Dayan Sun¹, Xiao Cao^2,*, Zhifeng Liang¹, Junrong Xia², Yuqi Wang³

Energy Engineering, Vol.122, No.8, pp. 3329-3346, 2025, DOI:10.32604/ee.2025.066341 - 24 July 2025

Abstract Photovoltaic (PV) power generation is undergoing significant growth and serves as a key driver of the global energy transition. However, its intermittent nature, which fluctuates with weather conditions, has raised concerns about grid stability. Accurate PV power prediction has been demonstrated as crucial for power system operation and scheduling, enabling power slope control, fluctuation mitigation, grid stability enhancement, and reliable data support for secure grid operation. However, existing prediction models primarily target centralized PV plants, largely neglecting the spatiotemporal coupling dynamics and output uncertainties inherent to distributed PV systems. This study proposes a novel Spatio-Temporal… More >

Penghui Liu^1,*, Tianyu Yang¹, Peng Zhang², Peiyuan Zou³

Energy Engineering, Vol.122, No.4, pp. 1387-1402, 2025, DOI:10.32604/ee.2025.062627 - 31 March 2025

Abstract The photovoltaic (PV) output process is inherently complex, often disrupted by a multitude of meteorological factors, while conventional detection methods at PV power stations prove inadequate, compromising prediction accuracy. To address this challenge, this paper introduces a power prediction method that leverages modal switching (MS), weight factor adjustment (WFA), and parallel long short-term memory (PALSTM). Initially, historical PV power station data is categorized into distinct modes based on global horizontal irradiance and converted solar angles. Correlation analysis is then employed to evaluate the impact of various meteorological factors on PV power, selecting those with strong… More >

Qiang Wang¹, Hao Cheng², Wenrui Zhang^2,*, Guangxi Li³, Fan Xu², Dianhao Chen⁴, Haixiang Zang⁴

Energy Engineering, Vol.122, No.2, pp. 747-764, 2025, DOI:10.32604/ee.2025.059533 - 31 January 2025

Abstract Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources. However, the fluctuations and intermittency of photovoltaic (PV) power pose challenges for its extensive incorporation into power grids. Thus, enhancing the precision of PV power prediction is particularly important. Although existing studies have made progress in short-term prediction, issues persist, particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data. These factors hinder improvements in PV power prediction performance. To overcome these challenges, this paper… More >

Daixuan Zhou¹, Yujin Liu¹, Xu Wang², Fuxing Wang¹, Yan Jia^2,*

Energy Engineering, Vol.121, No.12, pp. 3573-3616, 2024, DOI:10.32604/ee.2024.055853 - 22 November 2024

Abstract With the increasing proportion of renewable energy in China’s energy structure, among which photovoltaic power generation is also developing rapidly. As the photovoltaic (PV) power output is highly unstable and subject to a variety of factors, it brings great challenges to the stable operation and dispatch of the power grid. Therefore, accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV energy. Currently, the short-term prediction of PV power has received extensive attention and research, but the accuracy and precision of the prediction have to be further improved. More > Graphic Abstract

Yuxuan Zhao^1,2,*, Bo Wang¹, Shu Wang¹, Wenjun Xu², Gang Ma²

Energy Engineering, Vol.121, No.12, pp. 3711-3733, 2024, DOI:10.32604/ee.2024.054032 - 22 November 2024

Abstract The output of photovoltaic power stations is significantly affected by environmental factors, leading to intermittent and fluctuating power generation. With the increasing frequency of extreme weather events due to global warming, photovoltaic power stations may experience drastic reductions in power generation or even complete shutdowns during such conditions. The integration of these stations on a large scale into the power grid could potentially pose challenges to system stability. To address this issue, in this study, we propose a network architecture based on VMD-KELM for predicting the power output of photovoltaic power plants during severe weather… More >

Yujin Liu¹, Zhenkai Zhang¹, Li Ma¹, Yan Jia^1,2,*, Weihua Yin³, Zhifeng Liu³

Energy Engineering, Vol.121, No.10, pp. 3019-3035, 2024, DOI:10.32604/ee.2024.052594 - 11 September 2024

Abstract Accurate short-term photovoltaic (PV) power prediction helps to improve the economic efficiency of power stations and is of great significance to the arrangement of grid scheduling plans. In order to improve the accuracy of PV power prediction further, this paper proposes a data cleaning method combining density clustering and support vector machine. It constructs a short-term PV power prediction model based on particle swarm optimization (PSO) optimized Long Short-Term Memory (LSTM) network. Firstly, the input features are determined using Pearson’s correlation coefficient. The feature information is clustered using density-based spatial clustering of applications with noise More >

Huizhi Gou^1,2,*, Yuncai Ning¹

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 803-822, 2021, DOI:10.32604/cmes.2021.015922 - 22 July 2021

Abstract Accurate photovoltaic (PV) power prediction can effectively help the power sector to make rational energy planning and dispatching decisions, promote PV consumption, make full use of renewable energy and alleviate energy problems. To address this research objective, this paper proposes a prediction model based on kernel principal component analysis (KPCA), modified cuckoo search algorithm (MCS) and deep convolutional neural networks (DCNN). Firstly, KPCA is utilized to reduce the dimension of the feature, which aims to reduce the redundant input vectors. Then using MCS to optimize the parameters of DCNN. Finally, the photovoltaic power forecasting method More >