Abstract
Electricity load forecasting is an important part of power system operation, planning and management. Support vector regression (SVR) is a commonly used and efficient method for medium and long-term load forecasting. The hyperparameters of SVR have a very high influence on its performance and are not easily determined. In this paper, an improved Harris Hawk optimization algorithm (TAHHO) is proposed for optimizing the hyperparameters of SVR, and a TAHHO-SVR medium- and long-term prediction model is developed. TAHHHO enhances the convergence and stability of the original algorithm by introducing the survival of the fittest principle and the crossover operator of the artificial tree (AT) algorithm, which is validated in 13 benchmark test functions. The proposed TAHHO-SVR model is used for forecasting the UK National Grid dataset and demonstrates the feasibility and competitiveness of the model, which effectively improves the forecasting accuracy compared to HHO-SVR.
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