Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions
The incorporation of weather variables is crucial in developing an effective demand forecasting model because electricity demand is strongly influenced by weather conditions. The dependence of demand on weather conditions may change with time during a day. Therefore, the time stamped weather informa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9528002/ |
| _version_ | 1811216480731136000 |
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| author | Dao H. Vu Kashem M. Muttaqi Ashish P. Agalgaonkar Arian Zahedmanesh Abdesselam Bouzerdoum |
| author_facet | Dao H. Vu Kashem M. Muttaqi Ashish P. Agalgaonkar Arian Zahedmanesh Abdesselam Bouzerdoum |
| author_sort | Dao H. Vu |
| collection | DOAJ |
| description | The incorporation of weather variables is crucial in developing an effective demand forecasting model because electricity demand is strongly influenced by weather conditions. The dependence of demand on weather conditions may change with time during a day. Therefore, the time stamped weather information is essential. In this paper, a multi-layer moving window approach is proposed to incorporate the significant weather variables, which are selected using Pearson and Spearman correlation techniques. The multi-layer moving window approach allows the layers to adjust their size to accommodate the weather variables based on their significance, which creates more flexibility and adaptability thereby improving the overall performance of the proposed approach. Furthermore, a recursive model is developed to forecast the demand in multi-step ahead. An electricity demand data for the state of New South Wales, Australia are acquired from the Australian Energy Market Operator and the associated results are reported in the paper. The results show that the proposed approach with dynamic incorporation of weather variables is promising for day-ahead and week-ahead load demand forecasting. |
| first_indexed | 2024-04-12T06:38:35Z |
| format | Article |
| id | doaj.art-f1b48e8b71fd44f9866e8cffdac47159 |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-04-12T06:38:35Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-f1b48e8b71fd44f9866e8cffdac471592022-12-22T03:43:47ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202022-01-011061552156210.35833/MPCE.2021.0002109528002Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather ConditionsDao H. Vu0Kashem M. Muttaqi1Ashish P. Agalgaonkar2Arian Zahedmanesh3Abdesselam Bouzerdoum4School of Electrical Computer and Telecommunications Engineering, University of Wollongong,Wollongong,AustraliaSchool of Electrical Computer and Telecommunications Engineering, University of Wollongong,Wollongong,AustraliaSchool of Electrical Computer and Telecommunications Engineering, University of Wollongong,Wollongong,AustraliaSchool of Electrical Computer and Telecommunications Engineering, University of Wollongong,Wollongong,AustraliaCollege of Science and Engineering, Hamad Bin Khalifa University,Information and Computing Technology Division,Doha,QatarThe incorporation of weather variables is crucial in developing an effective demand forecasting model because electricity demand is strongly influenced by weather conditions. The dependence of demand on weather conditions may change with time during a day. Therefore, the time stamped weather information is essential. In this paper, a multi-layer moving window approach is proposed to incorporate the significant weather variables, which are selected using Pearson and Spearman correlation techniques. The multi-layer moving window approach allows the layers to adjust their size to accommodate the weather variables based on their significance, which creates more flexibility and adaptability thereby improving the overall performance of the proposed approach. Furthermore, a recursive model is developed to forecast the demand in multi-step ahead. An electricity demand data for the state of New South Wales, Australia are acquired from the Australian Energy Market Operator and the associated results are reported in the paper. The results show that the proposed approach with dynamic incorporation of weather variables is promising for day-ahead and week-ahead load demand forecasting.https://ieeexplore.ieee.org/document/9528002/Autoregressive (AR) modelload forecastingmulti-layer moving windowPearson correlationSpearman correlation |
| spellingShingle | Dao H. Vu Kashem M. Muttaqi Ashish P. Agalgaonkar Arian Zahedmanesh Abdesselam Bouzerdoum Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions Journal of Modern Power Systems and Clean Energy Autoregressive (AR) model load forecasting multi-layer moving window Pearson correlation Spearman correlation |
| title | Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions |
| title_full | Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions |
| title_fullStr | Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions |
| title_full_unstemmed | Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions |
| title_short | Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions |
| title_sort | recurring multi layer moving window approach to forecast day ahead and week ahead load demand considering weather conditions |
| topic | Autoregressive (AR) model load forecasting multi-layer moving window Pearson correlation Spearman correlation |
| url | https://ieeexplore.ieee.org/document/9528002/ |
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