西北工程技术学报2026，Vol.25Issue(2)：110-119,10.DOI:10.26974/j.cnki.XBGC.2026.02.002
基于LSTM模型的光伏辐照度短期预报订正技术
LSTM-Based Model for Short-Term Correction of Photovoltaic Irradiance Forecasting
摘要
Abstract
To improve the accuracy of short-term photovoltaic irradiance forecasts from numerical models,the study utilized the hourly short-term radiation forecast products from the China Meteorological Administration Wind and Solar Numerical Forecasting System(CMA-WSP 2.0)for the period 2023-2025,along with ground observational irradiance data from photovoltaic power stations and meteorological observation stations in Ningxia.A sequence correction model integrating a long short-term memory(LSTM)network with an Attention Mechanism(hereafter referred to as attention-enhanced LSTM sequence correction,ALSC)was constructed,in which the LSTM captured the long-term dependence of radiation time series,and the attention mechanism assigned adaptive weights to meteorological factors.The uncorrected CMA-WSP 2.0 forecast products served as a control.The effectiveness of the model correction was evaluated using metrics including the coefficient of determination(R²),root mean square error(RMSE),and mean absolute error(MAE).The results indicate that,using 2025 data as the test set,R² of the corrected radiation forecast significantly improved,while RMSE and MAE decreased by 10.57%and 7.57%,respectively.Evaluation of March data under different sky conditions showed that the average relative errors under overcast and cloudy conditions were reduced from 28.87%and 25.47%to 3.06%and 0.33%,respectively,with skill scores(indicating the reduction in errors of the corrected forecasts relative to the original forecast values)of 89.4%and 98.7%,indicating a significant enhancement in forecasting accuracy.Under weather conditions with cloud cover and rapid radiation fluctuations,the model's responsiveness to abrupt radiation changes was also enhanced.This study provides a theoretical basis and practical guidance for power generation forecasting at photovoltaic power stations and optimization of power grid scheduling.关键词
CMA-WSP 2.0/长短期记忆(LSTM)/注意力机制/光伏辐照度/短期预报Key words
CMA-WSP 2.0/long short-term memory(LSTM)/attention mechanism/photovoltaic irradiance/short-term forecasting分类
信息技术与安全科学引用本文复制引用
曾荣阳,雍佳,武万里,刘建宏,龚晓丽..基于LSTM模型的光伏辐照度短期预报订正技术[J].西北工程技术学报,2026,25(2):110-119,10.基金项目
中国气象局旱区特色农业气象灾害监测预警与风险管理重点实验室项目(CAMT-202503,CAMP-202508) （CAMT-202503,CAMP-202508）
宁夏自然科学基金项目(2022AAC03676) （2022AAC03676）
宁夏气象局重点创新团队项目 （）
