A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting |
| |
| Affiliation: | 1. Department of Electrical Engineering, Sarvestan Branch, Islamic Azad University, Sarvestan, Iran;2. School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran;3. American University of Sharjah, Sharjah, United Arab Emirates;1. Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia;2. Odette School of Business, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada;1. Grup de Recerca en Sistemes Intel·ligents, Ramon Llull University, Quatre Camins 2, 08022 Barcelona, Spain;2. Grup de Recerca en Internet Technologies & Storage, Ramon Llull University, Quatre Camins 2, 08022 Barcelona, Spain;3. Departamento de Ingeniería Matemática e Informática, Universidad Pública de Navarra, Campus de Arrosadía, 31006 Pamplona, Spain;1. University of Cauca, Cll. 5 4-70 Popayán, Colombia;2. Universidad Carlos III de Madrid, Av. Universidad 30, 28911 Leganés, Spain;3. University of East London, Docklands Campus, London E16 2RD, United Kingdom;1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China;2. School of Software Microelectronics, Peking University, Beijing 100190, China;1. Instituto de Engenharia Mecânica e Gestão Industrial, Faculdade de Engenharia, Universidade do Porto, R. Dr. Roberto Frias s/n, 4200-465 Porto, Portugal;2. Department of Urology, School of Medicine Stanford University, Stanford, CA, USA |
| |
| Abstract: | 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. |
| |
| Keywords: | Support Vector Regression (SVR) Modified Firefly Algorithm (MFA) Short Term Load Forecasting (STLF) Adaptive Modification Method |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
