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Optimal electrical load forecasting for hybrid renewable resources through a hybrid memetic cuckoo search approach

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Abstract

Although renewable energy grows to be progressively trendier in the universal power grid, enhancing the precision or accuracy is a crucial task. Therefore, managing, operating and planning of modern power systems become difficult in case of renewable energy load forecasting. Due to the intermittent and disordered nature of renewable resources, load forecasting becomes a complicated task. The renewable energy system introduces various approaches to enhance load forecasting accuracy. This paper describes the technofeasibility and the optimal design of HRE resources such as photovoltaic, wind turbine, biogasifiers, and battery to satisfy all power demand optimally using a hybrid algorithm. The hybrid algorithm is the grouping of DRNN, memetic and cuckoo search algorithm to form a proposed HMCS-DRNN approach. This proposed approach is employed to provide better optimization performances, and apart from precision and stability in load forecasting, the HMCS-DRNN approach offers the predicted result with better efficiency and minimum error value rate. The efficiency of the proposed approach articulates by calculating the statistical measure regarding RMSE and MAPE, respectively. The simulation results describe that the performances of the HMCS algorithm provide better optimization results on various 30 unconstrained benchmark functions.

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Authors and Affiliations

  1. School of Control Science and Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, People’s Republic of China

    Shweta Sengar & Xiaodong Liu

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  1. Shweta Sengar
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  2. Xiaodong Liu
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Correspondence to Xiaodong Liu.

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Sengar, S., Liu, X. Optimal electrical load forecasting for hybrid renewable resources through a hybrid memetic cuckoo search approach. Soft Comput 24, 13099–13114 (2020). https://doi.org/10.1007/s00500-020-04727-9

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