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






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

    • UHI RESEARCH AND POST GRADUATE CONFERENCE John Egbuta (PhD Candidate : Systems Engineering-University of Aberdeen) Artificial Intelligence Applications for Building Energy Management Applications
    • 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?
    • 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!
    • 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
    • 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.
    • 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 )
    • 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
    • Final Model of Model Predictive Controller
    • 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
    • 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
    • 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
    • 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)
    • Thank You
      • Appreciation
        • Greenspace Research LCBL
        • Hydrogen Labs at LCC
        • Dr. Alasdair Macleod
        • University of Aberdeen Supervisory Team