Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter
<p dir="ltr">The intermittent non-dispatchable power produced by Renewable Energy Sources (RESs) in distribution networks caused additional challenges in load forecasting due to the introduced uncertainties. Therefore, high-quality load forecasting is essential for distribution netwo...
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2023
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| _version_ | 1864513527412162560 |
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| author | Mena S. ElMenshawy (17983807) |
| author2 | Ahmed M. Massoud (16896417) |
| author2_role | author |
| author_facet | Mena S. ElMenshawy (17983807) Ahmed M. Massoud (16896417) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mena S. ElMenshawy (17983807) Ahmed M. Massoud (16896417) |
| dc.date.none.fl_str_mv | 2023-09-14T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3315591 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Short-Term_Load_Forecasting_in_Active_Distribution_Networks_Using_Forgetting_Factor_Adaptive_Extended_Kalman_Filter/25239514 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Load forecasting Kalman filters Load modeling Predictive models Adaptation models Forecasting Data models Adaptive extended Kalman filter forgetting factor adaptive extended Kalman filter maximum absolute error root mean square error short-term load forecasting |
| dc.title.none.fl_str_mv | Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The intermittent non-dispatchable power produced by Renewable Energy Sources (RESs) in distribution networks caused additional challenges in load forecasting due to the introduced uncertainties. Therefore, high-quality load forecasting is essential for distribution network planning and operation. Most of the work presented in literature focusing on Short-Term Load Forecasting (STLF) has paid little consideration to the intrinsic uncertainty associated with the load dataset. A few research studies focused on developing data filtering algorithm for the load forecasting process using approaches such as Kalman filter, which has good tracking capability in the presence of noise in the data collection process. To avoid the divergence of conventional Kalman filter and improve the system stability, Adaptive Extended Kalman Filter (AEKF) is introduced through incorporating a moving-window method with the Extended Kalman Filter (EKF). Nonetheless, the moving window adds an extra computational burden. In this regard, this paper employs the concept of Forgetting Factor AEKF (FFAEKF) for STLF in distribution networks to avoid the computational burden introduced by the AEKF. The forgetting factor improves the estimation accuracy and increases the system convergence when compared with the AEKF. In this paper, the AEKF and FFAEKF are compared in terms of their performance using Maximum Absolute Error (MaxAE) and Root Mean Square Error (RMSE). Matlab/Simulink platform is used to apply the AEKF and FFAEKF algorithms on the load dataset. Results have demonstrated that the FFAEKF improves the forecasting performance through providing less MaxAE and less RMSE. In which, the FFAEKF MaxAE and RMSE are reduced by two and three times, respectively, compared to the AEKF MaxAE and RMSE.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<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/10.1109/access.2023.3315591" target="_blank">https://dx.doi.org/10.1109/access.2023.3315591</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1b04bc8cedb1d7a97a6bf7a1ae848705 |
| identifier_str_mv | 10.1109/access.2023.3315591 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25239514 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman FilterMena S. ElMenshawy (17983807)Ahmed M. Massoud (16896417)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringLoad forecastingKalman filtersLoad modelingPredictive modelsAdaptation modelsForecastingData modelsAdaptive extended Kalman filterforgetting factor adaptive extended Kalman filtermaximum absolute errorroot mean square errorshort-term load forecasting<p dir="ltr">The intermittent non-dispatchable power produced by Renewable Energy Sources (RESs) in distribution networks caused additional challenges in load forecasting due to the introduced uncertainties. Therefore, high-quality load forecasting is essential for distribution network planning and operation. Most of the work presented in literature focusing on Short-Term Load Forecasting (STLF) has paid little consideration to the intrinsic uncertainty associated with the load dataset. A few research studies focused on developing data filtering algorithm for the load forecasting process using approaches such as Kalman filter, which has good tracking capability in the presence of noise in the data collection process. To avoid the divergence of conventional Kalman filter and improve the system stability, Adaptive Extended Kalman Filter (AEKF) is introduced through incorporating a moving-window method with the Extended Kalman Filter (EKF). Nonetheless, the moving window adds an extra computational burden. In this regard, this paper employs the concept of Forgetting Factor AEKF (FFAEKF) for STLF in distribution networks to avoid the computational burden introduced by the AEKF. The forgetting factor improves the estimation accuracy and increases the system convergence when compared with the AEKF. In this paper, the AEKF and FFAEKF are compared in terms of their performance using Maximum Absolute Error (MaxAE) and Root Mean Square Error (RMSE). Matlab/Simulink platform is used to apply the AEKF and FFAEKF algorithms on the load dataset. Results have demonstrated that the FFAEKF improves the forecasting performance through providing less MaxAE and less RMSE. In which, the FFAEKF MaxAE and RMSE are reduced by two and three times, respectively, compared to the AEKF MaxAE and RMSE.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<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/10.1109/access.2023.3315591" target="_blank">https://dx.doi.org/10.1109/access.2023.3315591</a></p>2023-09-14T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3315591https://figshare.com/articles/journal_contribution/Short-Term_Load_Forecasting_in_Active_Distribution_Networks_Using_Forgetting_Factor_Adaptive_Extended_Kalman_Filter/25239514CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252395142023-09-14T06:00:00Z |
| spellingShingle | Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter Mena S. ElMenshawy (17983807) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Load forecasting Kalman filters Load modeling Predictive models Adaptation models Forecasting Data models Adaptive extended Kalman filter forgetting factor adaptive extended Kalman filter maximum absolute error root mean square error short-term load forecasting |
| status_str | publishedVersion |
| title | Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter |
| title_full | Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter |
| title_fullStr | Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter |
| title_full_unstemmed | Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter |
| title_short | Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter |
| title_sort | Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Load forecasting Kalman filters Load modeling Predictive models Adaptation models Forecasting Data models Adaptive extended Kalman filter forgetting factor adaptive extended Kalman filter maximum absolute error root mean square error short-term load forecasting |