Caston Sigauke

Caston is a statistical modeller with research interests in probabilistic forecasting with applications in energy and environmental systems. Caston’s research is on probabilistic load and renewable energy (solar and wind) forecasting including the optimization of grid integration of renewable energies. He is a C3 National Research Foundation of South Africa rated researcher.

Publications

Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods

Lucky O. Daniel, Caston Sigauke, Colin Chibaya, Rendani Mbuvha

Algorithms, vol. 13(6), 2020 Apr 26

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

Phathutshedzo Mpfumali, Caston Sigauke, Alphonce Bere, Sophie Mulaudzi

Energies, vol. 12(18), 2019 Aug 18

Reliable Predictions of Peak Electricity Demand and Reliability of Power System Management

Caston Sigauke, Santosh Kumar, Norman Maswanganyi, Edmore Ranganai

2018 Aug 21, pp. 137-160

Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models

Caston Sigauke, Murendeni Maurel Nemukula, Daniel Maposa

Energies, vol. 11(9), 2018 Jul 23

Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem

Moshoko Emily Lebotsa, Caston Sigauke, Alphonce Bere, Robert Fildes, John E. Boylan

Applied Energy, vol. 222, 2018 Jun 15, pp. 104-118

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Projects

Probabilistic Load and Renewable Energy (solar and wind) Forecasting

Short term probabilistic load forecasting using quantile regression with an application to the unit commitment problem. Long-term peak electricity demand using extremal quantile regression. Renewable energy (solar and wind) forecasting and modelling.