First Phase of "Building Data Analytics", by Intellicasa & UCL
Updated: Nov 4, 2020
Smart Buildings make use of IT technology nowadays that allow them to become more efficient and transparent in terms of operation. With the advent of the Internet of Things (IoT) and the convergence between IT and OT, buildings, in order to become smarter, must utilise effectively and efficiently the amount of data that building control systems are able to provide. Data analytics for buildings can improve the comfort levels of occupants and reduce operating costs by the implementation of predictive control and machine learning algorithms and thus contributing to a more sustainable built environment.
IntelliCasa has been the inspirer of the “Building Data Analytics” project that ignited as a result of the MOU signed with UCL University last year among other activities. The first phase of the “Building Data Analytics” project has been completed successfully with interesting findings that will lead to further research at the next stage.
The project commenced as a collaboration between UCL (UK), IntelliCasa (UK) and General Technology Ltd (Greece). IntelliCasa directors Mr Elie Kfoury and Mr Konstantinos Karagiannis acted as Industry advisors for the UCL research team, led by Dr Ryan Grammenos and MSc students Hyunjee Kim and Changyou Gong of the Electronic and Electrical Engineering department. General Technology Ltd, a company specialised in building energy management systems provided the dataset of recorded building variables from a selected office building for the research.
The project aims to examine the interrelation between indoor and outdoor building variables measured from a building management system and derive conclusions for comfort and energy optimisation through predictive control. The first stage was focused on Data Analytics, while at the second stage a machine learning algorithm will be formulated for predictive control.