scholarly journals Development of an electrical load demand and response model based on a rational synthesis from elementary devices

1979 ◽  
Author(s):  
Keyword(s):  
Response Model ◽  
Electrical Load ◽  
Model Based ◽  
Load Demand

scholarly journals Electrical Load Forecasting using SVM Algorithm

2020 ◽  
Vol 8 (6) ◽  
pp. 4811-4816
Keyword(s):  
Support Vector Machine ◽  
Load Forecasting ◽  
Percentage Error ◽  
Support Vector ◽  
Live Load ◽  
Electric Load ◽  
Mean Square ◽  
Electrical Load ◽  
Load Demand ◽  
Svm Algorithm

Electrical load demand is variable in nature. Also, with the increase in technological development and automation, electric load demand tends to rise with time. For this, our generation facilities should be adequate 24x7 to meet the consumer’s load demand effectively. Therefore, load demand needs to be predicted or forecasted to avoid the energy crisis. In this paper, support vector machine (SVM) algorithm is explored for electric load forecasting. The live load data for the period of three months i.e., January to March, 2015, from a typical 66kV sub-station of the Punjab State Power Corporation Limited (PSPCL) for a selected site at Bhai Roopa sub-station, Bathinda, situated in the Punjab state of India, is acquired for the presented simulation study. The collected live data is divided into three categories, i.e., validation, training, and testing for the simulation study considering a SVM approach. Then, based on the environmental data input for the next 50 hours, the electric load is predicted. The obtained results from simulation were validated with the live load data of the selected site and found to be within the permissible limits. The mean square error (MSE), root-mean-square error (RMSE), mean absolute error (MAE), absolute percentage error (APE), mean absolute percentage error (MAPE) and sum of squares error (SSE) were calculated to show the effectiveness of the proposed support vector machine (SVM) algorithm based STLF. SVM is one of the effective machine learning algorithms. The errors so obtained clearly suggest that the proposed SVM algorithm gives reasonably accurate results, and is reliable for electric load forecasting.

scholarly journals Author response: Model-based fMRI reveals dissimilarity processes underlying base rate neglect

2018 ◽  
Author(s):  
Sean R O'Bryan ◽  
Darrell A Worthy ◽  
Evan J Livesey ◽  
Tyler Davis
Keyword(s):  
Base Rate ◽  
Author Response ◽  
Response Model ◽  
Model Based ◽  
Base Rate Neglect

scholarly journals A Proficient Two-Stage Stratified Randomized Response Strategy

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Tanveer A. Tarray ◽  
Housila P. Singh
Keyword(s):  
Optimal Allocation ◽  
Response Strategy ◽  
Response Model ◽  
Randomized Response ◽  
Two Stage ◽  
Model Based ◽  
Large Gain ◽  
Proposed Model ◽  
Randomized Response Model

A stratified randomized response model based on R. Singh, Singh, Mangat, and Tracy (1995) improved two-stage randomized response strategy is proposed. It has an optimal allocation and large gain in precision. Conditions are obtained under which the proposed model is more efficient than R. Singh et al. (1995) and H. P. Singh and Tarray (2015) models. Numerical illustrations are also given in support of the present study.

Sign in / Sign up

Export Citation Format

Share Document