scholarly journals Residential Energy Consumer Occupancy Prediction Based on Support Vector Machine

2021 ◽  
Vol 13 (15) ◽  
pp. 8321
Author(s):  
Dinh Hoa Nguyen

The occupancy of residential energy consumers is an important subject to be studied to account for the changes on the load curve shape caused by paradigm shifts to consumer-centric energy markets or by significant energy demand variations due to pandemics, such as COVID-19. For non-intrusive occupancy analysis, multiple types of sensors can be installed to collect data based on which the consumer occupancy can be learned. However, the overall system cost will be increased as a result. Therefore, this research proposes a cheap and lightweight machine learning approach to predict the energy consumer occupancy based solely on their electricity consumption data. The proposed approach employs a support vector machine (SVM), in which different kernels are used and compared, including positive semi-definite and conditionally positive definite kernels. Efficiency of the proposed approach is depicted by different performance indexes calculated on simulation results with a realistic, publicly available dataset. Among SVM models with different kernels, those with Gaussian (rbf) and sigmoid kernels have the highest performance indexes, hence they may be most suitable to be used for residential energy consumer occupancy prediction.

2021 ◽  
Vol 5 (3) ◽  
pp. 466-473
Author(s):  
Azam Zamhuri Fuadi ◽  
Irsyad Nashirul Haq ◽  
Edi Leksono

Predicted electricity consumption is needed to perform energy management. Electricity consumption prediction is also very important in the development of intelligent power grids and advanced electrification network information. we implement a Support Vector Machine (SVM) to predict electrical loads and results compared to measurable electrical loads. Laboratory electrical loads have their own characteristics when compared to residential, commercial, or industrial, we use electrical load data in energy management laboratories to be used to be predicted. C and Gamma as searchable parameters use GridSearchCV to get optimal SVM input parameters. Our prediction data is compared to measurement data and is searched for accuracy based on RMSE (Root Square Mean Error), MAE (Mean Absolute Error) and MSE (Mean Squared Error) values. Based on this we get the optimal parameter values C 1e6 and Gamma 2.97e-07, with the result RSME (Root Square Mean Error) ; 0.37, MAE (meaning absolute error); 0.21 and MSE (Mean Squared Error); 0.14.


High Temperature In The Summer Of India. Interpretation Of Electricity Consumption Is Crucial In Summer For Urban Consumers. We Are Focus Here For Only Indian Summer Urban Customers Energy Consumption To Analysis And Predict Behavior Of Electricity Theft. Data Mining Techniques Are Employing To Analyses Indian Summer Urban Customers. Online Sequential Machine And Support Vector Machine Is Used For This Behaviors Classification And Prediction.Mainly we focus Support vector machine to classified consumers and online sequential machine is used to detect and predict consumers behaviors.


2015 ◽  
Vol 109 (3) ◽  
pp. 34-39 ◽  
Author(s):  
Baraka Kichonge ◽  
Geoffrey R. John ◽  
Thomas Tesha ◽  
Iddi S.N. Mkilaha

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