Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter
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. Mo...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10251506/ |
| _version_ | 1797668040160051200 |
|---|---|
| author | Mena S. ElMenshawy Ahmed M. Massoud |
| author_facet | Mena S. ElMenshawy Ahmed M. Massoud |
| author_sort | Mena S. ElMenshawy |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T20:23:17Z |
| format | Article |
| id | doaj.art-61694d3be39a44a49b7af30fe29f3f91 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T20:23:17Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-61694d3be39a44a49b7af30fe29f3f912023-10-02T23:00:49ZengIEEEIEEE Access2169-35362023-01-011110391610392410.1109/ACCESS.2023.331559110251506Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman FilterMena S. ElMenshawy0https://orcid.org/0000-0001-8812-2472Ahmed M. Massoud1https://orcid.org/0000-0001-9343-469XDepartment of Electrical Engineering, Qatar University, Doha, QatarDepartment of Electrical Engineering, Qatar University, Doha, QatarThe 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.https://ieeexplore.ieee.org/document/10251506/Adaptive extended Kalman filterforgetting factor adaptive extended Kalman filtermaximum absolute errorroot mean square errorshort-term load forecasting |
| spellingShingle | Mena S. ElMenshawy Ahmed M. Massoud Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter IEEE Access Adaptive extended Kalman filter forgetting factor adaptive extended Kalman filter maximum absolute error root mean square error short-term load forecasting |
| 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 | Adaptive extended Kalman filter forgetting factor adaptive extended Kalman filter maximum absolute error root mean square error short-term load forecasting |
| url | https://ieeexplore.ieee.org/document/10251506/ |
| work_keys_str_mv | AT menaselmenshawy shorttermloadforecastinginactivedistributionnetworksusingforgettingfactoradaptiveextendedkalmanfilter AT ahmedmmassoud shorttermloadforecastinginactivedistributionnetworksusingforgettingfactoradaptiveextendedkalmanfilter |