Optimized Mathematical Model for Energy Efficient Construction Management in Smart Cities Using Building Information Modeling

Authors

  • Jing Wang Fuzhou Institute of Technology, Fuzhou, Fujian, China

DOI:

https://doi.org/10.13052/spee1048-5236.4113

Keywords:

Construction management, smart city, building information modeling, optimized mathematical model, energy management.

Abstract

Nowadays, a Smart city design brings smart buildings and structures and environmental using BIM. The performance and evaluation of the model are experimentally sustainability. Building Information Modeling (BIM) performance describes how to measure a construction project or entity’s capability and maturity in terms of development, utilization, and assessment. Energy fluctuation remains a barrier in such development and utilization. In this paper, an Optimized Mathematical Model for Energy Management (OMMEM) has been proposed to assess energy utilization in the construction management of smart cities analyzed by determining building information and distribution systems to the OMMEM performance analysis model. A collection of parameters and variables important for planning and prediction concerning the energy management of construction is acquired to model a smart infrastructure in a smart city. The findings revealed that the mathematical model provides a new method of evaluating the potential of the BIM application towards energy management in smart building construction of smart cities with high accuracy, performance with low delay and error rate.

Downloads

Download data is not yet available.

Author Biography

Jing Wang, Fuzhou Institute of Technology, Fuzhou, Fujian, China

Jing Wang was born in FuZhou, FuJian, P.R. China, in 1990. She graduated from Putian University in China with a bachelor’s degree. She is now working at Fuzhou Institute of Technology. Her research interest covers engineering cost and engineering management.

The research is supported by: Education and Scientific Research Project for Young and Middle-aged Teachers of The Education Department Of Fujian Province in 2019: Project Name: Research on performance evaluation of BIM prefabricated building (Project No.: JAT191022).

References

V ́azquez-Canteli, J. R., Ulyanin, S., K ̈ampf, J., and Nagy, Z. (2019). Fus-

ing TensorFlow with building energy simulation for intelligent energy

management in smart cities. Sustainable cities and society, 45, 243–257.

Calvillo, C. F., S ́anchez-Miralles, A., and Villar, J. (2016). Energy

management and planning in smart cities. Renewable and Sustainable

Energy Reviews, 55, 273–287.

Mahapatra, C., Moharana, A. K., and Leung, V. (2017). Energy manage-

ment in smart cities based on internet of things: Peak demand reduction

and energy savings. Sensors, 17(12), 2812.

Subramani Jegadeesan, Maria Azees, Priyan Malarvizhi Kumar,

Gunasekaran Manogaran, Naveen Chilamkurti, R Varatharajan, Ching-

Hsien, “An efficient anonymous mutual authentication technique for

providing secure communication in mobile cloud computing for smart

city applications”, Sustainable Cities and Society, Volume 49, Pages

Ejaz, W., Naeem, M., Shahid, A., Anpalagan, A., and Jo, M. (2017).

Efficient energy management for the internet of things in smart cities.

IEEE Communications Magazine, 55(1), 84–91.

Jegadeesan, S., Azees, M., Kumar, P. M., Manogaran, G., Chilamkurti,

N., Varatharajan, R., C. H. (2019). An efficient anonymous mutual

authentication technique provides secure communication in mobile

Optimized Mathematical Model for Energy Efficient Construction Management 77

cloud computing for smart city applications. Sustainable Cities and

Society, 49, 101522.

Sayah, Z., Kazar, O., Lejdel, B., Laouid, A., and Ghenabzia, A. (2020).

An intelligent system for energy management in smart cities based on

big data and ontology. Smart and Sustainable Built Environment.

Sharma, S., Dua, A., Singh, M., Kumar, N., and Prakash, S. (2018).

Fuzzy rough set based energy management system for self-sustainable

smart city. Renewable and Sustainable Energy Reviews, 82, 3633–3644.

Nguyen, N. T., and Liu, B. H. (2018). The mobile sensor deployment

problem and the target coverage problem in mobile wireless sensor

networks are NP-hard. IEEE Systems Journal, 13(2), 1312–1315.

Capozzoli, A., Piscitelli, M. S., Brandi, S., Grassi, D., and Chicco,

G. (2018). Automated load pattern learning and anomaly detection

for enhancing energy management in smart buildings. Energy, 157,

–352.

Kumar, N., Vasilakos, A. V., and Rodrigues, J. J. (2017). A multi tenant

cloud-based DC nano grid for self-sustained smart buildings in smart

cities. IEEE Communications Magazine, 55(3), 14–21.

Silva, B. N., Khan, M., and Han, K. (2020). Futuristic Sustainable

Energy Management in Smart Environments: A Review of Peak Load

Shaving and Demand Response Strategies, Challenges, and Opportuni-

ties. Sustainability, 12(14), 5561.

Amudha, G., and Narayanasamy, P. (2018). Distributed location and

trust based replica detection in wireless sensor networks. Wireless

Personal Communications, 102(4), 3303–3321.

Rizk M. Rizk-Allah, Aboul Ella Hassanien, Mohamed Elhoseny, A

Multi-Objective Transportation Model under Neutrosophic Environ-

ment, Computers and Electrical Engineering, Volume 69, July 2018,

Pages 705–719, (https://doi.org/10.1016/j.compeleceng.2018.02.024)

Gomathi, P., Baskar, S., and Shakeel, P. M. (2020). Concurrent service

access and management framework for user-centric future internet of

things in smart cities. Complex & Intelligent Systems. https://doi.org/10

.1007/s40747-020-00160-5

Jegadeesan, S., Azees, M., Kumar, P. M., Manogaran, G., Chilamkurti,

N., Varatharajan, R., and C. H. (2019). An efficient anonymous mutual

authentication technique for providing secure communication in mobile

cloud computing for smart city applications. Sustainable Cities and

Society, 49, 101522.

J. Wang

Golpˆıra, H., and Bahramara, S. (2020). Internet-of-things-based optimal

smart city energy management considering shiftable loads and energy

storage. Journal of Cleaner Production, 121620.

Brundu, F. G., Patti, E., Osello, A., Del Giudice, M., Rapetti, N.,

Krylovskiy, A., . . . and Acquaviva, A. (2016). IoT software infras-

tructure for energy management and simulation in smart cities. IEEE

Transactions on Industrial Informatics, 13(2), 832–840.

Qian Gao, Shanshan Guo, Xiaofu Liu, Gunasekaran Manogaran, Naveen

Chilamkurti, Seifedine Kadry, “Simulation analysis of supply chain risk

management system based on IoT information platform”, Enterprise

Information Systems, Pages 1–25.

Wang, S., Wu, J., and Zhang, Y. (2018). Consumer preference–enabled

intelligent energy management for smart cities using game theoretic

social tie. International Journal of Distributed Sensor Networks, 14(4),

Kim, D., Kwon, D., Park, L., Kim, J., and Cho, S. (2020). Multi-

scale LSTM-Based Deep Learning for Very-Short-Term Photovoltaic

Power Generation Forecasting in Smart City Energy Management. IEEE

Systems Journal.

Wu, X., Hu, X., Yin, X., and Moura, S. J. (2016). Stochastic optimal

energy management of smart home with PEV energy storage. IEEE

Transactions on Smart Grid, 9(3), 2065–2075.

Carli, R., Dotoli, M., and Pellegrino, R. (2016). A hierarchical decision-

making strategy for the energy management of smart cities. IEEE

Transactions on Automation Science and Engineering, 14(2), 505–523.

Liu, Y., Yang, C., Jiang, L., Xie, S., and Zhang, Y. (2019). Intelligent

edge computing for IoT-based energy management in smart cities. IEEE

Network, 33(2), 111–117.

Butt, A. A., Khan, S., Ashfaq, T., Javaid, S., Sattar, N. A., and Javaid, N.

(2019, June). A cloud and fog based architecture for energy management

of smart city by using meta-heuristic techniques. In 2019 15th Inter-

national Wireless Communications & Mobile Computing Conference

(IWCMC) (pp. 1588–1593). IEEE.

Lu, Y., Wu, Z., Chang, R., and Li, Y. (2017). Building Information Mod-

eling (BIM) for green buildings: A critical review and future directions.

Automation in Construction, 83, 134–148.

Singh, P., and Sadhu, A. (2019). Multi-component energy assessment of

buildings using building information modeling. Sustainable Cities and

Society, 49, 101603.

Optimized Mathematical Model for Energy Efficient Construction Management 79

Li, Y. W., and Cao, K. (2020). Establishment and application of intel-

ligent city building information model based on BP neural network

model. Computer Communications, 153, 382–389.

AlSaggaf, A., and Jrade, A. (2021). ArcSPAT: an integrated building

information modeling (BIM) and geographic information system (GIS)

model for site layout planning. International Journal of Construction

Management, 1–25.

Saravanan, V., Anpalagan, A., Poongodi, T., and Khan, F. (Eds.). (2020).

Securing IoT and Big Data: Next Generation Intelligence. CRC Press.

Saravanan, V., Singh, I., Szarek, E., Hak, J., and Pillai, A. S. A

Novel Implementation of Sentiment Analysis Toward Data Science.

In Applied Learning Algorithms for Intelligent IoT (pp. 175–192).

Auerbach Publications

Downloads

Published

2022-04-02

How to Cite

Wang, J. . (2022). Optimized Mathematical Model for Energy Efficient Construction Management in Smart Cities Using Building Information Modeling . Strategic Planning for Energy and the Environment, 41(1), 61–80. https://doi.org/10.13052/spee1048-5236.4113

Issue

Section

Articles