Xinjian Li¹, Yu Zhang^1,2,*, Zewen Wang¹, Zhenyun Song¹

Energy Engineering, Vol.122, No.11, pp. 4603-4619, 2025, DOI:10.32604/ee.2025.065799 - 27 October 2025

Abstract Power prediction has been critical in large-scale wind power grid connections. However, traditional wind power prediction methods have long suffered from problems, for instance low prediction accuracy and poor reliability. For this purpose, a hybrid prediction model (VMD-LSTM-Attention) has been proposed, which integrates the variational modal decomposition (VMD), the long short-term memory (LSTM), and the attention mechanism (Attention), and has been optimized by improved dung beetle optimization algorithm (IDBO). Firstly, the algorithm’s performance has been significantly enhanced through the implementation of three key strategies, namely the elite group strategy of the Logistic-Tent map, the nonlinear… More >

Mehmet Balci^1,*, Emrah Dokur², Ugur Yuzgec³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 945-968, 2025, DOI:10.32604/cmes.2025.067851 - 31 July 2025

Abstract Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems. Nevertheless, the inherently variable nature of wind and the intricacy of high-dimensional datasets pose major obstacles to reliable forecasting. To address these difficulties, this study presents an innovative hybrid method for short-term wind power prediction by combining a Long Short-Term Memory (LSTM) network with a Single Candidate Optimizer (SCO) algorithm. In contrast to conventional techniques that rely on random parameter initialization, the proposed LSTM-SCO framework leverages the distinctive capability of SCO to work More > Graphic Abstract

S. M. Rezaul Karim^1,2, Md. Shouquat Hossain^1,3, Khadiza Akter¹, Debasish Sarker⁴, Md. Moniul Kabir ², Mamdouh Assad^5,*

Energy Engineering, Vol.122, No.8, pp. 3041-3054, 2025, DOI:10.32604/ee.2025.066867 - 24 July 2025

Abstract Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems. This research explores the influence of dataset size on the accuracy and reliability of regression models for solar power prediction, contributing to better forecasting methods. The study analyzes data from two solar panels, aSiMicro03036 and aSiTandem72-46, over 7, 14, 17, 21, 28, and 38 days, with each dataset comprising five independent and one dependent parameter, and split 80–20 for training and testing. Results indicate… More > Graphic Abstract

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 >

Zhebin Sun¹, Wei Wang¹, Mingxuan Du², Tao Liang¹, Yang Liu¹, Hailong Fan³, Cuiping Li², Xingxu Zhu², Junhui Li^2,*

Energy Engineering, Vol.122, No.6, pp. 2155-2175, 2025, DOI:10.32604/ee.2025.062218 - 29 May 2025

Abstract Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation. This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition (VMD) and Channel Attention Mechanism. First, Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power. Second, the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition (VMD). Finally, the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM… More >

Feng Guo, Chen Yang^*, Dezhong Xia, Jingxiang Xu

Energy Engineering, Vol.122, No.5, pp. 1975-1999, 2025, DOI:10.32604/ee.2025.062035 - 25 April 2025

Abstract Short-term photovoltaic (PV) power forecasting plays a crucial role in enhancing the stability and reliability of power grid scheduling. To address the challenges posed by complex environmental variables and difficulties in modeling temporal features in PV power prediction, a short-term PV power forecasting method based on an improved CNN-LSTM and cascade learning strategy is proposed. First, Pearson correlation coefficients and mutual information are used to select representative features, reducing the impact of redundant features on model performance. Then, the CNN-LSTM network is designed to extract local features using CNN and learn temporal dependencies through LSTM,… More > Graphic Abstract

Jifeng Liang¹, Qiang Wang², Leibao Wang¹, Ziwei Zhang³, Yonghui Sun^3,*, Hongzhu Tao⁴, Xiaofei Li⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 945-965, 2025, DOI:10.32604/cmes.2025.062315 - 11 April 2025

Abstract Wind power generation is subjected to complex and variable meteorological conditions, resulting in intermittent and volatile power generation. Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations. This enhances the efficiency of wind power integration into the grid. It allows grid operators to anticipate and mitigate the impact of wind power fluctuations, significantly improving the resilience of wind farms and the overall power grid. Furthermore, it assists wind farm operators in optimizing the management of power generation facilities and reducing… More > Graphic Abstract

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