Adaptive systems for smart buildings utilizing wireless sensor networks and artificial intelligence

Abstract

In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – transforming them into smart adaptable devices, i.e., Smart Thermostats. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Learning System (ALS) model utilizing WSN, a rule-based system and Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant's pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the RL and Agent-based Search, versus the RL only technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a House Simulator is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the House Simulator, the Smart Thermostat, and a placeholder for the Adaptive Learning Models. As a result, a novel adaptive learning algorithm, Observe, Learn and Adapt (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the House Simulator. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.