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  • Open Access


    A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

    Xing Deng1,2, Feipeng Da1,*, Haijian Shao2, Xia Wang3

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

    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 analysis to save information that… More > Graphic Abstract

    A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas

  • Open Access


    Key Optimization Issues for Renewable Energy Systems under Carbon-Peaking and Carbon Neutrality Targets: Current States and Perspectives

    Bo Yang1, Zhengxun Guo1, Jingbo Wang1,*, Chao Duan2, Yaxing Ren3, Yixuan Chen4

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

    Abstract This article has no abstract. More >

  • Open Access


    The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of the Wind Power and Photovoltaic Energy

    Dandan Xu1, Haijian Shao1,*, Xing Deng1,2, Xia Wang3

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

    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. Moreover, the experiments demonstrated that… More >

  • Open Access


    Fuzzy Based MPPT and Solar Power Forecasting Using Artificial Intelligence

    G. Geethamahalakshmi1,*, N. Kalaiarasi2, D. Nageswari1

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

    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 the quantity of solar rays… More >

  • Open Access


    Inferential Statistics and Machine Learning Models for Short-Term Wind Power Forecasting

    Ming Zhang, Hongbo Li, Xing Deng*

    Energy Engineering, Vol.119, No.1, pp. 237-252, 2022, DOI:10.32604/EE.2022.017916

    Abstract The inherent randomness, intermittence and volatility of wind power generation compromise the quality of the wind power system, resulting in uncertainty in the system's optimal scheduling. As a result, it's critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation. Inferred statistics are utilized in this research to infer general features based on the selected information, confirming that there are differences between two forecasting categories: Forecast Category 1 (0–11 h ahead) and Forecast Category 2 (12–23 h ahead). In z-tests, the null hypothesis provides the corresponding quantitative findings. To verify the final performance of… More >

  • Open Access


    Deep Learning Approach with Optimizatized Hidden-Layers Topology for Short-Term Wind Power Forecasting

    Xing Deng1,2, Haijian Shao1,2,*

    Energy Engineering, Vol.117, No.5, pp. 279-287, 2020, DOI:10.32604/EE.2020.011619

    Abstract Recurrent neural networks (RNNs) as one of the representative deep learning methods, has restricted its generalization ability because of its indigestion hidden-layer information presentation. In order to properly handle of hidden-layer information, directly reduce the risk of over-fitting caused by too many neuron nodes, as well as realize the goal of streamlining the number of hidden layer neurons, and then improve the generalization ability of RNNs, the hidden-layer information of RNNs is precisely analyzed by using the unsupervised clustering methods, such as Kmeans, Kmeans++ and Iterative self-organizing data analysis (Isodata), to divide the similarity of raw data points, and maps… More >

  • Open Access


    Wind Power Forecasting Methods Based on Deep Learning: A Survey

    Xing Deng1, 2, Haijian Shao1, *, Chunlong Hu1, Dengbiao Jiang1, Yingtao Jiang3

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.1, pp. 273-301, 2020, DOI:10.32604/cmes.2020.08768

    Abstract Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of… More >

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