Smart Building Energy Management using Neural Network Algorithm
Abstract:
This research presents a prototype of an innovative approach to smart building management using artificial intelligence and wireless sensors. There has been a lot of research to determine the most optimal operation of heating and cooling systems. However, the traditional controlling algorithms are complicated and difficult to adjust if the usage pattern changes or the configuration of heating and cooling systems changes. The approach proposed in this research uses an adaptive neural network algorithm to automatically and continuously adjust / optimize the heating and cooling systems for the most efficient energy usage and the most optimal comfort to the users. Wireless and wired sensors are integrated to monitor temperature, humidity, indoor CO2, motion, and air flow. In addition to the sensory data input, time, date, and outdoor weather condition are also fed to the neural network. User input data are used for supervised learning of the neural network and users’ need-based interception continuously updates the neural network. This research presents laboratory and simulation experiments to prove the concept.
