A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting

作者：Kavousi-Fard, Abdollah; Samet, Haidar^*; Marzbani, Fatemeh

来源：Expert Systems with Applications, 2014, 41(13): 6047-6056.

DOI：10.1016/j.eswa.2014.03.053

• Support Vector Regression (SVR)
• Modified Firefly Algorithm (MFA)
• Short Term Load Forecasting (STLF)
• Adaptive Modification Method

Precise forecast of the electrical load plays a highly significant role in the electricity industry and market. It provides economic operations and effective future plans for the utilities and power system operators. Due to the intermittent and uncertain characteristic of the electrical load, many research studies have been directed to nonlinear prediction methods. In this paper, a hybrid prediction algorithm comprised of Support Vector Regression (SVR) and Modified Firefly Algorithm (MFA) is proposed to provide the short term electrical load forecast. The SVR models utilize the nonlinear mapping feature to deal with nonlinear regressions. However, such models suffer from a methodical algorithm for obtaining the appropriate model parameters. Therefore, in the proposed method the MFA is employed to obtain the SVR parameters accurately and effectively. In order to evaluate the efficiency of the proposed methodology, it is applied to the electrical load demand in Fars, Iran. The obtained results are compared with those obtained from the ARMA model, ANN, SVR-GA, SVR-HBMO, SVR-PSO and SVR-FA. The experimental results affirm that the proposed algorithm outperforms other techniques.

• 出版日期2014-10-1