.png){kind=icon}
Return
Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms
.png){kind=icon}
OA
AI
DOI：10.1155/2013/292575.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA‐MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA‐MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA‐MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well‐known forecasting models but also outperform the hybrid algorithms in the related existing literature.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
No journal information available
Organization
No organization information available
Cited Papers
No cited papers available