Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature Engineering
Short-term load forecasting (STLF) plays a pivotal role in the electricity industry because it helps reduce, generate, and operate costs by balancing supply and demand. Recently, the challenge in STLF has been the load variation that occurs in each period, day, and seasonality. This work proposes th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/10/4882 |
| _version_ | 1797501817140019200 |
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| author | Pyae-Pyae Phyo Chawalit Jeenanunta |
| author_facet | Pyae-Pyae Phyo Chawalit Jeenanunta |
| author_sort | Pyae-Pyae Phyo |
| collection | DOAJ |
| description | Short-term load forecasting (STLF) plays a pivotal role in the electricity industry because it helps reduce, generate, and operate costs by balancing supply and demand. Recently, the challenge in STLF has been the load variation that occurs in each period, day, and seasonality. This work proposes the bagging ensemble combining two machine learning (ML) models—linear regression (LR) and support vector regression (SVR). For comparative analysis, the performance of the proposed model is evaluated and compared with three advanced deep learning (DL) models, namely, the deep neural network (DNN), long short-term memory (LSTM), and hybrid convolutional neural network (CNN)+LSTM models. These models are trained and tested on the data collected from the Electricity Generating Authority of Thailand (EGAT) with four different input features. The forecasting performance is measured considering mean absolute percentage error (<i>MAPE</i>), mean absolute error (<i>MAE</i>), and mean squared error (<i>MSE</i>) parameters. Using several input features, experimental results show that the integrated model provides better accuracy than others. Therefore, it can be revealed that our approach could improve accuracy using different data in different forecasting fields. |
| first_indexed | 2024-03-10T03:24:03Z |
| format | Article |
| id | doaj.art-0c43a496e6b645bc99fcafa1724124c1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:24:03Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0c43a496e6b645bc99fcafa1724124c12023-11-23T09:54:34ZengMDPI AGApplied Sciences2076-34172022-05-011210488210.3390/app12104882Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature EngineeringPyae-Pyae Phyo0Chawalit Jeenanunta1Department of Electrical Engineering, Eindhoven University of Technology, 5611 AZ Eindhoven, The NetherlandsSchool of Management Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, ThailandShort-term load forecasting (STLF) plays a pivotal role in the electricity industry because it helps reduce, generate, and operate costs by balancing supply and demand. Recently, the challenge in STLF has been the load variation that occurs in each period, day, and seasonality. This work proposes the bagging ensemble combining two machine learning (ML) models—linear regression (LR) and support vector regression (SVR). For comparative analysis, the performance of the proposed model is evaluated and compared with three advanced deep learning (DL) models, namely, the deep neural network (DNN), long short-term memory (LSTM), and hybrid convolutional neural network (CNN)+LSTM models. These models are trained and tested on the data collected from the Electricity Generating Authority of Thailand (EGAT) with four different input features. The forecasting performance is measured considering mean absolute percentage error (<i>MAPE</i>), mean absolute error (<i>MAE</i>), and mean squared error (<i>MSE</i>) parameters. Using several input features, experimental results show that the integrated model provides better accuracy than others. Therefore, it can be revealed that our approach could improve accuracy using different data in different forecasting fields.https://www.mdpi.com/2076-3417/12/10/4882accuracybagging ensembledeep learningmachine learningshort-term load forecasting |
| spellingShingle | Pyae-Pyae Phyo Chawalit Jeenanunta Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature Engineering Applied Sciences accuracy bagging ensemble deep learning machine learning short-term load forecasting |
| title | Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature Engineering |
| title_full | Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature Engineering |
| title_fullStr | Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature Engineering |
| title_full_unstemmed | Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature Engineering |
| title_short | Advanced ML-Based Ensemble and Deep Learning Models for Short-Term Load Forecasting: Comparative Analysis Using Feature Engineering |
| title_sort | advanced ml based ensemble and deep learning models for short term load forecasting comparative analysis using feature engineering |
| topic | accuracy bagging ensemble deep learning machine learning short-term load forecasting |
| url | https://www.mdpi.com/2076-3417/12/10/4882 |
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