Short term power dispatch using neural network based ensemble classifier. (January 2021)

• Record Type: Journal Article
• Authors: Mehmood, Kashif; Cheema, Khalid Mehmood; Tahir, Muhammad Faizan; Tariq, Abdul Rehman; Milyani, Ahmad H.; Elavarasan, Rajvikram Madurai; Shaheen, Shaheer; Raju, Kannadasan
• Is Part Of: Journal of energy storage. Volume 33(2021)
• Publication Date: 2021-01
• Subjects: Bagging -- Bootstraps -- Optimal power generation -- Ensemble artificial neural networks -- Neural networks -- Small bags -- Unit commitment; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2020.102101
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: The application of ensemble artificial neural networks is proposed in this paper The solution of short-term optimal power generation on the IEEE 30-bus test system is presented and anlysed. The results, obtained from the proposed algorithm, are compared with other heuristic and conventional techniques to validate the proposed methodology. Abstract: 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.