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

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 th...

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Bibliographic Details
Published in:Energies (Basel) 2019-06, Vol.12 (12), p.2283
Main Authors: Pham, Manh-Hai, Vu, T-A-Tho, Nguyen, Duc-Quang, Dang, Viet-Hung, Nguyen, Ngoc-Trung, Dang, Thu-Huyen, Nguyen, The Vinh
Format: Article
Language:English
Subjects:
24-h daily load
Accuracy
Algorithms
Computer simulation
Electrical engineering
Electricity consumption
Forecasting
Genetic algorithms
International conferences
Neural networks
Optimization
PSO
Short term
short-term load forecasting
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12122283