An Effective Approach to ANN-Based Short-Term Load Forecasting Model Using Hybrid Algorithm GA-PSO
| Autor: | Thu-Huyen Dang, Van-Duy Pham, The-Vinh Nguyen, Due-Quang Nguyen, Manh-Hai Pham, Ngoc-Trung Nguyen, T-A-Tho Vu, Viet-Hung Dang |
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| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
Computer science business.industry 020209 energy Load forecasting Particle swarm optimization 02 engineering and technology Encryption Hybrid algorithm Term (time) Power (physics) Convergence (routing) Genetic algorithm 0202 electrical engineering electronic engineering information engineering business |
| Zdroj: | 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). |
| DOI: | 10.1109/eeeic.2018.8493908 |
| Popis: | In recent years, research of optimal algorithms for short-term load forecasting has become popular. Optimal algorithms can improve forecast results in two main directions: precision and speed of convergence. To achieve both of these goals, researchers often use a combination of different algorithms. This paper describes a new combination of two optimal algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The forecasting of the 24-hour daily-load on the normal working day of the SPC-Southern Power Corporation (a big local Vietnam Company) showed a significant improvement in the error as well as the speed of convergence with several selected days. In the most accurate case, the average error of prediction reaches 1.15% while the biggest error is 3.74% and the smallest is 0.02% |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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