Intra-Hour PV Power Forecasting Technique Based on Total-Sky Images

Songjie Zhang; Zhekang Dong; Donglian Qi; Minghao Wang; Zhao Xu; Yifeng Han; Yunfeng Yan; Zhenming Li

doi:10.17775/CSEEJPES.2022.07770

您当前的位置：

首页 >

文章列表页 >

Intra-Hour PV Power Forecasting Technique Based on Total-Sky Images

Regular Papers | 更新时间：2026-03-25

- Intra-Hour PV Power Forecasting Technique Based on Total-Sky Images
- CSEE Journal of Power and Energy Systems Vol. 12, Issue 1, Pages: 210-219(2026)
- 作者机构：
  
  1. College of Electrical Engineering, Zhejiang University,Hangzhou,China
  2. Department of Electrical Engineering, Research Institute for Smart Energy, The Hong Kong Polytechnic University, Kowloon, HKSAR,China
  3. School of Electronics and Information, Hangzhou Dianzi University,Hangzhou,China
  4. Hainan Institute of Zhejiang University,Hainan,China
  5. College of Electrical Electronic Engineering, Zhejiang University,Hangzhou,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2022.07770
  CLC：
- Published：2026
- 稿件说明：
移动端阅览
Songjie Zhang, Zhekang Dong, Donglian Qi, et al. Intra-Hour PV Power Forecasting Technique Based on Total-Sky Images[J]. CSEE Journal of Power and Energy Systems, 2026, 12(1): 210-219.
DOI：

Songjie Zhang, Zhekang Dong, Donglian Qi, et al. Intra-Hour PV Power Forecasting Technique Based on Total-Sky Images[J]. CSEE Journal of Power and Energy Systems, 2026, 12(1): 210-219. DOI： 10.17775/CSEEJPES.2022.07770.

摘要

Abstract

Clouds are one of the leading causes of sun shading

which reduces the direct horizontal irradiance and curtails the photovoltaic (PV) power. It is critical to estimate cloud cover to accurately predict PV generation within a very short horizon (second/minute). To achieve the precise forecasting of cloud cover

an image preprocessing method based on total-sky images is proposed to remove the interference and address the image edge distortion issue. An optimal threshold estimation method is further designed to achieve higher cloud identification precision. Considering the cloud's meteorological properties

a random hypersurface model (RHM) based on the Gaussian mixture probability hypothesis density (GM-PHD) filter is applied to track the cloud. The GM-PHD can track the rotation and diffusion of clouds

which helps to estimate sun-cloud collision. Furthermore

a hybrid autoregressive integrated moving average (ARIMA) and backpropagation (BP) neural network-based model is applied for intra-hour PV power forecasting. The experiment results demonstrate that the proposed cloud-tracking-based PV power forecasting model can capture the ramp behavior of PV power

improving forecasting precision.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Online Probabilistic Load Forecasts Considering Data Gaps

Data-Driven Approach for Analyzing and Correlating Energy Market Products: Case Studies of Denmark and Croatia

Investigating Black-Box Model for Wind Power Forecasting Using Local Interpretable Model-Agnostic Explanations Algorithm

Very Short-Term Forecasting of Distributed PV Power Using GSTANN

A Temporal Convolutional Network Based Hybrid Model for Short-Term Electricity Price Forecasting

Related Author

Pengfei Zhao

Weihao Hu

Di Cao

Longcheng Dai

Qi Huang

Zhe Chen

Domagoj Badanjak

Ivan Pavić

Related Institution

School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China

Electric Power Research Institute of State Grid Ningxia Electric Power Co., Ltd.

Chinese Academy of Science and Technolgy for Development

Department of Energy Technology, Aalborg University, Aalborg

Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb

AI问答

⁰