A deep learning approach to electric energy consumption modeling
Automated metering Infrastructure (AMI) is an integral part of a smart grid. Employing the data collected by the AMI from the consumers to generate accurate electricity consumption forecasts can help the utility in significantly improving the quality of service delivered to the consumer. Design and...
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Published in | Journal of intelligent & fuzzy systems Vol. 36; no. 5; pp. 4049 - 4055 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Amsterdam
IOS Press BV
01.01.2019
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Abstract | Automated metering Infrastructure (AMI) is an integral part of a smart grid. Employing the data collected by the AMI from the consumers to generate accurate electricity consumption forecasts can help the utility in significantly improving the quality of service delivered to the consumer. Design and empirical validation of machine learning based electric energy consumption forecasting systems, is presented in the present study. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Extreme Learning Machines (ELM) based models are designed and evaluated. One of the major aspects of the work is that the proposed consumption forecasting systems are designed as generalized models, i.e. one single model can be used to generate forecasts for any of the consumers considered, as opposed to the conventional technique of generating a separate model for each consumer. The forecasting systems are designed to generate half-hour-ahead and two-hour-ahead electric energy consumption forecasts. The proposed systems are validated on data for 485 Small and Medium Enterprise (SME) consumers in the CER electric energy consumption dataset. Results indicate that the models proposed in the present study result in good consumption forecast accuracy are hence, well suited for generating electric energy consumption forecast models. |
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AbstractList | Automated metering Infrastructure (AMI) is an integral part of a smart grid. Employing the data collected by the AMI from the consumers to generate accurate electricity consumption forecasts can help the utility in significantly improving the quality of service delivered to the consumer. Design and empirical validation of machine learning based electric energy consumption forecasting systems, is presented in the present study. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Extreme Learning Machines (ELM) based models are designed and evaluated. One of the major aspects of the work is that the proposed consumption forecasting systems are designed as generalized models, i.e. one single model can be used to generate forecasts for any of the consumers considered, as opposed to the conventional technique of generating a separate model for each consumer. The forecasting systems are designed to generate half-hour-ahead and two-hour-ahead electric energy consumption forecasts. The proposed systems are validated on data for 485 Small and Medium Enterprise (SME) consumers in the CER electric energy consumption dataset. Results indicate that the models proposed in the present study result in good consumption forecast accuracy are hence, well suited for generating electric energy consumption forecast models. |
Author | Nair, Binoy B. Balaji, A. Jayanth Harish Ram, D.S. |
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Cites_doi | 10.1109/CVPR.2015.7298761 10.1109/IJCNN.2015.7280624 10.1007/978-3-319-57264-2_26 10.1049/cp:19991218 10.1016/J.APENERGY.2018.09.190 10.1109/PES.2008.4596961 10.1109/SPAWC.2018.8445943 10.1007/978-3-319-33389-2_16 10.1016/J.PROCS.2018.05.050 |
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SubjectTerms | Artificial neural networks Consumers Deep learning Electricity consumption Energy consumption Forecasting Machine learning Mathematical models Neural networks Small business Smart grid Window treatments |
Title | A deep learning approach to electric energy consumption modeling |
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