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Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]
Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]
Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]
Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]
Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]
Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]
Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]
Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]
Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]
Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]
Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]
Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]
Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]
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Artificial Intelligence Applications For Building Energy Management Applications [John Egbuta]

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  • 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

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