Medium-term load forecasting with Power Market Survey: GEPCO case study
This research presents a comprehensive case study on medium-term load forecasting (MTLF) in the intricate dynamics of Pakistan’s power sector, Gujranwala Electric Power Company (GEPCO). The time horizon for MTLF ranges from a few weeks to one year and it has applications in energy managem...
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Academia.edu Journals
2024-06-01
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| Series: | Academia Green Energy |
| Online Access: | https://www.academia.edu/121278999/Medium_Term_Load_Forecasting_with_Power_Market_Survey_GEPCO_Case_Study |
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| author | Sheeza Mariam Nawaz Ubaid Ahmed Adil Amin Syed Afraz Hussain Shah Anzar Mahmood |
| author_facet | Sheeza Mariam Nawaz Ubaid Ahmed Adil Amin Syed Afraz Hussain Shah Anzar Mahmood |
| author_sort | Sheeza Mariam Nawaz |
| collection | DOAJ |
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This research presents a comprehensive case study on medium-term load forecasting (MTLF) in the intricate dynamics of Pakistan’s power sector, Gujranwala Electric Power Company (GEPCO). The time horizon for MTLF ranges from a few weeks to one year and it has applications in energy management and planning. The deep-learning networks (DLNs), proposed in recent years, have a black-box nature, which reduces the interpretability of the results. Therefore, in the proposed study, a mathematical model, Power Market Survey (PMS), is implemented to forecast month-ahead load for GEPCO division dataset. The historical load data, incorporating relevant features such as growth rate, load factor, schedule of load shedding, etc., are fed as input to the proposed model for efficient load forecasting. The PMS is trained with a bottom-up approach and its performance is validated through mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and confidence interval with 95% of probability. The results demonstrate that the proposed model performed well while incorporating the effects of planned load shedding as compared to the case in which load-shedding effects are neglected. Moreover, while incorporating the load-shedding effects, the proposed model recorded MAPE and MAE as 11.91% and 0.22, respectively, for the fiscal year 2022–2023. However, when the proposed model is implemented while neglecting the effects of load shedding, the MAPE and MAE of 13.64% and 0.24 are recorded by the PMS technique, correspondingly. |
| format | Article |
| id | doaj-art-2a28c0e0b12a4555bf465162413d3d5a |
| institution | Kabale University |
| issn | 2998-3665 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Academia.edu Journals |
| record_format | Article |
| series | Academia Green Energy |
| spelling | doaj-art-2a28c0e0b12a4555bf465162413d3d5a2025-02-10T21:37:08ZengAcademia.edu JournalsAcademia Green Energy2998-36652024-06-011110.20935/AcadEnergy6257Medium-term load forecasting with Power Market Survey: GEPCO case studySheeza Mariam Nawaz0Ubaid Ahmed1Adil Amin2Syed Afraz Hussain Shah3Anzar Mahmood4Department of Electrical Engineering, Mirpur University of Science & Technology, Mirpur, 10250, AJ&K, Pakistan.Department of Electrical Engineering, Mirpur University of Science & Technology, Mirpur, 10250, AJ&K, Pakistan.Department of Electrical Engineering, Mirpur University of Science & Technology, Mirpur, 10250, AJ&K, Pakistan.Department of Electrical Engineering, Mirpur University of Science & Technology, Mirpur, 10250, AJ&K, Pakistan.Department of Electrical Engineering, Mirpur University of Science & Technology, Mirpur, 10250, AJ&K, Pakistan. This research presents a comprehensive case study on medium-term load forecasting (MTLF) in the intricate dynamics of Pakistan’s power sector, Gujranwala Electric Power Company (GEPCO). The time horizon for MTLF ranges from a few weeks to one year and it has applications in energy management and planning. The deep-learning networks (DLNs), proposed in recent years, have a black-box nature, which reduces the interpretability of the results. Therefore, in the proposed study, a mathematical model, Power Market Survey (PMS), is implemented to forecast month-ahead load for GEPCO division dataset. The historical load data, incorporating relevant features such as growth rate, load factor, schedule of load shedding, etc., are fed as input to the proposed model for efficient load forecasting. The PMS is trained with a bottom-up approach and its performance is validated through mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and confidence interval with 95% of probability. The results demonstrate that the proposed model performed well while incorporating the effects of planned load shedding as compared to the case in which load-shedding effects are neglected. Moreover, while incorporating the load-shedding effects, the proposed model recorded MAPE and MAE as 11.91% and 0.22, respectively, for the fiscal year 2022–2023. However, when the proposed model is implemented while neglecting the effects of load shedding, the MAPE and MAE of 13.64% and 0.24 are recorded by the PMS technique, correspondingly.https://www.academia.edu/121278999/Medium_Term_Load_Forecasting_with_Power_Market_Survey_GEPCO_Case_Study |
| spellingShingle | Sheeza Mariam Nawaz Ubaid Ahmed Adil Amin Syed Afraz Hussain Shah Anzar Mahmood Medium-term load forecasting with Power Market Survey: GEPCO case study Academia Green Energy |
| title | Medium-term load forecasting with Power Market Survey: GEPCO case study |
| title_full | Medium-term load forecasting with Power Market Survey: GEPCO case study |
| title_fullStr | Medium-term load forecasting with Power Market Survey: GEPCO case study |
| title_full_unstemmed | Medium-term load forecasting with Power Market Survey: GEPCO case study |
| title_short | Medium-term load forecasting with Power Market Survey: GEPCO case study |
| title_sort | medium term load forecasting with power market survey gepco case study |
| url | https://www.academia.edu/121278999/Medium_Term_Load_Forecasting_with_Power_Market_Survey_GEPCO_Case_Study |
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