GÜLÜSTAN DOĞAN, Ted BROWN, Eric DAGOBERT, Marco ASCAZUBI, Joseph MANORGE

Data Analytics of Building Automation Systems: A Case Study

In today’s technology, when costs of time, energy and human resources are considered, efficient use of resources provides significant advantages over many aspects. In light of this, role of building automation systems, which are a part of smart cities, become even more important. At the very core of building automation systems there lies the efficient use of resources and systems for providing comfortable living situations. With the advancement in network technology, systems can be programmed smartly and any malfunctions on the systems can be detected and fixed remotely. In addition to that, all data gathered during this process can be analyzed to create machine- learning solutions for a system to control and program itself. In this document we are presenting a Web application offering features of data analysis and most importantly predictive modeling in the context of building data energy management. As of today, the implementation is made from a CUNY building at John Jay College and contains thousands of data collected from hundreds of sensors over a period of two years, and regularly updated. That is a particular context but the tool can easily be adapted to any type of data environment based on time series. The system articulates around three concepts: visualization, and predicting statistics and forecasting. Visualization is made possible with powerful widgets, and statistics and forecasting based on Python modules. The web client server architecture has several purposes, including, of course, the ones related to any web application, but what is most important it allows transparency between users; every user being able to see each other works. Overall, the originality of this application comes from its high degree of customization: indeed it contains an on-the-fly python interpreter ready to be used with the data, itself encapsulated inside a python object. Therefore, all kind of formulation is allowed to be immediately displayed. The forecasting part is versatile as well, and it sits on python machine learning features, but adapted to manipulate time series.

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