Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting

Prince Waqas Khan, Yung-Cheol Byun, Sang-Joon Lee and Namje 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
Namje Park: Department of Computer Education, Teachers College, Jeju National University, Jeju City 63243, Korea

Energies, 2020, vol. 13, issue 11, 1-23

Abstract: The ongoing upsurge of deep learning and artificial intelligence methodologies manifest incredible accomplishment in a broad scope of assessing issues in different industries, including the energy sector. In this article, we have presented a hybrid energy forecasting model based on machine learning techniques. It is based on the three machine learning algorithms: extreme gradient boosting, categorical boosting, and random forest method. Usually, machine learning algorithms focus on fine-tuning the hyperparameters, but our proposed hybrid algorithm focuses on the preprocessing using feature engineering to improve forecasting. We also focus on the way to impute a significant data gap and its effect on predicting. The forecasting exactness of the proposed model is evaluated using the regression score, and it depicts that the proposed model, with an R-squared of 0.9212, is more accurate than existing models. For the testing purpose of the proposed energy consumption forecasting model, we have used the actual dataset of South Korea’s hourly energy consumption. The proposed model can be used for any other dataset as well. This research result will provide a scientific premise for the strategy modification of energy supply and demand.

Keywords: deep learning; energy forecasting; machine learning; feature engineering; time series; XGBoost; CatBoost; random forest; 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 (6)

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