Abstract
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.
Highlights
With the rapid development of China's electric power industry, electric load forecasting technology has aroused widespread concerns among practitioners and academia
This paper examines the feasibility of using the least squares support vector machine (LSSVM) model to forecast annual electric loads
How to improve the annual electric load forecasting accuracy is worthy of study
Summary
With the rapid development of China's electric power industry, electric load forecasting technology has aroused widespread concerns among practitioners and academia. Hong [14] proposed an electric load forecasting model which combined the seasonal recurrent support vector regression model with a chaotic artificial bee colony algorithm, and this method could provide a more accurate forecasting result than the TF-ε-SVR-SA and ARIMA model. Pai et al [15] used support vector machines with a simulated annealing algorithm to forecast Taiwan’s electricity load, and the empirical results revealed this model outperforms the general regression neural network model and the autoregressive integrated moving average model. These methods, to a certain extent, all improve the annual electric load forecasting accuracy.
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