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

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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
- Intra-Hour PV Power Forecasting Technique Based on Total-Sky Images
- 中国电机工程学会电力与能源系统学报(英文) 2026年12卷第1期页码：210-219
- 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
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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Songjie Zhang, Zhekang Dong, Donglian Qi, 等. Intra-Hour PV Power Forecasting Technique Based on Total-Sky Images[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(1):210-219.

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.
Songjie Zhang, Zhekang Dong, Donglian Qi, 等. Intra-Hour PV Power Forecasting Technique Based on Total-Sky Images[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(1):210-219. DOI： 10.17775/CSEEJPES.2022.07770.

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.

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