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User-Based Load Visualization of Categorical Forecasted Smart Meter Data Using LSTM Network
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Author(s): Ajay Kumar (JSS Academy of Technical Education, Noida, India), Parveen Poon Terang (JSS Academy of Technical Education, Noida, India)and Vikram Bali (JSS Academy of Technical Education, Noida, India)
Copyright: 2020
Volume: 11
Issue: 1
Pages: 21
Source title:
International Journal of Multimedia Data Engineering and Management (IJMDEM)
Editor(s)-in-Chief: Chengcui Zhang (University of Alabama at Birmingham, USA)and Shu-Ching Chen (University of Missouri-Kansas City, United States)
DOI: 10.4018/IJMDEM.2020010103
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Abstract
Electrical load forecasting is an essential feature in power systems planning, operation and control. The non-linearity and non-stationary nature of the data, however, poses a challenge in terms of accuracy. This article explores a deep learning technique, a long short-term memory recurrent neural network-based framework to tackle this tricky issue. The proposed machine learning model framework is tested on real time residential smart meter data showing promising results. A web application has also been developed to allow consumers to have access to greater levels of information and facilitate decision-making at their end. The performance of the proposed model is also comprehensively compared to other methods in the field of load forecasting showing more accurate results for the function of forecasting of load on short term basis.
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