Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction

Prince Waqas Khan, Yongjun Kim, Yung-Cheol Byun and Sang-Joon Lee
Additional contact information
Prince Waqas Khan: Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea
Yongjun Kim: 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

Energies, 2021, vol. 14, issue 21, 1-22

Abstract: Modern computing resources, including machine learning-based techniques, are used to maintain stability between the demand and supply of electricity. Machine learning is widely used for the prediction of energy consumption. The researchers present several artificial intelligence and machine learning-based methods to improve the prediction accuracy of energy consumption. However, the discrepancy between actual energy consumption and predicted energy consumption is still challenging. Various factors, including changes in weather, holidays, and weekends, affect prediction accuracy. This article analyses the overall prediction using error curve learning and a hybrid model. Actual energy consumption data of Jeju island, South Korea, has been used for experimental purposes. We have used a hybrid ML model consisting of Catboost, Xgboost, and Multi-layer perceptron for the prediction. Then we analyze the factors that affect the week-ahead (WA) and 48 h prediction results. Mean error on weekdays is recorded as 2.78%, for weekends 2.79%, and for special days it is recorded as 4.28%. We took into consideration significant predicting errors and looked into the reasons behind those errors. Furthermore, we analyzed whether factors, such as a sudden change in temperature and typhoons, had an effect on energy consumption. Finally, the authors have considered the other factors, such as public holidays and weekends, to analyze the significant errors in the prediction. This study can be helpful for policymakers to make policies according to the error-causing factors.

Keywords: machine learning; energy consumption; energy prediction; hybrid model; error curve learning (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/21/7167/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/21/7167/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:21:p:7167-:d:670143

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().