Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting
نویسندگان
چکیده
منابع مشابه
Short Term Load Forecasting Using Empirical Mode Decomposition, Wavelet Transform and Support Vector Regression
The Short-term forecasting of electric load plays an important role in designing and operation of power systems. Due to the nature of the short-term electric load time series (nonlinear, non-constant, and non-seasonal), accurate prediction of the load is very challenging. In this article, a method for short-term daily and hourly load forecasting is proposed. In this method, in the first step, t...
متن کاملSupport Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting
Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) ...
متن کاملShort Term Load Forecasting Using A Hybrid Model Based On Support Vector Regression
This paper proposes a new hybrid method based on support vector regression (SVR) to predict the load value of power systems accurately. The proposed method will use the SVR to overcome some deficiencies such as overfitting and complicated structure that exist in the neural network. In order to find the optimal values of the parameters, krill herd (KH) algorithm is used as the optimizer. The KH ...
متن کاملA new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting
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 predi...
متن کاملShort-term load forecasting using a kernel-based support vector regression combination model
Kernel-based methods, such as support vector regression (SVR), have demonstrated satisfactory performance in short-term load forecasting (STLF) application. However, the good performance of kernel-based method depends on the selection of an appropriate kernel function that fits the learning target, unsuitable kernel function or hyper-parameters setting may lead to significantly poor performance...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Energies
سال: 2019
ISSN: 1996-1073
DOI: 10.3390/en12061093