Secure Cloud Computing based Energy Analytics Framework in Construction of Residential Buildings

Junjie Li; Ernawati Binti Mustafa Kamal; Khoo Terh Jing

Author(s)

Junjie Li School of Housing, Building & Planning, University Sains,,Malaysia

Ernawati Binti Mustafa Kamal School of Housing, Building & Planning, University Sains Malaysia

Khoo Terh Jing School of Housing, Building & Planning, University Sains Malaysia

Abstract

The buildings are emanating a massive producer of data amidst being massive consumers of energy resources. Electrification of a region is seen as a breakthrough in fostering the economic development of the region. However, rapid urbanization has paved the way for the construction of huge buildings which is home to a large amount of population, which directly or indirectly contributes to energy consumption. Energy analytics is a form of energy conservation, especially in residential buildings, which is generally harnessed through cutting-edge computing technologies. This work proposed a comprehensive framework with five layers that collects data from the energy monitoring edge devices to build energy analytics by processing the data in the cloud platform. In addition to this, the framework uses a security score to monitor the illegitimate access of the cloud source by tracking the registered devices. This is a robust and generic framework that has the scope to include AI-based strategies that can be orchestrated in the cloud computing platform.

Publication Details

Page(s) 117-129

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 1, Year: 2024

Request the full text from PASTIC

Keywords

urbanization, Cloud computing, Residential Buildings, Security Score, Energy Analytics, Household Appliances

References

Doll C. N.,J. A. P. C. N. .2017 .Climate Co-benefits: Implementation of Win-win Interventions in Cities. , : .

Dakheel J. Al,Del Pero C.,Aste N.,Leonforte F. .2020 .Smart buildings features and key performance indicators: A review,”. Sustainable Cities and Society, 61 : 102328.

Jia M.,Komeily A.,Wang Y.,Srinivasan R.S. .2019 .Adopting Internet of Things for the development of smart buildings: A review of enabling technologies and applications. , 101 : 111-126.

Farzaneh H. .2019 .Devising A Clean Energy Strategy For Asian Cities. , : .

Petrović N. .2020 .Data-driven framework for energy-efficient smart cities. Serbian Journal of Electrical Engineering, 17(1) : 41-63.

Chui K. T.,Lytras M. D.,Visvizi A. .2018 .Energy sustainability in smart cities: Artificial intelligence, smart monitoring, and optimization of energy consumption. ” Energies, 11(11) : 2869.

Mehmood M. U.,Chun D.,G D.,Chen K. .2019 .A review of the applications of artificial intelligence and big data to buildings for energy-efficiency and a comfortable indoor living environment,” Energy and Buildings. , 202 : 109383.

Rydning D. R .J .G. J.,Reinsel J.,Gantz J. .2018 .The digitization of the world from edge to core,” Framingham: International Data Corporation. , 16 : 1-28.

Noura M.,Atiquzzaman M.,Gaedke M. .2019 .Interoperability in internet of things: Taxonomies and open challenges,” Mobile networks and applications. , 24 : 796-809.

D. D'Agostino L.,Mazzarella L. .2019 .What is a Nearly zero energy building? Overview, implementation. ” Journal of Building Engineering, 21 : 200-212.

Energy International,Agency International .2017 .. Digitalization & Energy, : .

D. van Dijk ,Hogeling J. .2008 .The European directive on energy performance of buildings (EPBD)-the EPBD buildings platform. ” ASHRAE Transactions, 114 : 338.

Dahash A.,Ochs F.,Janetti M. B. .2019 .Advances in seasonal thermal energy storage for solar district heating applications: A critical review on large-scale hot-water tank and pit thermal energy storage systems. ” Applied Energy, 239 : 296-315.

Marinakis V.,Doukas H. .2018 .An advanced IoT-based system for intelligent energy management in buildings. , 18(2) : 610.

Hussain M. E.,Qayyum M.,Hussain M. R.,Hussain R. .2019 .Effective and secure vWSN applications in a virtualized cloud computing environment. International Journal of Communication Networks and Information Security, 11(2) : 256-261.

Wen L.,Zhou K.,Yang S.,Li L. .2018 .Compression of smart meter big data: A survey,” Renewable and Sustainable Energy Reviews. , 91 : 59-69.

Ionescu C.,Baracu T.,Vlad G. E.,Necula H.,Badea A. .2015 .The historical evolution of the energy efficient buildings,” Renewable and Sustainable Energy Reviews. , 49 : 243-253.

Araújo J.D.,Abdelouahab Z. .2015 .EICIDS-elastic and internal cloudbased detection system. International Journal of Communication Networks and Information Security, 7(1) : 34.

Li C.,Shen D.,Wang L. .2019 .A low power data transfer and fusion algorithm for building energy consumption monitoring. International Journal of Low-Carbon Technologies, 14(3) : 426-431.

Himeur Y.,Alsalemi A.,Bensaali F.,Amira A. .2020 .Improving in-home appliance identification using fuzzyneighbors-preserving analysis based QR-decomposition,” in International congress on information and communication technology. , : 303-311.

Raju D. N.,Saritha V. .2016 .Architecture for fault tolerance in mobile cloud computing using disease resistance approach. International Journal of Communication Networks and Information Security, 8(2) : 112.

Nesa N.,I. Banerjee N. .2017 .IoT-based sensor data fusion for occupancy sensing using Dempster - Shafer evidence theory for smart buildings. ” IEEE Internet of Things Journal, 4(5) : 1563-1570.

Fikru M. G.,Gautier L. .2015 .The impact of weather variation on energy consumption in residential houses. ” Applied Energy, 144 : 19-30.

Wang W.,Hong T.,Xu N.,Xu X.,Chen J.,Shan X. .2019 .Cross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering,” Building and Environment. , 162 : 106280.

Heebøll A.,Wargocki P.,Toftum J. .2018 .Window and door opening behavior, carbon dioxide concentration, temperature, and energy use during the heating season in classrooms with different ventilation retrofits - ASHRAE RP1624,” Science and Technology for the Built Environment. , 24(6) : 626-637.

Cabeza L. F. .2022 .Mitigation of Climate Change. Sixth Assessment Report of the Intergovernmental Panel on Climate Change, : 953-1048.

Sikeridis D.,Papapanagiotou I.,Rimal B. P.,Devetsikiotis M. .2017 .A Comparative taxonomy and survey of public cloud infrastructure vendors. , : 01476.

Himeur Y.,Alsalemi A.,Bensaali F.,Amira A. .2021 .The emergence of hybrid edge-cloud computing for energy efficiency in buildings. in Proceedings of SAI Intelligent Systems Conference, Aug, : 70-83.

Pan J.,Jain R. .2014 .A survey of energy efficiency in buildings and microgrids using networking technologies. ” IEEE Communications Surveys & Tutorials, 16(3) : 1709-1731.

Martín-Garín A.,Millán-García J. A.,Baïri A.,Gabilondo M.,Rodríguez A.,Creation A. .2020 .IoT and cloud computing for building energy efficiency. -12-819946- 6, : 00010-2.

Lee S. H.,Hong T.,Piette M. A.,Chen Y.,Taylor-Lange S. C. .2015 .“Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance,” Energy. , 90 : 738-747.

C. K. Metallidou K. E.,Psannis E. A.,Egyptiadou E. A. .2020 .Energy efficiency in smart buildings: IoT approaches. , 8 : 63679-63699.

Previtali M. A. T. T. I. A.,L. U. I. G. I. Barazzetti M. A. T. T. I. A.,Brumana R. A. F. F. A. E. L. L. A.,Cuca B. R. A. N. K. A.,Oreni D. A. N. I. E. L. A.,Roncoroni F. A. B. I. O.,Scaioni M. A. R. C. O. .2014 .Automatic façade modelling using point cloud data for energy-efficient retrofitting. ” Applied Geomatics, 6 : 95-113.

Wei C.,Li Y. .2011 .Design of energy consumption monitoring and energy-saving management system of intelligent building based on the Internet of things. Communications and Control (ICECC), : 3650-3652.

Mohamed N.,Lazarova-Molnar S.,Al-Jaroodi J .2016 .CE-BEMS: A cloud-enabled building energy management system. ” in 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), : 1-6.

Aslam M. S. .2021 .Energy-efficiency model for residential buildings using supervised machine learning algorithm. ” Intelligent Automation and Soft Computing, 30(3) : 881-888.

Beloglazov A.,Abawajy J.,Buyya R. .2012 .Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing,” Future Generation Computer Systems. , 28(5) : 755-768.

Yang S. Y. .2013 .A novel cloud information agent system with web service techniques: Example of an energysaving multi-agent system. ” Expert Systems with Applications, 40(5) : 1758-1785.

Maréchal K.,Holzemer L. .2018 .Unravelling the 'ingredients' of energy consumption: Exploring home-related practices in Belgium,”. Energy Research & Social Science, 39 : 19-28.

Sharanya S.,Venkataraman R. .2024 .Predicting remaining useful life of turbofan engines using degradation signal based echo state network. International Journal of Turbo & Jet-Engines, 40(s1) : s181-s194.