Sustainable Smart City Building Construction Methods

Abstract

In a global world, the human population invariably increases while resources gradually decrease as cities and towns constantly consume resources to satisfy their needs and requirements. At this point, it is very necessary to focus on making these urban areas more sustainable and greener. The need for some advanced and automated systems improves the situation, which leads to the innovation of smart cities. Smart city is the concept that helps in developing sustainable cities via optimized resource utilization methods. In smart city development, various sensing technologies can be used that can sense and utilize natural resources in better ways, like storing rainwater to use afterward, intelligent and smart control system, smart infrastructure monitoring system, smart healthcare system, smart transportation system, and smart system for energy consumption and generation by various facilities. To make the city smart and sustainable with efficient energy consumption, we propose renewable solar and wind energy-enabled hybrid heating and cooling HVAC-DHW (heating, ventilation, and air conditioning-Domestic Hot Water) system in which energy consumption is evaluated using optimized NARX-ANN and fuzzy controller based on user needs, dynamic behavior of the atmospheric environment, and spatial distribution of energy supply. To achieve the proposed goal, first, via sensor, heating and cooling effect of environment and building is sensed and these sensed inputs are then fed into deep-learning-based NARX-ANN that forecast internal building temperature. This forecasted temperature is fed into a fuzzy controller for optimizing output based on user demand. This processed information leads to energy distribution based on their requirement using a smart energy sensing system. Based on the experimentation result and performance analysis, it was found that the proposed system is more robust and has a high control response in comparison to the existing systems with minimum energy consumption. The analytical results support the feasibility of the proposed framework architecture to facilitate energy conserving in smart city buildings.