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IEECB&SC Efficiency in Commercial
Buildings and Smart Communities
Synthesis and Refinement of Artificial HVAC Sensor Data
...
IES Building Design Tools
IES Future: Building Operation Tools
SCAN:
EINSTEIN:
Einstein Overview:
- Marie Curie Grant funded FP7 project
- Partnered with Trinity College Dublin
- Four year du...
Prevalence and Effect of Building Faults
“Poorly maintained, degraded, and improperly controlled equipment wastes an estim...
Building Fault Detection & Diagnostics
1) *Fault Detection
identify whether or not a fault is present
2) *Fault Diagnosis
...
Automated Fault Detection & Diagnostics
Desirable to detect and diagnose faults as quickly as possible, automated approach...
Rules, Model & Data Driven Approaches
Different Approaches to AFDD:
- Knowledge Based – - uses expert user experience
- Ru...
Artificial Neural Networks
Multilayer Perceptrons: Artificial Neural Networks
Input Layer Hidden Layers Output Layer
All n...
Data Driven AFDD: Neural Networks
Forward propagating sensor readings through a trained MLP returns fault predictions…
The...
- regions of the feature space:
- non-faulty system operation
- faulty system operation
- fault type 1, type 2, type 3 …
A...
Neural Networks & Feature Space
decision boundary takes the form of a parameterised function represented by a
network:
… n...
Classifier Training
supervised learning technique involves teaching the classifier the predication boundaries using:
- som...
Faulty Operation Data Procurement Issues
Data-driven Approach:
Here are some of the problems with fault operation BEMS dat...
Fault Data Synthesis Overview
Synthesis of Artificial Building Fault Data using IESVE
technique for the quick production o...
Synthesis of Artificial Fault Data (1 of 2)
Fault Simulation Utility
Simulating faulty operation using IESVE software to
p...
Synthesis of Artificial Fault Data (2 of 2)
Anomaly detection using Gaussian Kernel Density Estimation
Example using a sin...
Use of pure Synthetic Data for Classifier
System has only been evaluated using simulated data - not on a real building!
 ...
Future Work
Concerns:
- model is not a precise representation of the
building – region boundaries for model and
building m...
Q&A
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Synthesis and Refinement of Artificial HVAC Sensor Data Intended for Supervised Learning in Data-Driven AFDD Techniques

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IES' David McCabe presented at the 9th International Conference Improving Energy Efficiency in Commercial Buildings and Smart Communities (IEECB&SC’16) in Frankfurt on 16th March 2016.

This presentation was in support of a paper published by IES R&D in conjunction with the EINSTEIN project. The paper can be viewed here: http://www.iesve.com/corporate/media-center/white-papers/general/hvac-afdd-jun2016.pdf

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Synthesis and Refinement of Artificial HVAC Sensor Data Intended for Supervised Learning in Data-Driven AFDD Techniques

  1. 1. IEECB&SC Efficiency in Commercial Buildings and Smart Communities Synthesis and Refinement of Artificial HVAC Sensor Data Intended for Supervised Learning in Data-Driven AFDD Techniques Presenter: David McCabe Wednesday, 14 January 2016
  2. 2. IES Building Design Tools
  3. 3. IES Future: Building Operation Tools SCAN:
  4. 4. EINSTEIN: Einstein Overview: - Marie Curie Grant funded FP7 project - Partnered with Trinity College Dublin - Four year duration - Secondments between academia and industry Objectives: - Forge partnerships between Industry and Academia - Develop a prototype smart building control framework - Use performance prediction and control optimisation - Exploit framework further with an AFDD capability
  5. 5. Prevalence and Effect of Building Faults “Poorly maintained, degraded, and improperly controlled equipment wastes an estimated 15% to 30% of energy used in commercial buildings.” - [Brambley et al 2005] “estimated 25% to 45% of energy consumption in HVAC plant serving commercial buildings is wastage due faults” - [Akinci et al 2011] “Studies have indicated that 20–30 % HVAC system energy savings are achievable by recommissioning air handling units (AHU) to rectify faulty operation.” - [Bruton et al 2013]
  6. 6. Building Fault Detection & Diagnostics 1) *Fault Detection identify whether or not a fault is present 2) *Fault Diagnosis determine the precise source of the problem 3) Impact Evaluation cost, comfort, environmental or safety impact 4) Decision/Action tolerate or shutdown for repair etc. 3) Impact Evaluation 4) Decision/ Action System e.g. building HVAC Fault Detection & Diagnostics (FDD) 1) Fault Detection 2) Fault Diagnostics BEMS Data *Fault Detection & Diagnostics
  7. 7. Automated Fault Detection & Diagnostics Desirable to detect and diagnose faults as quickly as possible, automated approach is favourable over a traditional manual approach.  Diligence and round the clock monitoring  Immediately interprets high volumes  Risks false alarms without proper guidance  Takes time to develop  Require high volumes of real-time data - commonly used within mass manufactured units such as consumer electronics and vehicles, through the utilisation of their on-board electrical architecture. - noticeably absent from commercial buildings perhaps due to both their lack of design homogeneity and in many cases the low availability of sensor data. Further more AFDD in sectors such as aerospace and automotive sectors also fulfils a vitally critical safety function.
  8. 8. Rules, Model & Data Driven Approaches Different Approaches to AFDD: - Knowledge Based – - uses expert user experience - Rule Based - Model based Diagnostics - - uses a calibrated building model - Empirical Models, Machine Learning - Data Driven Diagnostics - uses historical building data - Statistical Methods, Empirical Data, Machine Learning
  9. 9. Artificial Neural Networks Multilayer Perceptrons: Artificial Neural Networks Input Layer Hidden Layers Output Layer All node take values within the interval: (0,1)
  10. 10. Data Driven AFDD: Neural Networks Forward propagating sensor readings through a trained MLP returns fault predictions… The ANN’s parameter values are trained using labelled fault data (Supervised Learning). Return Temp Flow Rate Elec. Fan [SF005] breakdown pred. Valve [#142] breakdown pred. ALERT: Check Valve - No. 142
  11. 11. - regions of the feature space: - non-faulty system operation - faulty system operation - fault type 1, type 2, type 3 … AFDD: Classification Problem Sensors variables are known as features e.g. Sensor variables form a feature space: - the complete set of sensor time series data represents a feature space trajectory Fault Detection: (Binary classification problem) Fault Classification & Diagnosis: (Multi-Variable classification problem) An Artificial Neural Network is a type of classifier. It maps or categorises the feature space into different regions.
  12. 12. Neural Networks & Feature Space decision boundary takes the form of a parameterised function represented by a network: … network topology determines the complexity of the prediction boundary Parameterised weights in the network determine the exact positions of the decision boundaries.
  13. 13. Classifier Training supervised learning technique involves teaching the classifier the predication boundaries using: - some labelled training data - a learning algorithm Fault Detection: Fault Diagnostics:
  14. 14. Faulty Operation Data Procurement Issues Data-driven Approach: Here are some of the problems with fault operation BEMS data: - insufficient data not enough data - unbalanced data (Skewed Classes) one class is over-represented in the data - incorrect labelling e.g. periodic problem is labelled continuously - unknown faults faults other than the known types may be present Effect: Extreme mismatch between decision boundaries and fault regions
  15. 15. Fault Data Synthesis Overview Synthesis of Artificial Building Fault Data using IESVE technique for the quick production of high volumes well labelled training data - Simulation ensures saturation -Anomaly Detection ensures observability -Balancing ensures uniform distribution Harvesting (Threshing) of Data from Fault Simulations Fault Type II Simulation Fault Type IV Simulation Training, Cross Validation & Testing Data Data Refinement Data Balancing Not Required Synthetic Fault Data Labelling (Anomaly Detection) No Anomaly Detected Anomaly Detected Required Data Balancing Synthetic Fault Data Labelling (Anomaly Detection) Anomaly Detected Required Raw Synthetic Fault Data Acquisition No Anomaly Detected Raw Synthetic Fault Data Acquisition Not Required
  16. 16. Synthesis of Artificial Fault Data (1 of 2) Fault Simulation Utility Simulating faulty operation using IESVE software to produce unlabelled results. Programmatic perturbations to operational and design profiles allow us to programmatically : - introduce faults - diversify results Higher coverage of the buildings operational Envelope in feature space Data labelling, volume, and balancing now require refinement…
  17. 17. Synthesis of Artificial Fault Data (2 of 2) Anomaly detection using Gaussian Kernel Density Estimation Example using a single feature (sensor) example…
  18. 18. Use of pure Synthetic Data for Classifier System has only been evaluated using simulated data - not on a real building!  99.8% of faults successfully detected  100% of detected faults successfully diagnosed  No fault alarms  AFDD only performed on simulation data
  19. 19. Future Work Concerns: - model is not a precise representation of the building – region boundaries for model and building may be quite different - accurate modelling can be as time consuming as designing an expert rule set and calibration requires actual data – commissioning needs to be done first! - Kernel Density Estimation (pictured) does not scale well
  20. 20. Q&A

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