Short term power dispatch using neural network based ensemble classifier
| Autor: | Muhammad Faizan Tahir, Khalid Mehmood Cheema, Kashif Mehmood, Shaheer Shaheen, Ahmad H. Milyani, Abdul Rehman Tariq, Rajvikram Madurai Elavarasan, Kannadasan Raju |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Renewable Energy Sustainability and the Environment Computer science 020209 energy Computation Energy Engineering and Power Technology 02 engineering and technology 021001 nanoscience & nanotechnology Power dispatch ComputingMethodologies_PATTERNRECOGNITION Electricity generation 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering 0210 nano-technology MATLAB Algorithm computer Classifier (UML) computer.programming_language |
| Zdroj: | Journal of Energy Storage. 33:102101 |
| ISSN: | 2352-152X |
| DOI: | 10.1016/j.est.2020.102101 |
| Popis: | This work reports the application of ensemble artificial neural networks, a machine learning technique, in the solution of short term optimal power generation on the IEEE 30-bus test system. The study, carried out using MATLAB, is being reported. The motive for using ensemble artificial neural networks has been to take advantage of multiple parallel processors computing rather than the traditional serial computation. The Bootstraps are obtained through small bags in the Bagging algorithm and are combined by averaging. Employing ensemble neural networks reduces bias and variance in machine learning so that the overall error is reduced, and hence better prediction can be achieved. The results obtained from the proposed algorithm are compared with those from the other heuristic and conventional techniques to validate the effectiveness of the proposed methodology. |
| Databáze: | OpenAIRE |
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