Bayesian model averaging of load demand forecasts from neural network models
Creating a set of a number of neural network (NN) models in an ensemble and accumulating them can achieve better overview capability as compared to single neural network. Neural network ensembles are designed to provide solutions to particular problems. Many researchers and academicians have adopted...
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2013
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my.utp.eprints.326772022-03-30T01:02:27Z Bayesian model averaging of load demand forecasts from neural network models Hassan, S. Khosravi, A. Jaafar, J. Creating a set of a number of neural network (NN) models in an ensemble and accumulating them can achieve better overview capability as compared to single neural network. Neural network ensembles are designed to provide solutions to particular problems. Many researchers and academicians have adopted this NN ensemble technique, especially in machine learning, and has been applied in various fields of engineering, medicine and information technology. This paper present a robust aggregation methodology for load demand forecasting based on Bayesian Model Averaging of a set of neural network models in an ensemble. This paper estimate a vector of coefficient for individual NN models' forecasts using validation data-set. These coefficients, also known as weights, are equal to posterior probabilities of the models generating the forecasts. These BMA weights are then used in combining forecasts generated from NN models with test data-set. By comparing the Bayesian results with the Simple Averaging method, it was observed that benefits are obtained by utilizing an advanced method like BMA for forecast combinations. © 2013 IEEE. 2013 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893573237&doi=10.1109%2fSMC.2013.544&partnerID=40&md5=86c3f59f36e76a37732301f37ef4406c Hassan, S. and Khosravi, A. and Jaafar, J. (2013) Bayesian model averaging of load demand forecasts from neural network models. In: UNSPECIFIED. http://eprints.utp.edu.my/32677/ |
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Creating a set of a number of neural network (NN) models in an ensemble and accumulating them can achieve better overview capability as compared to single neural network. Neural network ensembles are designed to provide solutions to particular problems. Many researchers and academicians have adopted this NN ensemble technique, especially in machine learning, and has been applied in various fields of engineering, medicine and information technology. This paper present a robust aggregation methodology for load demand forecasting based on Bayesian Model Averaging of a set of neural network models in an ensemble. This paper estimate a vector of coefficient for individual NN models' forecasts using validation data-set. These coefficients, also known as weights, are equal to posterior probabilities of the models generating the forecasts. These BMA weights are then used in combining forecasts generated from NN models with test data-set. By comparing the Bayesian results with the Simple Averaging method, it was observed that benefits are obtained by utilizing an advanced method like BMA for forecast combinations. © 2013 IEEE. |
| format |
Conference or Workshop Item |
| author |
Hassan, S. Khosravi, A. Jaafar, J. |
| spellingShingle |
Hassan, S. Khosravi, A. Jaafar, J. Bayesian model averaging of load demand forecasts from neural network models |
| author_facet |
Hassan, S. Khosravi, A. Jaafar, J. |
| author_sort |
Hassan, S. |
| title |
Bayesian model averaging of load demand forecasts from neural network models |
| title_short |
Bayesian model averaging of load demand forecasts from neural network models |
| title_full |
Bayesian model averaging of load demand forecasts from neural network models |
| title_fullStr |
Bayesian model averaging of load demand forecasts from neural network models |
| title_full_unstemmed |
Bayesian model averaging of load demand forecasts from neural network models |
| title_sort |
bayesian model averaging of load demand forecasts from neural network models |
| publishDate |
2013 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893573237&doi=10.1109%2fSMC.2013.544&partnerID=40&md5=86c3f59f36e76a37732301f37ef4406c http://eprints.utp.edu.my/32677/ |
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