Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions
<p dir="ltr">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...
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2022
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| _version_ | 1864513506562277376 |
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| author | Dao H. Vu (19517236) |
| author2 | Kashem M. Muttaqi (19517239) Ashish P. Agalgaonkar (19517242) Arian Zahedmanesh (19517245) Abdesselam Bouzerdoum (17900021) |
| author2_role | author author author author |
| author_facet | Dao H. Vu (19517236) Kashem M. Muttaqi (19517239) Ashish P. Agalgaonkar (19517242) Arian Zahedmanesh (19517245) Abdesselam Bouzerdoum (17900021) |
| author_role | author |
| dc.creator.none.fl_str_mv | Dao H. Vu (19517236) Kashem M. Muttaqi (19517239) Ashish P. Agalgaonkar (19517242) Arian Zahedmanesh (19517245) Abdesselam Bouzerdoum (17900021) |
| dc.date.none.fl_str_mv | 2022-09-02T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.35833/mpce.2021.000210 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Recurring_Multi-layer_Moving_Window_Approach_to_Forecast_Day-ahead_and_Week-ahead_Load_Demand_Considering_Weather_Conditions/26888800 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electronics, sensors and digital hardware Information and computing sciences Data management and data science Machine learning Autoregressive (AR) model load forecasting multi-layer moving window Pearson correlation Spearman correlation Predictive models Meteorology Load modeling Demand forecasting Australia Adaptation models Correlation |
| dc.title.none.fl_str_mv | Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Modern Power Systems and Clean Energy<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.35833/mpce.2021.000210" target="_blank">https://dx.doi.org/10.35833/mpce.2021.000210</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_da9e97918e305e051168e8f0289acafd |
| identifier_str_mv | 10.35833/mpce.2021.000210 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26888800 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather ConditionsDao H. Vu (19517236)Kashem M. Muttaqi (19517239)Ashish P. Agalgaonkar (19517242)Arian Zahedmanesh (19517245)Abdesselam Bouzerdoum (17900021)EngineeringElectronics, sensors and digital hardwareInformation and computing sciencesData management and data scienceMachine learningAutoregressive (AR) modelload forecastingmulti-layer moving windowPearson correlationSpearman correlationPredictive modelsMeteorologyLoad modelingDemand forecastingAustraliaAdaptation modelsCorrelation<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Modern Power Systems and Clean Energy<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.35833/mpce.2021.000210" target="_blank">https://dx.doi.org/10.35833/mpce.2021.000210</a></p>2022-09-02T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.35833/mpce.2021.000210https://figshare.com/articles/journal_contribution/Recurring_Multi-layer_Moving_Window_Approach_to_Forecast_Day-ahead_and_Week-ahead_Load_Demand_Considering_Weather_Conditions/26888800CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/268888002022-09-02T06:00:00Z |
| spellingShingle | Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions Dao H. Vu (19517236) Engineering Electronics, sensors and digital hardware Information and computing sciences Data management and data science Machine learning Autoregressive (AR) model load forecasting multi-layer moving window Pearson correlation Spearman correlation Predictive models Meteorology Load modeling Demand forecasting Australia Adaptation models Correlation |
| status_str | publishedVersion |
| 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 | Engineering Electronics, sensors and digital hardware Information and computing sciences Data management and data science Machine learning Autoregressive (AR) model load forecasting multi-layer moving window Pearson correlation Spearman correlation Predictive models Meteorology Load modeling Demand forecasting Australia Adaptation models Correlation |