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Brno University of Technology, Czech Republic

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National Taiwan University of Science and Technology, Taiwan, Province of China

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Institute of Medical Technology and Equipment, Poland

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Hongik University, Korea

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Ca’ Foscari University of Venice, Italy

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Ton Duc Thang University, Vietnam

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Wroclaw University of Science and Technology, Poland

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The University of Edinburgh, United Kingdom

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Technical University of Cluj Napoca, Romania

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Posts and Telecommunications Institute of Technology, Ho Chi Minh City, Vietnam

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Henan Polytechnic University, China

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Ton Duc Thang University, Vietnam

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University of Pardubice, Czech Republic

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Delhi Technological University, India

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Ho Chi Minh City University of Technology and Education, Vietnam

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DIMES Department of University of Calabria, Italy

Gaojian Huang
Henan Polytechnic University, China

Nguyen Quang Sang
Ho Chi Minh City University of Transport, Vietnam

Anh-Tu Le
Ho Chi Minh City University of Transport, Vietnam

Phu Tran Tin
Ton Duc Thang University, Vietnam


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Data-Driven Hyperparameter Optimized Extreme Gradient Boosting Machine Learning Model for Solar Radiation Forecasting

Kumari Namrata, Mantosh Kumar, Nishant Kumar

DOI: 10.15598/aeee.v20i4.4650


Abstract

The uncertainty of the non-conventional sources especially solar energy caused due to spatio-temporal factors like temperature, pressure, relative humidity etc. is continuously disrupting the productivity and reliability of an integrated power system which motivates the researcher or energy industry for strategic forecasting solutions to enhance the proper scheduling and control of solar generation power plants. Several studies have been carried out; but still the objective of achieving accurate forecasting dependent on the spatio-temporal features is not achieved. To address this critical forecasting issue in this research article a hyper parametric tuning of the Extreme Gradient Boosting (XGB) machine learning model has been carried out using two met heuristic algorithms: Moth Flame Optimization (MFO) and Grey Wolf Optimization (GWO). The dataset comprises five years of metrological attributes collected from the National Renewable Energy Laboratory (NREL) for analysis. The validation of the proposed model has been done based on the five statistical errors: Max Error (ME), Mean Absolute Error (MAE), Coefficient of Determination (R^2), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The regressive assessment of all three models has confirmed that the XGB-MFO model outperformed the others as showing the highest R^2 score of 0.9337, 0.9011, 0.8744 and lowest RMSE values of 76.29 Wcm^{-2}, 41.90 Wm^{-2} and 95.94Wm^{-2} for Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI) respectively which ensures the proposed model implementation for the prediction and production of solar power.

Keywords


Extreme Gradient Boosting; forecasting; Grey Wolf Optimization; Moth Flame Optimization; solar irradiance.

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