Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources

Prince Waqas Khan, Yung-Cheol Byun, Sang-Joon Lee, Dong-Ho Kang, Jin-Young Kang and Hae-Su Park
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Prince Waqas Khan: Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea
Yung-Cheol Byun: Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea
Sang-Joon Lee: Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea
Dong-Ho Kang: Power Technology Development Team, HODI, Mapo Ssangyong Geum Building 3f, Mapo-gu, Seoul 04178, Korea
Jin-Young Kang: Jeju Regional Headquarter, Korea Power Exchange, 81, Ora-NamRo, Jeju 63144, Korea
Hae-Su Park: Jeju Regional Headquarter, Korea Power Exchange, 81, Ora-NamRo, Jeju 63144, Korea

Energies, 2020, vol. 13, issue 18, 1-16

Abstract: In today’s world, renewable energy sources are increasingly integrated with nonrenewable energy sources into electric grids and pose new challenges because of their intermittent and variable nature. Energy prediction using soft-computing techniques plays a vital role in addressing these challenges. As electricity consumption is closely linked to other energy sources such as natural gas and oil, forecasting electricity consumption is essential for making national energy policies. In this paper, we utilize various data mining techniques, including preprocessing historical load data and the load time series’s characteristics. We analyzed the power consumption trends from renewable energy sources and nonrenewable energy sources and combined them. A novel machine learning-based hybrid approach, combining multilayer perceptron (MLP), support vector regression (SVR), and CatBoost, is proposed in this paper for power forecasting. A thorough comparison is made, taking into account the results obtained using other prediction methods.

Keywords: renewable energy; nonrenewable energy; energy prediction; soft-computing; machine learning; wind energy; solar energy; time series; CatBoost; multilayer perceptron; support vector regression; hybrid model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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