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id : 783
type : MSc_Thesis
dateandtime : 2017-09-12 14:00:00
duration : 90 min.
Recommended duration for PhD thesis is 90 minutes, for other seminar types, it is 60 minutes. The duration specified here is used to reserve the room.
place : A101
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departmental : yes
supervisors : DR. CEVAT SENER
Supervisors field is applicable especially for a Thesis Defense
company : Computer Engineering Dept. Middle East Technical Univ.
country : Turkey
abstract : Efficient energy consumption is a trending topic nowadays, which has serious
effects both environmentally and financially. Commercial and industrial buildings
waste huge amounts of energy because of lack of integrated optimization systems. In
this thesis, a big data analytics architecture for large-scale multi-tenant energy
optimization systems is proposed, which is capable of doing various near-real time
analyses on sensor data with the help of machine learning models created from old
sensor data.
In order to build a big data analytics handling subsystem there are several
steps during the flow of the sensor data. Raw data collected from the sensors in the
field to the system is parsed and turned into meaningful data containing required
features. This meaningful data is used for predicting the forth-coming energy
consumption values. Prediction feature of the system is carried out with a machine
learning model created from old sensor data. This meaningful data is also used for
updating this machine learning model, to improve the accuracy and provide
compatibility of model with live sensor data. Prediction and model update analyses
are implemented on the streaming sensor data, without first storing it to a database or
file system to provide near-real time feature of system. A very important feature of
the system is scalability, which means adding new tenants or increasing the
frequency of sensor data arrival is handled by system.

Keywords: Big data analytics, Scalability, Internet of Things, Apache Spark
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COW by: Ahmet Sacan