Smart City Gnosys

Smart city article details

Title	Integration Of Intelligent System And Big Data Environment To Find The Energy Utilization In Smart Public Buildings
ID_Doc	32168
Authors	Bhardwaj S.; Sampath B.; Kosimov L.; Kosimova S.
Year	2024
Published	Sustainable Smart Homes and Buildings with Internet of Things
DOI	http://dx.doi.org/10.1002/9781394231522.ch10
Abstract	Buildings are the leading consumer of energy in the setting of smart cities, and public structures such as hospitals, schools, government offices, and additional institutions have high energy needs owing to their frequent use. However, there needs to be adequate use of the latest innovations in machine learning inside the big data context in this field. Controlling the energy efficiency of public subdivisions is a crucial aspect of the smart city concept. This chapter aims to address the challenge of integrating big data platforms and machine learning algorithms into an intelligent system for this purpose to forecast how much energy various Croatian government buildings will consume, prediction models were constructed using deep learning neural networks, Rpart regression tree models, and random forests using variable reduction techniques. The evaluation of all three techniques considered critical aspects, and the random forest methodology yielded the most precise model. The MERIDA intelligent system aims to enhance energy efficiency in public buildings by integrating big data and predictive algorithms. This research examines the technological requirements for a platform that facilitates public administration in planning public building reconstruction, reducing energy consumption and expenses, and connecting intelligent public buildings in smart cities. Digitizing energy management may improve public administration efficiency, service quality, and environmental health. © 2025 Scrivener Publishing LLC. All rights reserved.
Author Keywords	big data platforms; deep learning; energy usage; ML algorithms; Smart cities

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