PROVIDE A METHOD IMPROVING TEMPERATURE CONTROL IN SMART BUILDINGS BASED IN SLICING TECHNIQUE AND CLUSTERING IOT NETWORK BASED ON COMPOSITION

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Volume 5 (1), June 2022, Pages 94-111

Armin Rabieifard¹, Zahra Tayyebi Qasabeh², Marzieh Faridi Masulehida³ and Maryam Abedini⁴

¹ Non-Profit Higher Education Institutions Sardarjangal Branch, Guilan, Iran. This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.

²Payame Noor university of Guilan, Guilan, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it.

³ Department of Computer, Non-profit higher education institutions Sardarjangal Branch, Guilan, Iran, 
This email address is being protected from spambots. You need JavaScript enabled to view it.

⁴PhD, guest lecturer at Guilan University,Guilan, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it..

Abstract

Today, energy consumption is important in calculating the heating and cooling loads of residential, industrial, and other units. In order to calculate, design, and select the heating-cooling system, a suitable method of consumption and cost analysis is needed to prepare the required data for air conditioning motors and design an intelligent system. In this research, a method for balancing the temperature of an intelligent building in the context of the Internet of Things is presented based on a combination of network cutting and clustering techniques. In order to achieve the optimization of the algorithm in this method, it is necessary to convert heterogeneous data into homogeneous data, which was done by introducing a complex network and appropriate clustering techniques. In this method, information was collected by the IoT, and a graph matrix of these data was generated, then recorded by an artificial intelligence method and a combination of three methods of hierarchical clustering, Gaussian mixture, and K-means for comparison with the preliminary results. Finally, due to the reliability of the K-means method and the use of majority voting for weights, the K-means method reached 0.4 and was selected as the clustering method. The main part of the proposed method is based on different classifications in Appropriate criteria that were evaluated. Acceptable results were recorded so that with the minimum value of 88% and the highest value of about 100, the results of the proposed method can be confirmed. All hypotheses of the method can be declared possible and acceptable.

Keywords:

Smart Building Temperature Equilibrium, IoT, Graph, Grid  Cutting Techniques, K-means Gaussian Mixed Hierarchy Clustering.

DOI: https://doi.org/10.32010/26166127.2022.5.1.94.111

Reference 

Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart cities: Definitions, dimensions, performance, and initiatives. Journal of urban technology, 22(1), 3-21. 

Casado-Vara, R., De la Prieta, F., Prieto, J., & Corchado, J. M. (2019, December). Improving temperature control in smart buildings based in IoT network slicing technique. In 2019 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.

Casado-Vara, R., Martin-del Rey, A., Affes, S., Prieto, J., & Corchado, J. M. (2020). IoT network slicing on virtual layers of homogeneous data for improved algorithm operation in smart buildings. Future generation computer systems, 102, 965-977.

Casado-Vara, R., Vale, Z., Prieto, J., & Corchado, J. M. (2018). Fault-tolerant temperature control algorithm for IoT networks in smart buildings. Energies, 11(12), 3430.

Floris, A., Porcu, S., Girau, R., & Atzori, L. (2021). An iot-based smart building solution for indoor environment management and occupants prediction. Energies, 14(10), 2959.

Gohar, M., Ahmed, S. H., et al. (2018). A big data analytics architecture for the internet of small things. IEEE Communications Magazine, 56(2), 128-133.

Lakshmi, K., Visalakshi, N. K., Shanthi, S., & Parvathavarthini, S. (2017). Clustering Categorical Data Using k-Modes based on Cuckoo Search Optimization Algorithm. Ictact journal on Soft Computing, 8(1).

Lymperopoulos, G., & Ioannou, P. (2020). Building temperature regulation in a multi-zone HVAC system using distributed adaptive control. Energy and Buildings, 215, 109825..

Nowicka, K. (2014). Smart city logistics on cloud computing model. Procedia-Social and Behavioral Sciences, 151, 266-281.

Paul, D., Chakraborty, T., Datta, S. K., & Paul, D. (2018, August). IoT and machine learning based prediction of smart building indoor temperature. In 2018 4th International Conference on Computer and Information Sciences (ICCOINS) (pp. 1-6). IEEE.

Sharma, V., & Tiwari, R. (2016). A review paper on “IOT” & It’s Smart Applications. International Journal of Science, Engineering and Technology Research (IJSETR), 5(2), 472-476.

Talei, H., Benhaddou, D., Gamarra, C., Benbrahim, H., & Essaaidi, M. (2021). Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning. Energies, 14(19), 6042.

Tatbul, N., Lee, T. J., Zdonik, S., Alam, M., & Gottschlich, J. (2018). Precision and recall for time series. Advances in neural information processing systems, 31.

Wickramasinghe, A., Muthukumarana, S., Loewen, D., & Schaubroeck, M. (2022). Temperature clusters in commercial buildings using k-means and time series clustering. Energy Informatics, 5(1), 1-14.