Daily peak electricity load forecasting in South Africa using a multivariate non-parametric regression approach

C Sigauke; D Chikobvu

doi:10.5784/26-2-89

Daily peak electricity load forecasting in South Africa using a multivariate non-parametric regression approach

ORiON ◽

10.5784/26-2-89 ◽

2010 ◽

Vol 26 (2) ◽

Cited By ~ 15

Author(s):

C Sigauke ◽

D Chikobvu

Keyword(s):

South Africa ◽

Load Forecasting ◽

Parametric Regression ◽

Regression Approach ◽

Electricity Load ◽

Electricity Load Forecasting ◽

Non Parametric

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A frequentist and Bayesian regression analysis to daily peak electricity load forecasting in South Africa

AFRICAN JOURNAL OF BUSINESS MANAGEMENT ◽

10.5897/ajbm12.719 ◽

2012 ◽

Vol 6 (40) ◽

pp. 10524-10533 ◽

Cited By ~ 4

Author(s):

Chikobvu D ◽

Sigauke C

Keyword(s):

South Africa ◽

Regression Analysis ◽

Load Forecasting ◽

Bayesian Regression ◽

Electricity Load ◽

Electricity Load Forecasting

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Short-term electricity load forecasting with special days: an analysis on parametric and non-parametric methods

Annals of Operations Research ◽

10.1007/s10479-017-2726-6 ◽

2017 ◽

Cited By ~ 2

Author(s):

Esra Erişen ◽

Cem Iyigun ◽

Fehmi Tanrısever

Keyword(s):

Load Forecasting ◽

Parametric Methods ◽

Short Term ◽

Electricity Load ◽

Electricity Load Forecasting ◽

Non Parametric

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A hybrid rough sets and support vector regression approach to short-term electricity load forecasting

2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century ◽

10.1109/pes.2008.4596688 ◽

2008 ◽

Cited By ~ 1

Author(s):

Fang Ruiming

Keyword(s):

Support Vector Regression ◽

Rough Sets ◽

Load Forecasting ◽

Support Vector ◽

Short Term ◽

Regression Approach ◽

Electricity Load ◽

Electricity Load Forecasting

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Artificial neural network in day-ahead electricity load forecasting for large steel factory

18th International Conference and Exhibition on Electricity Distribution (CIRED 2005) ◽

10.1049/cp:20051387 ◽

2005 ◽

Cited By ~ 1

Author(s):

B. Swiatek ◽

R. Klempka ◽

J. Dzieza

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Load Forecasting ◽

Steel Factory ◽

Large Steel ◽

Electricity Load ◽

Artificial Neural ◽

Electricity Load Forecasting

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Micro-Spatial Electricity Load Forecasting Using Clustering Technique

2020 2nd International Conference on Industrial Electrical and Electronics (ICIEE) ◽

10.1109/iciee49813.2020.9277274 ◽

2020 ◽

Author(s):

Christine Widyastuti ◽

Adri Senen ◽

Oktaria Handayani

Keyword(s):

Load Forecasting ◽

Clustering Technique ◽

Electricity Load ◽

Electricity Load Forecasting

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Classification of electricity load forecasting based on the factors influencing the load consumption and methods used: An-overview

2015 IEEE Conference on Energy Conversion (CENCON) ◽

10.1109/cencon.2015.7409585 ◽

2015 ◽

Cited By ~ 10

Author(s):

M. Mustapha ◽

M. W. Mustafa ◽

S. N Khalid ◽

I. Abubakar ◽

H. Shareef

Keyword(s):

Load Forecasting ◽

Factors Influencing ◽

Electricity Load ◽

Electricity Load Forecasting

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Household Level Electricity Load Forecasting Using Echo State Network

2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) ◽

10.1109/smartgridcomm47815.2020.9302961 ◽

2020 ◽

Author(s):

Debneil Saha Roy

Keyword(s):

Load Forecasting ◽

Echo State Network ◽

Household Level ◽

Electricity Load ◽

Electricity Load Forecasting

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An adaptive backpropagation algorithm for long-term electricity load forecasting

Neural Computing and Applications ◽

10.1007/s00521-021-06384-x ◽

2021 ◽

Author(s):

Nooriya A. Mohammed ◽

Ammar Al-Bazi

Keyword(s):

Load Forecasting ◽

Backpropagation Algorithm ◽

Electricity Load ◽

Electricity Load Forecasting

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Analysis of temperature-sensitive on short-term electricity load forecasting

2020 IEEE-HYDCON ◽

10.1109/hydcon48903.2020.9242910 ◽

2020 ◽

Author(s):

Nguyen Xuan Tung ◽

Nguyen Quang Dat ◽

Tran Ngoc Thang ◽

Vijender Kumar Solanki ◽

Nguyen Thi Ngoc Anh

Keyword(s):

Load Forecasting ◽

Temperature Sensitive ◽

Short Term ◽

Electricity Load ◽

Electricity Load Forecasting

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Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting

Measurement ◽

10.1016/j.measurement.2017.02.007 ◽

2017 ◽

Vol 103 ◽

pp. 52-61 ◽

Cited By ~ 43

Author(s):

Yusuf Yaslan ◽

Bahadır Bican

Keyword(s):

Time Series ◽

Support Vector Regression ◽

Time Series Prediction ◽

Load Forecasting ◽

Support Vector ◽

Denoising Method ◽

Mode Decomposition ◽

Electricity Load ◽

Electricity Load Forecasting

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