Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city

The fast development of urban advancement in the past decade requires reasonable and realistic solutions for transport, building infrastructure, natural conditions, and personal satisfaction in smart cities. This paper presents and explores predictive energy consumption models based on data-mining t...

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Bibliographic Details
Published in:Building research and information : the international journal of research, development and demonstration development and demonstration, 2021-01, Vol.49 (1), p.127-143
Main Authors: V E, Sathishkumar, Shin, Changsun, Cho, Yongyun
Format: Article
Language:English
Subjects:
Algorithms
Carbon dioxide
Carbon dioxide emissions
Coefficient of variation
data analysis
Data mining
Design optimization
Energy consumption
Energy efficiency
Energy management
feature ranking
Iron and steel industry
Power factor
Prediction models
Reactive power
Regression analysis
Smart cities
Statistical analysis
Steel industry
Structural design
Structural engineering
Support vector machines
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Summary:The fast development of urban advancement in the past decade requires reasonable and realistic solutions for transport, building infrastructure, natural conditions, and personal satisfaction in smart cities. This paper presents and explores predictive energy consumption models based on data-mining techniques for a smart small-scale steel industry in South Korea. Energy consumption data is collected using IoT based systems and used for prediction. Data used include the lagging and leading current reactive power, the lagging and leading current power factor, carbon dioxide emissions, and load types. Five statistical algorithms are used for energy consumption prediction:(a) General linear regression, (b) Classification and regression trees, (c) Support vector machine with a radial basis kernel, (d) K nearest neighbours, (e) CUBIST. Root mean squared error, Mean absolute error and Coefficient of variation are used to measure the prediction efficiency of the models. The results show that CUBIST model provides best results with lower error values and this model can be used for the development of energy efficient structural design which helps to optimize the energy consumption and policy making in smart cities.
ISSN:0961-3218
1466-4321
DOI:10.1080/09613218.2020.1809983