Data-driven Modelling of Smart Building Ventilation Subsystem

Considering the advances in building monitoring and control through networks of interconnected devices, effective handling of the associated rich data streams is becoming an important challenge. In many situations the application of conventional system identification or approximate grey-box models, partly theoretic and partly data-driven, is either unfeasible or unsuitable. The paper discusses and illustrates an application of black-box modelling achieved using data mining techniques with the purpose of smart building ventilation subsystem control. We present the implementation and evaluation of a data mining methodology on collected data over one year of operation. The case study is carried out on four air handling units of a modern campus building for preliminary decision support for facility managers. The data processing and learning framework is based on two steps: raw data streams are compressed using the Symbolic Aggregate Approximation method, followed by the resulting segments being input into a Support Vector Machine algorithm. The results are useful for deriving the behaviour of each equipment in various modi of operation and can be built upon for fault detection or energy efficiency applications. Challenges related to online operation within a commercial Building Management System are also discussed as the approach shows promise for deployment.

[1]  Christer Åhlund,et al.  Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency , 2018, Renewable and Sustainable Energy Reviews.

[2]  Guoqiang Hu,et al.  Spatio-temporal environmental monitoring for smart buildings , 2017, 2017 13th IEEE International Conference on Control & Automation (ICCA).

[3]  Stéphane Ploix,et al.  Estimating Occupancy In Heterogeneous Sensor Environment , 2016 .

[4]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[5]  Houda Najeh,et al.  Diagnosis of sensor grids in a building context: Application to an office setting , 2018 .

[6]  Ulrich H.-G. Kreßel,et al.  Pairwise classification and support vector machines , 1999 .

[7]  Eamonn J. Keogh,et al.  Locally adaptive dimensionality reduction for indexing large time series databases , 2001, SIGMOD '01.

[8]  Grigore Stamatescu,et al.  Data-driven methods for smart building AHU subsystem modelling , 2017, 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).

[9]  María del Mar Castilla,et al.  An Economic Model-Based Predictive Control to Manage the Users’ Thermal Comfort in a Building , 2017 .

[10]  Nora El-Gohary,et al.  A review of data-driven building energy consumption prediction studies , 2018 .

[11]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[12]  Sang-Bong Rhee,et al.  IoT-Based Smart Building Environment Service for Occupants' Thermal Comfort , 2018, J. Sensors.

[13]  Zhuo Wang,et al.  From model-based control to data-driven control: Survey, classification and perspective , 2013, Inf. Sci..

[14]  Roger N. Anderson,et al.  Forecasting Energy Demand in Large Commercial Buildings Using Support Vector Machine Regression , 2011 .

[15]  Carlos León,et al.  Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach , 2016, Expert Syst. Appl..

[16]  Benjamin C. M. Fung,et al.  Advances and challenges in building engineering and data mining applications for energy-efficient communities , 2016 .

[17]  Antonio F. Gómez-Skarmeta,et al.  Towards Energy Efficiency Smart Buildings Models Based on Intelligent Data Analytics , 2016, ANT/SEIT.

[18]  Grigore Stamatescu,et al.  Open and closed loop simulation for predictive control of buildings , 2016, 2016 24th Mediterranean Conference on Control and Automation (MED).

[19]  Carlos Agón,et al.  Time-series data mining , 2012, CSUR.

[21]  Srinivas Katipamula,et al.  VOLTTRON: An Open-Source Software Platform of the Future , 2016, IEEE Electrification Magazine.

[22]  Iulia Stamatescu,et al.  Decision Support System for a Low Voltage Renewable Energy System , 2017 .

[23]  Francisco Martínez-Álvarez,et al.  Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities , 2018 .

[24]  Clayton Miller,et al.  The Building Data Genome Project: An open, public data set from non-residential building electrical meters , 2017 .

[25]  Eamonn J. Keogh,et al.  HOT SAX: efficiently finding the most unusual time series subsequence , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[26]  Christian Weber,et al.  A Graph-Based Sensor Fault Detection and Diagnosis for Demand-Controlled Ventilation Systems Extracted from a Semantic Ontology , 2018, 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES).

[27]  Kishik Park,et al.  Lambda-Based Data Processing Architecture for Two-Level Load Forecasting in Residential Buildings , 2018 .

[28]  Gernot Kubin,et al.  Dependable Internet of Things for Networked Cars , 2017 .

[29]  Miguel Molina-Solana,et al.  Data science for building energy management: A review , 2017 .

[30]  Rahul Mangharam,et al.  Data-Driven Model Predictive Control with Regression Trees—An Application to Building Energy Management , 2018, ACM Trans. Cyber Phys. Syst..