Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm

• Published in: Energies (Basel) Vol. 5; no. 11; pp. 4430 - 4445
• Main Authors: Li, Hongze, Guo, Sen, Zhao, Huiru, Su, Chenbo, Wang, Bao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2012
• Subjects: Accuracy; annual electric load forecasting; Electric power; Electricity; Electricity distribution; Forecasting; Forecasting techniques; fruit fly optimization algorithm (FOA); Genetic algorithms; Heuristic; least squares support vector machine (LSSVM); Neural networks; Optimization algorithms; optimization problem; Optimization techniques; Support vector machines; Beijing China; China
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en5114430

The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM) has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA) has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA) outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA), generalized regression neural network (GRNN) and regression model.