1. UHI
RESEARCH AND POST GRADUATE
CONFERENCE
John Egbuta
(PhD Candidate : Systems Engineering-University of Aberdeen)
Artificial Intelligence Applications for Building Energy
Management
Applications

2. The Research Question
• How can Artificial Intelligence be used to
Automate the Building Energy Module by
Integrating the Building Energy Management
System and Micro-generation systems on the
same platform in order to achieve the Zero
Energy Building status?

3. The Current Paradigm and the
Proposed Shift
• Michael Mozer’s Cost function for the Neurothermostat
• My Proposed Cost Function for the Controller in this PhD
Endeavour
– Where relevant variables are quantized in Great British Pounds and
defined as follows
• Ec (Ut)= Energy cost as the result of the control decision (Ut)
• M(Xt) = Misery as a result of the environmental variable (Xt) i.e. Indoor Temperature
• Ed(Ut) = Energy debit from Micro-generation Systems
– Note: At the moment, no known controller combines these three
variables in a cost function!

4. CONTROLLER
Current Modelling Effort
• MATLAB Model of the Model Predictive Controller
• Relevant Parameters
– Yr = Desired Response
– Ym = Neural Network Model Response
» Which is compared to the desired response to generate an error that is minimized over the Control
and Cost Horizons
– Yp is the output of the plant
– U is the control input
OPTIMIZATION
BLOCK
OPTIMIZATION
BLOCK
ENERGY PLANTENERGY PLANT
BEMS NEURAL
NETWORK
MODEL
Yr
Ym
Ypu
u’

5. Modelling: Optimization Block
• Relevant Parameters
– N1 and N2 are the Cost Horizons
– Nu is the Control Horizon
• The 3 horizons above refer to the number of discrete steps over which the error from
prediction and control are minimized.
– Yr = Desired Response
– Ym = Neural Network Model Response
– A backtracking line search routine is used to step through the input (u) during
the error minimization process.

6. Modelling : BEMS Neural Network
• Network Architecture
– Feed-forward Network (Multiple Layer Perceptron) with tansig and purlin transfer functions
• Inputs(3)
• Hidden Layer (20 Neurons)
• Outputs(3)
– All inputs and outputs are TDL (Tapped Delayed Lines (TDL) are used to enable correct prediction based on past
inputs to the network )

7. Modelling :Energy Plant
• Emulated Linear Model of Proportional Control Action of a Heat Plant
– Linear Representation of the Plant is
• V = KE + M
– Where:
» V is the final Temperature output by the plant
» K is the Gain, which is equal to the 100/Proportional Band
» E is the error which is the difference between the Control Point and the Set Point
» M is the bias, which is the output of the plant when the final control element is at 50% of its range

8. Final Model of Model Predictive
Controller

9. Modelling Results: Plant Identification
• There is a very close scaled relationship between the
input and output data that was generated from the
Model.
• This is means the data can be used to train the Neural
Network

10. Modelling Results: Neural Network
Training and Validation of Generated
Data
• Performance Evaluation
– Mean Square Error: 0.0023425
– Plant to Neural Network Tracking: Good
Training data Validation data

11. Controller Simulation Results
• Green Signal is the Reference Signal (which has no variable states at the moment)
• The Blue signal is the Plant Output Optimized to track the reference signal
– At the moment, more work is being done to optimize this signal
– Also, if a derivative or Integral block were included in the design of the plant model, the plant
signal will track the reference signal better

12. Conclusion
• In the end, this research endeavour will investigate how the
Building Module could become
– An Intelligent Entity (i.e. more ADAPTIVE and less reactive)
– An Energy System (i.e. an energy asset)

13. Thank You
• Appreciation
– Greenspace Research LCBL
– Hydrogen Labs at LCC
– Dr. Alasdair Macleod
– University of Aberdeen Supervisory Team