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Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands

Luo, X. J.; Oyedele, Lukumon O.; Ajayi, Anuoluwapo O.; Monyei, Chukwuka G.; Akinade, Olugbenga O.; Akanbi, Lukman A.

Authors

Xiaojun Luo Xiaojun.Luo@uwe.ac.uk
Research Fellow - Evolutionary Computing and Optimisation

Chukwuka Monyei Chukwuka.Monyei@uwe.ac.uk
Research Fellow - Demand Side Energy Management & Big Data Analytics

Olugbenga Akinade Olugbenga.Akinade@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence

Dr Lukman Akanbi Lukman.Akanbi@uwe.ac.uk
Associate Professor - Big Data Application Developer



Abstract

© 2019 Elsevier Ltd The emerging technologies of the Internet of Things (IoT) and big data can be utilised to derive knowledge and support applications for energy-efficient buildings. Effective prediction of heating and cooling demands is fundamental in building energy management. In this study, a 4-layer IoT-based big data platform is developed for day-ahead prediction of building energy demands, while the core part is the hybrid machine learning-based predictive model. The proposed energy demand predictive model is based on the hybrids of k-means clustering and artificial neural network (ANN). Due to different temperatures of walls, windows, grounds, roofs and indoor air, various IoT sensors are installed at different locations of the building. To determine the input variables to the hybrid machine learning-based predictive model, correlation analysis is adopted. Through clustering analysis, the characteristic patterns of daily weather profile are identified. Thus, the annual profile is classified into several featuring groups. Each group of weather profile, along with IoT sensor readings, building operating schedules as well as heating and cooling demands, is used to train the sub-ANN predictive models. Due to the involvement of IoT sensors, the overall prediction accuracy can be improved. It is found that the mean absolute percentage error of energy demands prediction is 3% and 8% in training and testing cases, respectively.

Citation

Luo, X. J., Oyedele, L. O., Ajayi, A. O., Monyei, C. G., Akinade, O. O., & Akanbi, L. A. (2019). Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands. Advanced Engineering Informatics, 41, https://doi.org/10.1016/j.aei.2019.100926

Journal Article Type Article
Acceptance Date May 17, 2019
Online Publication Date May 23, 2019
Publication Date Aug 1, 2019
Deposit Date May 31, 2019
Publicly Available Date May 24, 2020
Journal Advanced Engineering Informatics
Print ISSN 1474-0346
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 41
Article Number 100926
DOI https://doi.org/10.1016/j.aei.2019.100926
Keywords Day-ahead prediction; Clustering; Artificial neural network; Building heating and cooling demand; Internet of Things; Big data
Public URL https://uwe-repository.worktribe.com/output/845793
Publisher URL http://doi.org/10.1016/j.aei.2019.100926
Additional Information Additional Information : This is the author's accepted manuscript. The final published version is available here: http://doi.org/10.1016/j.aei.2019.100926.

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