Multi-Kernel Learning based Sugar Industry Load Forecasting
Yamanappa Doddamani1, Ravindra R Malagi2, U C Kapale3
1Yamanappa. N. Doddamani,* Research Scholar, Visvesvaraya Technical University, Belagavi, , Karnataka, India.
2Ravindra R Malagi, Research Scholar, Visvesvaraya Technical University, Belagavi, , Karnataka, India.
3U C Kapale, Research Scholar, Visvesvaraya Technical University, Belagavi, , Karnataka, India.
Manuscript received on January 22, 2021. | Revised Manuscript received on January 31, 2021. | Manuscript published on January 30, 2021. | PP: 275-278 | Volume-9 Issue-5, January 2021. | Retrieval Number: 100.1/ijrte.E5304019521 | DOI: 10.35940/ijrte.E5304.019521
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sugar industry which plans for power usage from Bagasse also needs the load forecasting carried out using the energy audit data. The stochastic nature of the load demand of the sugar industry needs to be forecasted in advance for the assuring uninterrupted power delivery to the industry. The manual energy audit data obtained from the sugar industry for a period of time is obtained and trained on a regression based on Multi Kernel Learning (MKL). The Support Vector Regression (SVR) formulation is applied with the Multi Kernel topology and the performance parameters including the Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) is observed in the implementation. The algorithm is the Multi Kernel Support Vector Regression algorithm using the Python based toolbox.
Keywords: Multi Kernel Learning, Support Vector Regression, Load Forecasting, Sugar Industry Energy Audit.