Comparative Analysis of Intelligently Tuned Support Vector Regression Models for Short Term Load Forecasting in Smart Grid Framework

Numerous studies have been performed to modify ferrites to achieve the desired magnetic properties for the intended applications. Many variables with interactions between themselves are involved in modifying these properties. This study aims to provide an accurate and effective method for predicting the magnetic properties of M‐type ferrites under the influence of involved variables. For this purpose, ferrites are synthesized by doping 17 different ions and the results of the analysis of samples form the experimental data. The support vector regression (SVR) model is selected for prediction and in order to optimize hyper parameters, genetic, particle swarm optimization, and sequential minimal optimization techniques are used. Based on the comparison between the outcomes of these algorithms, the model with the best performance is used to predict coercivity and residual magnetism in M‐type magnetic ferrites. Furthermore, with the cross validation technique, the accuracy and robustness of the model designed for new samples are evaluated. The results show that the selected SVR model, with an average absolute relative error of 2% and 0.7%, is able to predict coercivity and residual magnetism, respectively. Consequently, the prediction method presented has the capability to accelerate and develop the modification of magnetic properties for different applications.

show abstract

“…This procedure is repeated until the optimal answer for the objective function is found. [46][47][48][49][50]…”

Section: Geneticmentioning

confidence: 99%

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Pourashraf

Shokri²,

Yousefi

et al. 2021

Advcd Theory and Sims

View full text Add to dashboard Cite

show abstract

“…The outcome accuracy of the proposed mode when estimated for SVM, SVRGA and SVRPSO was about 97.67%, 97.82% and 97.89% respectively. The article concluded that the three models were highly active for STLF, but SVRPSO, and SVRGA consumed more time than standard Energies 2020, 13, 6105 6 of 12 SVM [28]. A data set from Hubei SVM for short-term power load prediction was studied by Ye et al The performance of the proposed approach was compared with traditional models: BPNN and the time series method.…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Intelligent Systems for Power Load Forecasting: A Study Review

2020

View full text Add to dashboard Cite

The study of power load forecasting is gaining greater significance nowadays, particularly with the use and integration of renewable power sources and external power stations. Power forecasting is an important task in the planning, control, and operation of utility power systems. In addition, load forecasting (LF) aims to estimate the power or energy needed to meet the required power or energy to supply the specific load. In this article, we introduce, review and compare different power load forecasting techniques. Our goal is to help in the process of explaining the problem of power load forecasting via brief descriptions of the proposed methods applied in the last decade. The study reviews previous research that deals with the design of intelligent systems for power forecasting using various methods. The methods are organized into five groups—Artificial Neural Network (ANN), Support Vector Regression, Decision Tree (DT), Linear Regression (LR), and Fuzzy Sets (FS). This way, the review provides a clear concept of power load forecasting for the purposes of future research and study.

show abstract

“…A deeper analysis of other existing tuning methods on this topic deserves further study. For further information on the existing strategies, the reader is referred to Kaneko and Funatsu (2015), Smets et al (2007) and Sreekumar and Verma (2016).…”

Section: An Alternating Algorithmmentioning

confidence: 99%

Selection of time instants and intervals with Support Vector Regression for multivariate functional data

Blanquero

Carrizosa

Jiménez-Cordero

et al. 2020

Computers & Operations Research

View full text Add to dashboard Cite

When continuously monitoring processes over time, data is collected along a whole period, from which only certain time instants and certain time intervals may play a crucial role in the data analysis. We develop a method that addresses the problem of selecting a finite and small set of short intervals (or instants) able to capture the information needed to predict a response variable from multivariate functional data using Support Vector Regression (SVR).In addition to improving interpretability, storage requirements, and monitoring cost, feature selection can potentially reduce overfitting by mitigating data autocorrelation. We propose a continuous optimization algorithm to fit the SVR parameters and select intervals and instants. Our approach takes advantage of the functional nature of the data by formulating a new bilevel optimization problem that integrates selection of intervals and instants, tuning of some key SVR parameters and fitting the SVR. We illustrate the usefulness of our proposal in some benchmark data sets.

show abstract

Comparative Analysis of Intelligently Tuned Support Vector Regression Models for Short Term Load Forecasting in Smart Grid Framework

Cited by 17 publications

References 21 publications

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Implementing Machine Learning in Laboratory Synthesis by Hybrid of SVR Model and Optimization Algorithms

Intelligent Systems for Power Load Forecasting: A Study Review

Selection of time instants and intervals with Support Vector Regression for multivariate functional data

Contact Info

Product

Resources

About