Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam

Manh-Hai Pham, T-A-Tho Vu, Duc-Quang Nguyen, Viet-Hung Dang, Ngoc-Trung Nguyen, Thu-Huyen Dang and The Vinh Nguyen
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Manh-Hai Pham: Faculty of Electrical Engineering, Electrical Power University, Hanoi 11355, Vietnam
T-A-Tho Vu: Faculty of Electrical Engineering, Electrical Power University, Hanoi 11355, Vietnam
Duc-Quang Nguyen: Faculty of Electrical Engineering, Electrical Power University, Hanoi 11355, Vietnam
Viet-Hung Dang: Faculty of Electrical Engineering, Electrical Power University, Hanoi 11355, Vietnam
Ngoc-Trung Nguyen: Faculty of Electrical Engineering, Electrical Power University, Hanoi 11355, Vietnam
Thu-Huyen Dang: Faculty of Electrical Engineering, Electrical Power University, Hanoi 11355, Vietnam
The Vinh Nguyen: Quang Ninh University of Industry, Quang Ninh 02451 Vietnam

Energies, 2019, vol. 12, issue 12, 1-19

Abstract: Recently, power companies apply optimal algorithms for short-term load forecasting, especially the daily load. However, in Vietnam, the load forecasting of the power system has not focused on this solution. Optimal algorithms and can help experts improve forecasting results including accuracy and the time required for forecasting. To achieve both goals, the combinations of different algorithms are still being studied. This article describes research using a new combination of two optimal algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This combination limits the weakness of the convergence speed of GA as well as the weakness of PSO that it easily falls into local optima (thereby reducing accuracy). This new hybrid algorithm was applied to the Southern Power Corporation’s (SPC—a large Power company in Vietnam) daily load forecasting. The results show the algorithm’s potential to provide a solution. The most accurate result was for the forecasting of a normal working day with an average error of 1.15% while the largest error was 3.74% and the smallest was 0.02%. For holidays and weekends, the average error always approximated the allowable limit of 3%. On the other hand, some poor results also provide an opportunity to re-check the real data provided by SPC.

Keywords: short-term load forecasting; GA; PSO; 24-h daily load (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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