Examining performance of aggregation algorithms for neural network-based electricity demand forecasting
The aim of this research is to examine the efficiency of different aggregation algorithms to the forecasts obtained from individual neural network (NN) models in an ensemble. In this study an ensemble of 100 NN models are constructed with a heterogeneous architecture. The outputs from NN models are...
| Main Authors: | Hassan, S., Khosravi, A., Jaafar, J. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.26140 / |
| Published: |
Elsevier Ltd
2015
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907755879&doi=10.1016%2fj.ijepes.2014.08.025&partnerID=40&md5=29a4cada6d55259477ea44336663b025 http://eprints.utp.edu.my/26140/ |
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utp-eprints.261402021-08-30T06:50:02Z Examining performance of aggregation algorithms for neural network-based electricity demand forecasting Hassan, S. Khosravi, A. Jaafar, J. The aim of this research is to examine the efficiency of different aggregation algorithms to the forecasts obtained from individual neural network (NN) models in an ensemble. In this study an ensemble of 100 NN models are constructed with a heterogeneous architecture. The outputs from NN models are combined by three different aggregation algorithms. These aggregation algorithms comprise of a simple average, trimmed mean, and a Bayesian model averaging. These methods are utilized with certain modifications and are employed on the forecasts obtained from all individual NN models. The output of the aggregation algorithms is analyzed and compared with the individual NN models used in NN ensemble and with a Naive approach. Thirty-minutes interval electricity demand data from Australian Energy Market Operator (AEMO) and the New York Independent System Operator's web site (NYISO) are used in the empirical analysis. It is observed that the aggregation algorithm perform better than many of the individual NN models. In comparison with the Naive approach, the aggregation algorithms exhibit somewhat better forecasting performance. © 2014 Elsevier Ltd. All rights reserved. Elsevier Ltd 2015 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907755879&doi=10.1016%2fj.ijepes.2014.08.025&partnerID=40&md5=29a4cada6d55259477ea44336663b025 Hassan, S. and Khosravi, A. and Jaafar, J. (2015) Examining performance of aggregation algorithms for neural network-based electricity demand forecasting. International Journal of Electrical Power and Energy Systems, 64 . pp. 1098-1105. http://eprints.utp.edu.my/26140/ |
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| description |
The aim of this research is to examine the efficiency of different aggregation algorithms to the forecasts obtained from individual neural network (NN) models in an ensemble. In this study an ensemble of 100 NN models are constructed with a heterogeneous architecture. The outputs from NN models are combined by three different aggregation algorithms. These aggregation algorithms comprise of a simple average, trimmed mean, and a Bayesian model averaging. These methods are utilized with certain modifications and are employed on the forecasts obtained from all individual NN models. The output of the aggregation algorithms is analyzed and compared with the individual NN models used in NN ensemble and with a Naive approach. Thirty-minutes interval electricity demand data from Australian Energy Market Operator (AEMO) and the New York Independent System Operator's web site (NYISO) are used in the empirical analysis. It is observed that the aggregation algorithm perform better than many of the individual NN models. In comparison with the Naive approach, the aggregation algorithms exhibit somewhat better forecasting performance. © 2014 Elsevier Ltd. All rights reserved. |
| format |
Article |
| author |
Hassan, S. Khosravi, A. Jaafar, J. |
| spellingShingle |
Hassan, S. Khosravi, A. Jaafar, J. Examining performance of aggregation algorithms for neural network-based electricity demand forecasting |
| author_sort |
Hassan, S. |
| title |
Examining performance of aggregation algorithms for neural network-based electricity demand forecasting |
| title_short |
Examining performance of aggregation algorithms for neural network-based electricity demand forecasting |
| title_full |
Examining performance of aggregation algorithms for neural network-based electricity demand forecasting |
| title_fullStr |
Examining performance of aggregation algorithms for neural network-based electricity demand forecasting |
| title_full_unstemmed |
Examining performance of aggregation algorithms for neural network-based electricity demand forecasting |
| title_sort |
examining performance of aggregation algorithms for neural network-based electricity demand forecasting |
| publisher |
Elsevier Ltd |
| publishDate |
2015 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907755879&doi=10.1016%2fj.ijepes.2014.08.025&partnerID=40&md5=29a4cada6d55259477ea44336663b025 http://eprints.utp.edu.my/26140/ |
| _version_ |
1741197089719713792 |
| score |
11.62408 |