Hybrid Techniques for Short Term Load Forecasting
Short Term Load Forecasting (STLF) is the projection of system load demands for the next day or week. Because of its openness in modeling, simplicity of implementation, and improved performance, the ANN-based STLF model has gained traction. The neural model consists of weights whose optimal values a...
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| Language: | English |
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OICC Press
2023-03-01
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| Series: | Majlesi Journal of Electrical Engineering |
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| Online Access: | https://oiccpress.com/mjee/article/view/4985 |
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| _version_ | 1850237750377185280 |
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| author | Saroj Panda Papia Ray Surender Salkuti |
| author_facet | Saroj Panda Papia Ray Surender Salkuti |
| author_sort | Saroj Panda |
| collection | DOAJ |
| description | Short Term Load Forecasting (STLF) is the projection of system load demands for the next day or week. Because of its openness in modeling, simplicity of implementation, and improved performance, the ANN-based STLF model has gained traction. The neural model consists of weights whose optimal values are determined using various optimization approaches. This paper uses an Artificial Neural Network (ANN) trained using multiple hybrid techniques (HT) such as Back Propagation (BP), Cuckoo Search (CS) model, and Bat algorithm (BA) for load forecasting. Here, a thorough examination of the various strategies is taken to determine their scope and ability to produce results using different models in different settings. The simulation results show that the BA-BP model has less predicting error than other techniques. However, the Back Propagation model based on the Cuckoo Search method produces less inaccuracy, which is acceptable. |
| format | Article |
| id | doaj-art-6b29e1db0ea14f00851ec3041fd49309 |
| institution | OA Journals |
| issn | 2345-377X 2345-3796 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | OICC Press |
| record_format | Article |
| series | Majlesi Journal of Electrical Engineering |
| spelling | doaj-art-6b29e1db0ea14f00851ec3041fd493092025-08-20T02:01:40ZengOICC PressMajlesi Journal of Electrical Engineering2345-377X2345-37962023-03-0117110.30486/mjee.2023.1970200.0Hybrid Techniques for Short Term Load ForecastingSaroj Panda0Papia Ray1Surender Salkuti2Veer Surendra Sai Univetsity of Technology, Burla, IndiaVeer Surendra Sai Univetsity of Technology, Burla, IndiaWoosong University, Daejon, Republic of KoreaShort Term Load Forecasting (STLF) is the projection of system load demands for the next day or week. Because of its openness in modeling, simplicity of implementation, and improved performance, the ANN-based STLF model has gained traction. The neural model consists of weights whose optimal values are determined using various optimization approaches. This paper uses an Artificial Neural Network (ANN) trained using multiple hybrid techniques (HT) such as Back Propagation (BP), Cuckoo Search (CS) model, and Bat algorithm (BA) for load forecasting. Here, a thorough examination of the various strategies is taken to determine their scope and ability to produce results using different models in different settings. The simulation results show that the BA-BP model has less predicting error than other techniques. However, the Back Propagation model based on the Cuckoo Search method produces less inaccuracy, which is acceptable.https://oiccpress.com/mjee/article/view/4985Artificial Neural NetworkBack PropagationCuckoo Search. Bat algorithmHybrid TechniquesShort Term Load Forecasting |
| spellingShingle | Saroj Panda Papia Ray Surender Salkuti Hybrid Techniques for Short Term Load Forecasting Majlesi Journal of Electrical Engineering Artificial Neural Network Back Propagation Cuckoo Search. Bat algorithm Hybrid Techniques Short Term Load Forecasting |
| title | Hybrid Techniques for Short Term Load Forecasting |
| title_full | Hybrid Techniques for Short Term Load Forecasting |
| title_fullStr | Hybrid Techniques for Short Term Load Forecasting |
| title_full_unstemmed | Hybrid Techniques for Short Term Load Forecasting |
| title_short | Hybrid Techniques for Short Term Load Forecasting |
| title_sort | hybrid techniques for short term load forecasting |
| topic | Artificial Neural Network Back Propagation Cuckoo Search. Bat algorithm Hybrid Techniques Short Term Load Forecasting |
| url | https://oiccpress.com/mjee/article/view/4985 |
| work_keys_str_mv | AT sarojpanda hybridtechniquesforshorttermloadforecasting AT papiaray hybridtechniquesforshorttermloadforecasting AT surendersalkuti hybridtechniquesforshorttermloadforecasting |