A-2-296-XG)
出版年
2024
NETL
NSTL
万方数据