A Machine Learning-based Building Operational Pattern Identification

doi:10.23940/ijpe.20.11.p15.18351844

Abstract

Abstract: Creativity has become more important in modern life due to the development of quality of life. The paper first discusses the relationships among creativity, building environment, and machine learning to illustrate that an energy conservative building environment can raise occupants' creativity and satisfaction. Then, an energy conservative way aided by machine learning is proposed. The building operational patterns are identified using this machine-learning model. The studied database builds air-conditioning energy consumption of a Singaporean school. The clustering process first removes inconsistent and null data. Then, four features are selected from the original 18 features based on the domain knowledge and the characteristics of the dataset. The k-means algorithm is used to discover the hidden building operational patterns in the building energy consumption information. The results show that the three derived clusters can be well interpreted by domain knowledge. An energy conservation measure is carried out based on further investigation on the clusters. The data mining process proposed in this article is a creative way for intelligent building. Future research could also be conducted based on the creative method in this article; possible future directions are highlighted in the last section.

Key words: machine learning, building operational patterns, building energy conservation, k-means

Mingzhu Li and Yufeng Deng. A Machine Learning-based Building Operational Pattern Identification [J]. Int J Performability Eng, 2020, 16(11): 1835-1844.

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References

1. Y. Liu and S. C. S. Jang, “Perceptions of Chinese Restaurants in the US: What Affects Customer Satisfaction and Behavioral Intentions?” International Journal of Hospitality Management, Vol. 28, No. 3, pp. 338-348, 2009
2. I. Borovskaia and M. Dedova, “Creativity in Hospitality Industry: Study of Hostels in St. Petersburg,” Coactivity Philosophy Communication, Vol. 22, No. 2, pp. 137-144, 2014
3. Z. H. Zhou, “Machine Learning,” Tsinghua University Press, 2016
4. L. R. Varshney, F. Pinel, K. R. Varshney, et al., “A Big Data Approach to Computational Creativity,” arXiv:1311.1213, 2013
5. P. Covington, J. Adams,E. Sargin, “Deep Neural Networks for Youtube Recommendations,” inProceedings of the 10th ACM Conference on Recommender Systems, pp. 191-198, ACM, 2016
6. J. S. Horng, C. H. Liu, S. F. Chou,C. Y. Tsai, “Creativity as a Critical Criterion for Future Restaurant Space Design: Developing a Novel Model with DEMATEL Application,” International Journal of Hospitality Management, Vol. 33, No. 1, pp. 96-105, 2013
7. J. S. Horng, S. F. Chou, C. H. Liu,C. Y. Tsai, “Creativity, Aesthetics and Eco-Friendliness: A Physical Dining Environment Design Synthetic Assessment Model of Innovative Restaurants,” Tourism Management, Vol. 36, No. 3, pp. 15-25, 2013
8. A. M. Nguyen, J. Yosinski,J. Clune, “Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning,” inProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 959-966, ACM, 2015
9. M. F.Peschl and T. Fundneider, “Creativity and Innovation in a Mid-Urban Size Learning Infrastructure-Designing Spaces for Thriving Innovation Communities,”REAL CORP, pp. 205-211, 2013
10. K. Oksanen and P. Ståhle P, “Physical Environment as a Source for Innovation: Investigating the Attributes of Innovative Space,” Journal of Knowledge Management, Vol. 17, No. 6, pp. 815-827, 2013
11. M. Faizi, A. K.Azari and S. N. Maleki, “Design Guidelines of Residential Environments to Stimulate Children's Creativity,” Journal of Asian Behavioural Studies, Vol. 3, No. 6, pp. 65-73, 2018
12. G. Dove, K. Halskov, J. Forlizzi,J. Zimmerman, “UX Design Innovation: Challenges for Working with Machine Learning as a Design Material,” in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems,” pp. 278-288, ACM, 2017
13. Z. Bylinskii, N. W. Kim, P. O'Donovan, S. Alsheikh, S. Madan, H. Pfister, et al., “Learning Visual Importance for Graphic Designs and Data Visualizations,” inProceedings of the 30th Annual ACM Symposium on User Interface Software and Technology, pp. 57-69, ACM, 2017
14. P. Covington, J. Adams,E. Sargin, “Deep Neural Networks for Youtube Recommendations,” inProceedings of the 10th ACM Conference on Recommender Systems, pp. 191-198, ACM, 2016
15. J. S.Gero and M. L. Maher, “Modeling Creativity and Knowledge-based Creative Design,” Psychology Press, 2013
16. C. F. Lin, Y. C. Yeh, Y. H. Hung,R. I. Chang, “Data Mining for Providing a Personalized Learning Path in Creativity: An Application of Decision Trees,”Computers and Education, Vol. 68, pp. 199-210, 2013
17. W. Wikhamn, “Innovation, Sustainable HRM and Customer Satisfaction,”International Journal of Hospitality Management, Vol. 76, pp. 102-110, 2019
18. S. Chang, Y. Gong,C. Shum, “Promoting Innovation in Hospitality Companies Through Human Resource Management Practices,” International Journal of Hospitality Management, Vol. 30, No. 4, pp. 812-818, 2011
19. J. S. Horng, C. H. Liu, S. F. Chou, C. Y. Tsai,Y. C. Chung, “From Innovation to Sustainability: Sustainability Innovations of Eco-Friendly Hotels in Taiwan,”International Journal of Hospitality Management, Vol. 63, pp. 44-52, 2017
20. L. C. Leonidou, C. N. Leonidou, T. A. Fotiadis,A. Zeriti, “Resources and Capabilities as Drivers of Hotel Environmental Marketing Strategy: Implications for Competitive Advantage and Performance,”Tourism Management, Vol. 35, pp. 94-110, 2013
21. Z. Wang and R. S. Srinivasan, “A Review of Artificial Intelligence based Building Energy Use Prediction: Contrasting the Capabilities of Single and Ensemble Prediction Models,” in Proceedings of the2015 Winter Simulation Conference, pp. 3438-3448, 2015
22. International Energy Agency (IEA),(https://www.iea.org/aboutus/faqs/ energyefficiency/, 2014)
23. L. Pérez-Lombard, J. Ortiz,C. Pout, “A Review on Buildings Energy Consumption Information,” Energy and Buildings, Vol. 40, No. 3, pp. 394-398, 2008
24. T. Ramesh, R. Prakash,K. K. Shukla, “Life Cycle Energy Analysis of Buildings: An Overview,” Energy and Buildings, Vol. 42, No. 10, pp. 1592-1600, 2010
25. U. Sbci, “Buildings and Climate Change: Summary for Decision-Makers,” 2009
26. K. Zhou, C. Fu,S. Yang, “Big Data Driven Smart Energy Management: From Big Data to Big Insights,”Renewable and Sustainable Energy Reviews, Vol. 56, pp. 215-225, 2016
27. F. W.Yu and K. T. Chan, “Using Cluster and Multivariate Analyses to Appraise the Operating Performance of a Chiller System Serving an Institutional Building,” Energy and Buildings, Vol. 44, No. 1, pp. 104-113, 2012
28. P. Waide, J. Ure, N. Karagianni, et al., “The Scope for Energy and CO₂ Savings in the EU Through the Use of Building Automation Technology,” Final Report for the European Copper Institute, 2013
29. C. Fan, L. F. Xiao, Z. D. Li,J. Y. Wang, “Unsupervised Data Analytics in Mining Big Building Operational Data for Energy Efficiency Enhancement: A Review,”Energy and Buildings, Vol. 159, 2018
30. P. N. Xue, Z. G. Zhou, X. M. Fang, X. Chen, L. Liu, Y. W. Liu, et al., “Fault Detection and Operation Optimization in District Heating Substations based on Data Mining Techniques,”Applied Energy, Vol. 205, pp. 926-940, 2017
31. S. Idowu, S. Saguna, C. Åhlund,O. Schelen, “Applied Machine Learning: Forecasting Heat Load in District Heating System,”Energy and Buildings, Vol. 133, pp. 478-488, 2016
32. C. Fan, F. Xiao,Y. Zhao, “A Short-Term Building Cooling Load Prediction Method using Deep Learning Algorithms,”Applied Energy, Vol. 195, pp. 222-233, 2017
33. C. Robinson, B. Dilkina, J. Hubbs, W. W. Zhang, S. Guhathakurta, M. A. Brown, et al., “Machine Learning Approaches for Estimating Commercial Building Energy Consumption,”Applied Energy, Vol. 208, pp. 889-904, 2017
34. G. Li, Y. Hu, H. Chen, et al., “Data Partitioning and Association Mining for Identifying VRF Energy Consumption Patterns under Various Part Loads and Refrigerant Charge Conditions,”Applied Energy, Vol. 185, pp. 846-861, 2017
35. C. Fan, F. Xiao,C. Yan, “A Framework for Knowledge Discovery in Massive Building Automation Data and Its Application in Building Diagnostics,”Automation in Construction, Vol. 50, pp. 81-90, 2015
36. C. Miller, Z. Nagy,A. Schlueter, “Automated Daily Pattern Filtering of Measured Building Performance Data,”Automation in Construction, Vol. 49, pp. 1-17, 2015
37. C. Miller, Z. Nagy,A. Schlueter, “A Seed Dataset for a Public, Temporal Data Repository for Energy Informatics Research on Commercial Building Performance,” inProceeding of the 3rd Conference on Future Energy Business and Energy Informatics, pp. 1-2, Rotterdam, Netherlands, 2014

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