辐照度
计算机科学
水准点(测量)
期限(时间)
卷积神经网络
一般化
太阳辐照度
人工智能
人工神经网络
深度学习
光伏系统
模式识别(心理学)
数据挖掘
气象学
地图学
地理
数学
工程类
数学分析
物理
电气工程
量子力学
作者
Haixiang Zang,Ling Liu,Li Sun,Lilin Cheng,Zhinong Wei,Guoqiang Sun
标识
DOI:10.1016/j.renene.2020.05.150
摘要
Accurate short-term solar irradiance forecasting is crucial for ensuring the optimum utilization of photovoltaic power generation sources. This study addresses this issue by proposing a spatiotemporal correlation model based on deep learning. The proposed model first applies a convolutional neural network (CNN) to extract spatial features from a two-dimensional matrix composed of meteorological parameters associated with a target site and its neighboring sites. Then, a long short-term memory (LSTM) network is applied to extract temporal features from historical solar irradiance time-series data associated with the target site. Finally, the spatiotemporal correlations are merged to predict global horizontal irradiance one hour in advance. The prediction performance and generalization ability of the proposed CNN-LSTM model are evaluated within a whole year, under diverse seasons and sky conditions. Three datasets are involved for case studies, which are collected from 34 locations spread across three different climate zones in Texas, USA. Moreover, the performance of the CNN-LSTM model is compared with those obtained using the CNN, LSTM, and other benchmark models based on five evaluation metrics. The results indicate that the proposed model has advantages over the other models considered and provides a good alternative for short-term solar radiation prediction.
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