IOT-based Smart Energy Management in Buildings of Smart Cities

Buildings consume nearly one-third of global energy and are responsible for one-fourth of CO2 emissions, thereby playing a crucial role in polluting the earth. Cities are more vulnerable as there are more buildings and a huge population due to employment opportunities. Hence, there is a need for the transformation of cities into smart cities with viable environments by making buildings smart. Smart cities with energy-efficient buildings can improve the economy and reduce pollution effects, thereby improving the quality of city life. As human errors and carelessness jeopardise energy conservation and eco-friendly initiatives in traditional buildings, automatic internet of things (IOT) monitored building control, also known as a smart building, is a need of the hour if the world is to advance toward smart cities. The management of the cities should estimate their energy consumption in advance and plan strategies that will help in reducing the energy consumption of both commercial and residential buildings towards creating a pollution-free smart city. The IOT sensors produce continuous streaming data, which necessitates big data analysis to improve the performance of building in terms of energy consumption. Big data analysis based on machine learning techniques is currently being employed for such an automatic analysis and management of buildings based on IOT sensor data. This chapter focuses on bringing out the commercially available IOT sensors for collecting building data, their efficiencies, extracted features and the commonly used machine learning techniques, their strengths, and drawbacks and also identifies the research gap and work to be done for further improving big data analysis of smart energy management.

Keywords: Big data, Gateway, HVAC, IOT, Machine Learning, Neural network, Raspberry pi, Sensors, Smart city, Smart building.