现代电力2026，Vol.43Issue(2)：253-264,12.DOI:10.19725/j.cnki.1007-2322.2024.0007
基于多类型天气识别的光伏功率日前预测
Day-ahead Prediction of Photovoltaic Power Generation Based on Multi-type Weather Identification
杨秀 1闫钟宇 1孙改平 1熊雪君 2冯煜尧2
作者信息
- 1. 上海电力大学 电气工程学院,上海市 杨浦区 200090
- 2. 国网上海市电力公司电科院,上海市 虹口区 200092
- 折叠
摘要
Abstract
In response to the issue of the drastic fluctuation in PV power output in a short period of time under transitional weather,which will result in lower prediction accuracy,a PV power day-ahead prediction method is proposed based on multi-type weather identification.A new model with multi-layer division is proposed for weather identification.Firstly,the weather state index is utilized to reflect the weather characteristics.Secondly,a Gaussian mixture model is employed to extract the power fluctuation characteristics in clustering way.Finally,these two models are crossed and combined based on the concept drift algorithm,so as to distinguish the turning weather days and four kinds of smooth weather days for improving the weather type identification precision.Meanwhile,an interval prediction model based on quantile regression is proposed for power prediction.Firstly,the significant meteorological features of the five weather types are selected according to the transfer entropy respectively,taking into full consideration the specificity of weather patterns.Subsequently,to enhance the model's generalization ability,the multilayer perceptron neural network,convolutional neural network,and bidirectional long-and short-term memory neural network are modularly integrated.Finally,the neural network quantile regression model is combined and the prediction interval is generated.The effectiveness of the proposed model in point prediction and interval prediction is verified using the data collected from a photovoltaic field located in Shanghai,China.关键词
光伏功率/天气分型/传递熵/深度学习/分位数回归Key words
photovoltaic power/weather classification/transfer entropy/deep learning/quantile regression分类
信息技术与安全科学引用本文复制引用
杨秀,闫钟宇,孙改平,熊雪君,冯煜尧..基于多类型天气识别的光伏功率日前预测[J].现代电力,2026,43(2):253-264,12.
