Support Vector Machine Regression Predicts Energy Consumption and Conservation Attitude in Households

2021 ◽  
Author(s):  
Bahareh Vojdani ◽  
Mansour Yegane ◽  
Julian Lang
Keyword(s):  
Support Vector Machine ◽  
Energy Consumption ◽  
Support Vector ◽  
Support Vector Machine Regression

scholarly journals Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression

2021 ◽  
Vol 13 (9) ◽  
pp. 4689
Author(s):  
Wei Qin ◽  
Linhong Wang ◽  
Yuhan Liu ◽  
Cheng Xu
Keyword(s):  
Support Vector Machine ◽  
Energy Consumption ◽  
Optimization Algorithm ◽  
Support Vector ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Support Vector Machine Regression ◽  
Electric Bus ◽  
Proposed Model ◽  
The Mean

Electric buses have many significant advantages, such as zero emissions and low noise and energy consumption, making them play an important role in saving the operation cost of bus companies and reducing urban traffic pollution emissions. Therefore, in recent years, many cities in the world dedicate to promoting the electrification of public transport vehicles. Whereas due to the limitation of on-board battery capacity, the driving range of electric buses is relatively short. The accurate estimation of energy consumption on the electric bus routes is the premise of conducting bus scheduling and optimizing the layout of charging facilities. This study collected the actual operation data of three electric bus routes in Meihekou City, China, and established the support vector machine regression (SVR) model by taking the state of charge (SOC), trip travel time, mean environment temperature and air-conditioning operation time as the independent variables; while the energy consumptions of the route operations served as the dependent variables. Furthermore, the grey wolf optimization (GWO) algorithm was adopted to select the optimal parameters of the proposed model. Finally, a support vector machine regression model based on the grey wolf optimization algorithm (GWO-SVR) is proposed. Three real bus lines were taken as examples to validate the model. The results show that the mean average percentage error is 14.47% and the mean average error is 0.7776. In addition, the estimation accuracy and training time of the proposed model are superior to the genetic algorithm-back propagation neural network model and grid-search support vector machine regression model.

Erratum to: Prognostics of Induction Motor Shaft Based on Feature Importance and Least Square Support Vector Machine Regression

2021 ◽  
Vol 18 (2) ◽  
pp. 8751
Author(s):  
Didik Djoko Susilo ◽  
A. Widodo ◽  
T. Prahasto ◽  
M. Nizam
Keyword(s):  
Support Vector Machine ◽  
Induction Motor ◽  
Mechanical Engineering ◽  
Least Square ◽  
Support Vector ◽  
Motor Shaft ◽  
Support Vector Machine Regression ◽  
Related Article ◽  
Feature Importance

This is an erratum to International Journal of Automotive and Mechanical Engineering 2021; 18(1): 8464–8477. Please refer to the related article: https://doi.org/10.15282/ijame.18.1.2021.06.0641

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