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A deep learning approach to electric energy consumption modeling

Publication Type : Journal Article

Publisher : IOS Press

Source : Journal of Intelligent and Fuzzy Systems, IOS Press, Volume 36, Number 5, p.4049-4055 (2019)

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Keywords : Advanced metering infrastructures, Automated metering infrastructure (AMI), Conventional techniques, Convolutional neural network, Deep learning, Electric energy consumption, Electric power generation, Electricity-consumption, Energy utilization, Extreme learning machine, Forecasting, Learning systems, Long short-term memory, LSTM, Machine learning, Quality of service, Small and medium enterprise

Campus : Coimbatore

School : School of Engineering

Department : Electronics and Communication

Year : 2019

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. © 2019 - IOS Press and the authors.

Cite this Research Publication : A. J. Balaji, Ram, D. S. Harish, and Dr. Binoy B. Nair, “A deep learning approach to electric energy consumption modeling”, Journal of Intelligent and Fuzzy Systems, vol. 36, pp. 4049-4055, 2019.

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