Chen Pan, ChangGyoon Lim^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3747-3781, 2025, DOI:10.32604/cmes.2025.073389 - 23 December 2025

Abstract Solar forecasting using ground-based sky image offers a promising approach to reduce uncertainty in photovoltaic (PV) power generation. However, existing methods often rely on deterministic predictions that lack diversity, making it difficult to capture the inherently stochastic nature of cloud movement. To address this limitation, we propose a new two-stage probabilistic forecasting framework. In the first stage, we introduce I-GPT, a multiscale physics-constrained generative model for stochastic sky image prediction. Given a sequence of past sky images, I-GPT uses a Transformer-based VQ-VAE. It also incorporates multi-scale physics-informed recurrent units (Multi-scale PhyCell) and dynamically weighted fuses… More >

Namal Rathnayake¹, Jeevani Jayasinghe^2,3, Rashmi Semasinghe², Upaka Rathnayake^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2287-2305, 2025, DOI:10.32604/cmes.2025.064464 - 30 May 2025

Abstract In this study, a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions. Using data on wind speed, air temperature, nacelle position, and actual power, lagged features were generated to capture temporal dependencies. Among 24 evaluated models, the ensemble bagging approach achieved the best performance, with R² values of 0.89 at 0 min and 0.75 at 60 min. Shapley Additive exPlanations (SHAP) analysis revealed that while wind speed is the primary driver for short-term predictions, air temperature and nacelle position become more More >

Ahmed A. Ewees^1,2,*, Mohammed A. A. Al-Qaness³, Ali Alshahrani¹, Mohamed Abd Elaziz⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2287-2303, 2025, DOI:10.32604/cmc.2025.061320 - 16 April 2025

Abstract Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output. This enhances the efficiency and reliability of renewable energy systems. Forecasting approaches inform energy management strategies, reduce reliance on fossil fuels, and support the broader transition to sustainable energy solutions. The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis. This research advances an optimized Multilayer Perceptron (MLP) model using recently proposed metaheuristic optimization algorithms, namely the Fire Hawk Optimizer (FHO)… More >

Zhaowei Yang¹, Bo Yang^2,*, Wenqi Liu¹, Miwei Li², Jiarong Wang², Lin Jiang³, Yiyan Sang⁴, Zhenning Pan⁵

Energy Engineering, Vol.122, No.2, pp. 405-430, 2025, DOI:10.32604/ee.2025.059515 - 31 January 2025

Abstract Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids. Although numerous studies have employed various methods to forecast wind power, there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction. To improve the accuracy of short-term wind power forecast, this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer, which is based on seasonal and trend decomposition using LOESS (STL) and iTransformer model optimized by improved arithmetic optimization algorithm (IAOA).… More >

Farhan Ullah¹, Xuexia Zhang^1,*, Mansoor Khan², Muhammad Abid^3,*, Abdullah Mohamed⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3373-3395, 2024, DOI:10.32604/cmc.2024.048656 - 15 May 2024

Abstract Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows. Traditional approaches frequently struggle with complex data and non-linear connections. This article presents a novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts. The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-Era Retrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms using in-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model, while a temporal convolutional network handles time-series complexities and data… More >

Chenglian Ma¹, Rui Han¹, Zhao An^2,*, Tianyu Hu², Meizhu Jin²

Energy Engineering, Vol.121, No.5, pp. 1245-1261, 2024, DOI:10.32604/ee.2024.046644 - 30 April 2024

Abstract Precise forecasting of solar power is crucial for the development of sustainable energy systems. Contemporary forecasting approaches often fail to adequately consider the crucial role of weather factors in photovoltaic (PV) power generation and encounter issues such as gradient explosion or disappearance when dealing with extensive time-series data. To overcome these challenges, this research presents a cutting-edge, multi-stage forecasting method called D-Informer. This method skillfully merges the differential transformation algorithm with the Informer model, leveraging a detailed array of meteorological variables and historical PV power generation records. The D-Informer model exhibits remarkable superiority over competing… More > Graphic Abstract

Xing Deng^1,2, Feipeng Da^1,*, Haijian Shao², Xia Wang³

Energy Engineering, Vol.120, No.2, pp. 385-408, 2023, DOI:10.32604/ee.2023.023480 - 29 November 2022

Abstract Photovoltaic power generating is one of the primary methods of utilizing solar energy resources, with large-scale photovoltaic grid-connected power generation being the most efficient way to fully utilize solar energy. In order to provide reference strategies for pertinent researchers as well as potential implementation, this paper tries to provide a survey investigation and technical analysis of machine learning-related approaches, statistical approaches and optimization techniques for solar power generation and forecasting. Deep learning-related methods, in particular, can theoretically handle arbitrary nonlinear transformations through proper model structural design, such as hidden layer topology optimization and objective function More > Graphic Abstract

Bo Yang¹, Zhengxun Guo¹, Jingbo Wang^1,*, Chao Duan², Yaxing Ren³, Yixuan Chen⁴

Energy Engineering, Vol.119, No.5, pp. 1789-1795, 2022, DOI:10.32604/ee.2022.022217 - 21 July 2022

Abstract This article has no abstract. More >

Dandan Xu¹, Haijian Shao^1,*, Xing Deng^1,2, Xia Wang³

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 567-597, 2022, DOI:10.32604/cmes.2022.019245 - 14 March 2022

Abstract As wind and photovoltaic energy become more prevalent, the optimization of power systems is becoming increasingly crucial. The current state of research in renewable generation and power forecasting technology, such as wind and photovoltaic power (PV), is described in this paper, with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting. The methods for forecasting wind power and PV production. The physical model, statistical learning method, and machine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production. More >

G. Geethamahalakshmi^1,*, N. Kalaiarasi², D. Nageswari¹

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1667-1685, 2022, DOI:10.32604/iasc.2022.022728 - 09 December 2021

Abstract Solar energy is the radiant heat and light energy harvested by ultra violet rays to convert into electrical Direct Current (DC). The solar energy stood ahead of other renewable energy as it can produce a constant level of alternating current over the year with minimal harmonic distortions. The renewable energy attracts the energy harvesters as there is rise of deficiency of carbon and reduction of efficiency in thermal energy generation. The concerns associated with the solar power generation are the fluctuation in the generated direct current due to the displacement of sun and deviation in… More >