It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
Electricity consumption forecasting plays a significant role in planning electric systems. However, this can only be achieved if the demand is accurate estimation .This research, different forecasting methods hybrid SARIMA-ANN and hybrid model by SARIMA- Gaussian Processes with combine Kernel Function technique were utilized to formulate forecasting models of the electricity consumption . The objective was to compare the performance of two approaches and the empirical data used in this study was the historical data regarding the electricity consumption (gross domestic product: GDP, forecast values calculated by SARIMA model and electricity consumption) in Thailand from 2005 to 2015. New Kernel Function design techniques for forecasting under Gaussian processes are presented in sum and product formats. The results showed that the hybrid model by SARIMA - Gaussian Processes with combine Kernel Function technique outperformed the SARIMA-ANN model have the Mean absolute percentage error is 4.7072e-09, 4.8623 respectively.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer