An Improved Hybrid Approach for Daily Electricity Peak Demand Forecasting during Disrupted Situations: A Case Study of COVID-19 Impact in Thailand
Accurate electricity demand forecasting is essential for global energy security, reducing costs, ensuring grid stability, and informing decision making in the energy sector. Disruptions often lead to unpredictable demand shifts, posing greater challenges for short-term load forecasting. Understandin...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/1/78 |
| _version_ | 1797358895176351744 |
|---|---|
| author | Lalitpat Aswanuwath Warut Pannakkong Jirachai Buddhakulsomsiri Jessada Karnjana Van-Nam Huynh |
| author_facet | Lalitpat Aswanuwath Warut Pannakkong Jirachai Buddhakulsomsiri Jessada Karnjana Van-Nam Huynh |
| author_sort | Lalitpat Aswanuwath |
| collection | DOAJ |
| description | Accurate electricity demand forecasting is essential for global energy security, reducing costs, ensuring grid stability, and informing decision making in the energy sector. Disruptions often lead to unpredictable demand shifts, posing greater challenges for short-term load forecasting. Understanding electricity demand patterns during a pandemic offers insights into handling future disruptions. This study aims to develop an effective forecasting model for daily electricity peak demand, which is crucial for managing potential disruptions. This paper proposed a hybrid approach to address scenarios involving both government intervention and non-intervention, utilizing integration methods such as stepwise regression, similar day selection-based day type criterion, variational mode decomposition, empirical mode decomposition, fast Fourier transform, and neural networks with grid search optimization for the problem. The electricity peak load data in Thailand during the year of the COVID-19 situation is used as a case study to demonstrate the effectiveness of the approach. To enhance the flexibility and adaptability of the approach, the new criterion of separating datasets and the new criterion of similar day selection are proposed to perform one-day-ahead forecasting with rolling datasets. Computational analysis confirms the method’s effectiveness, adaptability, reduced input, and computational efficiency, rendering it a practical choice for daily electricity peak demand forecasting, especially in disrupted situations. |
| first_indexed | 2024-03-08T15:08:50Z |
| format | Article |
| id | doaj.art-ca7c4cd885a0487f8d128e239327956b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T15:08:50Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ca7c4cd885a0487f8d128e239327956b2024-01-10T14:55:42ZengMDPI AGEnergies1996-10732023-12-011717810.3390/en17010078An Improved Hybrid Approach for Daily Electricity Peak Demand Forecasting during Disrupted Situations: A Case Study of COVID-19 Impact in ThailandLalitpat Aswanuwath0Warut Pannakkong1Jirachai Buddhakulsomsiri2Jessada Karnjana3Van-Nam Huynh4School of Manufacturing Systems and Mechanical Engineering (MSME), Sirindhorn International Institute of Technology (SIIT), Thammasat University, 99 Moo 18, Paholyothin Road, Khlong Nueng, Khlong Luang 12120, Pathum Thani, ThailandSchool of Manufacturing Systems and Mechanical Engineering (MSME), Sirindhorn International Institute of Technology (SIIT), Thammasat University, 99 Moo 18, Paholyothin Road, Khlong Nueng, Khlong Luang 12120, Pathum Thani, ThailandSchool of Manufacturing Systems and Mechanical Engineering (MSME), Sirindhorn International Institute of Technology (SIIT), Thammasat University, 99 Moo 18, Paholyothin Road, Khlong Nueng, Khlong Luang 12120, Pathum Thani, ThailandNational Electronics and Computer Technology Center (NECTEC), National Science and Technology Development Agency (NSTDA), 112 Thailand Science Park (TSP), Paholyothin Road, Khlong Nueng, Khlong Luang 12120, Pathum Thani, ThailandSchool of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Ishikawa, JapanAccurate electricity demand forecasting is essential for global energy security, reducing costs, ensuring grid stability, and informing decision making in the energy sector. Disruptions often lead to unpredictable demand shifts, posing greater challenges for short-term load forecasting. Understanding electricity demand patterns during a pandemic offers insights into handling future disruptions. This study aims to develop an effective forecasting model for daily electricity peak demand, which is crucial for managing potential disruptions. This paper proposed a hybrid approach to address scenarios involving both government intervention and non-intervention, utilizing integration methods such as stepwise regression, similar day selection-based day type criterion, variational mode decomposition, empirical mode decomposition, fast Fourier transform, and neural networks with grid search optimization for the problem. The electricity peak load data in Thailand during the year of the COVID-19 situation is used as a case study to demonstrate the effectiveness of the approach. To enhance the flexibility and adaptability of the approach, the new criterion of separating datasets and the new criterion of similar day selection are proposed to perform one-day-ahead forecasting with rolling datasets. Computational analysis confirms the method’s effectiveness, adaptability, reduced input, and computational efficiency, rendering it a practical choice for daily electricity peak demand forecasting, especially in disrupted situations.https://www.mdpi.com/1996-1073/17/1/78hybrid approachdaily peak load forecastingdisrupted situationVMDEDMFFT |
| spellingShingle | Lalitpat Aswanuwath Warut Pannakkong Jirachai Buddhakulsomsiri Jessada Karnjana Van-Nam Huynh An Improved Hybrid Approach for Daily Electricity Peak Demand Forecasting during Disrupted Situations: A Case Study of COVID-19 Impact in Thailand Energies hybrid approach daily peak load forecasting disrupted situation VMD EDM FFT |
| title | An Improved Hybrid Approach for Daily Electricity Peak Demand Forecasting during Disrupted Situations: A Case Study of COVID-19 Impact in Thailand |
| title_full | An Improved Hybrid Approach for Daily Electricity Peak Demand Forecasting during Disrupted Situations: A Case Study of COVID-19 Impact in Thailand |
| title_fullStr | An Improved Hybrid Approach for Daily Electricity Peak Demand Forecasting during Disrupted Situations: A Case Study of COVID-19 Impact in Thailand |
| title_full_unstemmed | An Improved Hybrid Approach for Daily Electricity Peak Demand Forecasting during Disrupted Situations: A Case Study of COVID-19 Impact in Thailand |
| title_short | An Improved Hybrid Approach for Daily Electricity Peak Demand Forecasting during Disrupted Situations: A Case Study of COVID-19 Impact in Thailand |
| title_sort | improved hybrid approach for daily electricity peak demand forecasting during disrupted situations a case study of covid 19 impact in thailand |
| topic | hybrid approach daily peak load forecasting disrupted situation VMD EDM FFT |
| url | https://www.mdpi.com/1996-1073/17/1/78 |
| work_keys_str_mv | AT lalitpataswanuwath animprovedhybridapproachfordailyelectricitypeakdemandforecastingduringdisruptedsituationsacasestudyofcovid19impactinthailand AT warutpannakkong animprovedhybridapproachfordailyelectricitypeakdemandforecastingduringdisruptedsituationsacasestudyofcovid19impactinthailand AT jirachaibuddhakulsomsiri animprovedhybridapproachfordailyelectricitypeakdemandforecastingduringdisruptedsituationsacasestudyofcovid19impactinthailand AT jessadakarnjana animprovedhybridapproachfordailyelectricitypeakdemandforecastingduringdisruptedsituationsacasestudyofcovid19impactinthailand AT vannamhuynh animprovedhybridapproachfordailyelectricitypeakdemandforecastingduringdisruptedsituationsacasestudyofcovid19impactinthailand AT lalitpataswanuwath improvedhybridapproachfordailyelectricitypeakdemandforecastingduringdisruptedsituationsacasestudyofcovid19impactinthailand AT warutpannakkong improvedhybridapproachfordailyelectricitypeakdemandforecastingduringdisruptedsituationsacasestudyofcovid19impactinthailand AT jirachaibuddhakulsomsiri improvedhybridapproachfordailyelectricitypeakdemandforecastingduringdisruptedsituationsacasestudyofcovid19impactinthailand AT jessadakarnjana improvedhybridapproachfordailyelectricitypeakdemandforecastingduringdisruptedsituationsacasestudyofcovid19impactinthailand AT vannamhuynh improvedhybridapproachfordailyelectricitypeakdemandforecastingduringdisruptedsituationsacasestudyofcovid19impactinthailand |