Feasibility of random neural networks as an intelligent self-learning platform for cost effective deployment of energy harvesting compatible wireless sensors applied to building management systems

This project addresses the need for energy harvesting compatible combined carbon dioxide / temperature /humidity wireless smart sensors, enabling people occupancy & indoor air quality (IAQ) monitoring in buildings and deployed within an intelligent self learning (ISL) network. Project output provides automated adaptive management of air conditioning systems to achieve required IAQ and minimise energy usage. The project focusses on establishing feasibility of using intelligent self learning networks based on a random neural network (RNN) approach, providing a control platform for simultaneous measurement of multiple wireless CO2/temperature/humidity sensor inputs The project combines unique patented energy harvesting compatible CO2/temperature/humidity wireless sensors, developed by project lead Gas Sensing Solutions, combined with innovative patented ISL network capability from Glasgow Caledonian University. The RNN wireless network methodology provides potential for low cost “fit and forget” deployment of CO2/humidity/ temperature wireless sensor ISL networks into existing and new buildings. The consortium includes non-funded end user assessment.