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Machine Learning.

Non-residential building occupancy modeling. Part I.


Idea: providing occupancy driven energy efficiency model. Since about 40% of building energy goes to HVAC(heating, ventilation and air conditioning) it is quite good idea to use these equipment when it is really required,e.g. switch on the air conditioner or ventilation when people in a room
The study discusses approaches towards optimizing energy consumption of commercial buildings. Such task is considered to be a part of more broad topics, e.g. smart buildings, green buildings. During the past years, the number of papers dedicated to energy efficiency optimization in buildings has been growing which confirms societal concern about finding the most efficient methods of improving energy usage by buildings. To maximize building’s energy efficiency various methods are known and can be split into re-organizational advances and strengthening currently employed management systems [1], [2]. This methods include triple glazing, different types of insulation techniques such as wall and windows insulation as well as heating and lighting control.Despite a range of approaches towards solving this problem, such as building envelope design, window parameters, climate conditions, the proposal will focus on HVAC(Heating Ventilation Air Conditioning) systems as well as appliances and lighting. In this section, the paper will argue about some associated research papers and represent outlook of this overview. Additionally, it will underline main methods and techniques in the topic of the study as well as consider primary challenges.

Speaking in a simple terms, if BMS (building management service) engineer or building owner will want to know occupancy predictions for a particular room/buidling sector at particular time. Having such occupancy model would be helful for many applications, including energy efficiency (manage lights, heating, ventilation). Also, speaking about shopping and entertainment malls, one might want to predict how many visitors boutique or shop may expect at a time or panning some advertising campaign to catch the time when number of people is maximum.
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Things that required to build up a occupancy prediction model
As in most machine learning problems the first step is to acquire appropriate dataset. After some research, I encounterd several public datasets, the most interesting ones are UCI, OpenEI and GitHub buildings datasets.

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