Metaheuristic Extreme Learning Machine for Improving Performance of Electric Energy Demand Forecasting
Electric energy demand forecasting is very important for electric utilities to procure and supply electric energy for consumers sufficiently, safely, reliably, and continuously. Consequently, the processing time and accuracy of the forecast system are essential to consider when applying in real powe...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
|
| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/11/5/66 |
| _version_ | 1797500608264011776 |
|---|---|
| author | Sarunyoo Boriratrit Chitchai Srithapon Pradit Fuangfoo Rongrit Chatthaworn |
| author_facet | Sarunyoo Boriratrit Chitchai Srithapon Pradit Fuangfoo Rongrit Chatthaworn |
| author_sort | Sarunyoo Boriratrit |
| collection | DOAJ |
| description | Electric energy demand forecasting is very important for electric utilities to procure and supply electric energy for consumers sufficiently, safely, reliably, and continuously. Consequently, the processing time and accuracy of the forecast system are essential to consider when applying in real power system operations. Nowadays, the Extreme Learning Machine (ELM) is significant for forecasting as it provides an acceptable value of forecasting and consumes less computation time when compared with the state-of-the-art forecasting models. However, the result of electric energy demand forecasting from the ELM was unstable and its accuracy was increased by reducing overfitting of the ELM model. In this research, metaheuristic optimization combined with the ELM is proposed to increase accuracy and reduce the cause of overfitting of three forecasting models, composed of the Jellyfish Search Extreme Learning Machine (JS-ELM), the Harris Hawk Extreme Learning Machine (HH-ELM), and the Flower Pollination Extreme Learning Machine (FP-ELM). The actual electric energy demand datasets in Thailand were collected from 2018 to 2020 and used to test and compare the performance of the proposed and state-of-the-art forecasting models. The overall results show that the JS-ELM provides the best minimum root mean square error compared with the state-of-the-art forecasting models. Moreover, the JS-ELM consumes the appropriate processing time in this experiment. |
| first_indexed | 2024-03-10T03:06:16Z |
| format | Article |
| id | doaj.art-eb239c7bf68547308907ebcaeafd08e5 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-10T03:06:16Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-eb239c7bf68547308907ebcaeafd08e52023-11-23T10:33:31ZengMDPI AGComputers2073-431X2022-04-011156610.3390/computers11050066Metaheuristic Extreme Learning Machine for Improving Performance of Electric Energy Demand ForecastingSarunyoo Boriratrit0Chitchai Srithapon1Pradit Fuangfoo2Rongrit Chatthaworn3Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandDepartment of Electrical Engineering, KTH Royal Institute of Technology, 11428 Stockholm, SwedenProvincial Electricity Authority of Thailand (PEA), Bangkok 10900, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandElectric energy demand forecasting is very important for electric utilities to procure and supply electric energy for consumers sufficiently, safely, reliably, and continuously. Consequently, the processing time and accuracy of the forecast system are essential to consider when applying in real power system operations. Nowadays, the Extreme Learning Machine (ELM) is significant for forecasting as it provides an acceptable value of forecasting and consumes less computation time when compared with the state-of-the-art forecasting models. However, the result of electric energy demand forecasting from the ELM was unstable and its accuracy was increased by reducing overfitting of the ELM model. In this research, metaheuristic optimization combined with the ELM is proposed to increase accuracy and reduce the cause of overfitting of three forecasting models, composed of the Jellyfish Search Extreme Learning Machine (JS-ELM), the Harris Hawk Extreme Learning Machine (HH-ELM), and the Flower Pollination Extreme Learning Machine (FP-ELM). The actual electric energy demand datasets in Thailand were collected from 2018 to 2020 and used to test and compare the performance of the proposed and state-of-the-art forecasting models. The overall results show that the JS-ELM provides the best minimum root mean square error compared with the state-of-the-art forecasting models. Moreover, the JS-ELM consumes the appropriate processing time in this experiment.https://www.mdpi.com/2073-431X/11/5/66electricity forecastingExtreme Learning Machineimprovement modelmachine learningmetaheuristicJellyfish Search Optimization |
| spellingShingle | Sarunyoo Boriratrit Chitchai Srithapon Pradit Fuangfoo Rongrit Chatthaworn Metaheuristic Extreme Learning Machine for Improving Performance of Electric Energy Demand Forecasting Computers electricity forecasting Extreme Learning Machine improvement model machine learning metaheuristic Jellyfish Search Optimization |
| title | Metaheuristic Extreme Learning Machine for Improving Performance of Electric Energy Demand Forecasting |
| title_full | Metaheuristic Extreme Learning Machine for Improving Performance of Electric Energy Demand Forecasting |
| title_fullStr | Metaheuristic Extreme Learning Machine for Improving Performance of Electric Energy Demand Forecasting |
| title_full_unstemmed | Metaheuristic Extreme Learning Machine for Improving Performance of Electric Energy Demand Forecasting |
| title_short | Metaheuristic Extreme Learning Machine for Improving Performance of Electric Energy Demand Forecasting |
| title_sort | metaheuristic extreme learning machine for improving performance of electric energy demand forecasting |
| topic | electricity forecasting Extreme Learning Machine improvement model machine learning metaheuristic Jellyfish Search Optimization |
| url | https://www.mdpi.com/2073-431X/11/5/66 |
| work_keys_str_mv | AT sarunyooboriratrit metaheuristicextremelearningmachineforimprovingperformanceofelectricenergydemandforecasting AT chitchaisrithapon metaheuristicextremelearningmachineforimprovingperformanceofelectricenergydemandforecasting AT praditfuangfoo metaheuristicextremelearningmachineforimprovingperformanceofelectricenergydemandforecasting AT rongritchatthaworn metaheuristicextremelearningmachineforimprovingperformanceofelectricenergydemandforecasting |