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This project is based on the following paper:

Lee, Donghwan ; Lee, Seungjae ; Karava, Panagiota ; Hu, Jianghai “Simulation-Based Policy Gradient and Its Building Control Application” 2018 Annual American Control Conference (ACC), June 2018, pp.5424-5429

It applies stochastic simulation-based policy gradient method for optimal office building HVAC control system:

  • Approximates the gradient of the cost function using simulations
  • Uses a gradient descent type algorithm to design a suboptimal control policy
  • Assesses its performance through a simulation of building HVAC system
  • Compares this method to the finite-horizon LQR state-feedback control policy

MATLAB Files:

  1. inputData.m: Loads pdf for user actions from pdf_data.mat file Loads weather data for dayn from weather2018.mat file Initializes all parameters for dynamic state-space model Computes state-feedback for LQR

  2. outputData.m - generates plots and histogram

  3. simulation.m - 24 hour simulation for a given weights and day

  4. bASOP.m - main script: Picks random day within [1 364] range as dayn Calls inputData.m for dayn Sets parameters for ß-ASOCP algorithm and runs it N_of_iteration times for initila theta=0 Simulates (dayn+1) day using new control policy with computed theta and calculates cost Simulates (dayn+1) day using state-feedback for LQR and calculates cost Compares both methods in histogram for 1000 samples WARNING: number of iterations set to 10000, which takes ~15 min. Can be lowered for test purposes.

  5. weather2018.mat - real weather data from UW weather station for 2018 with step=15 minutes

  6. pdf_data.mat - probability density function of occupant actions based on indoor temperature

FOR MORE DETAILS SEE ECE686ProjNTuktibayev.pptx