Smart HVAC Control in IoT: Energy Consumption Minimization with User Comfort Constraints

Algorithm 2

Dynamic energy scheduler with comfort constraints relaxation (DES-CCR).

Process: For each time interval, do

 (1) Input:

   (a) The measurements of temperature of the previous interval.

   (b) Value of 𝑝₁,𝑝₂ and 𝑝₃ of the energy cost function 𝐶(𝐿(𝑠_𝑗)) defined in (1).

   (c) The user’s temperature of comfort at each location, that is, 𝑇_𝑢,𝑖,𝑖=1,…,𝑀 in (13).

   (d) The parameter 𝜃∈(0,1) controlling the comfort relaxation in (13).

 (2) Estimation Step

  Estimate 𝑎^𝑗_𝑖 and 𝛾^𝑗_𝑖 in the prediction model (7) using (9)

  to (12) and the temperature measurements from the past time interval.

 (3) Prediction Step

   (a) Substitute the estimation of 𝑎^𝑗_𝑖 and 𝛾^𝑗_𝑖 into the prediction model (7).

   (b) For 𝑖=1 to 𝑀

   For 𝑛=2 to 𝑁

   Compute and store the vector of predicted temperatures

    𝐪^𝑇_𝑖(𝑛) in (14) using (7).

   End For 𝑛

   End For 𝑖

 (4) Optimization Step

   (a) Compute the cost function in (13) using the vectors in

   the step 3(b) and the inputs in (1).

   (b) Solve the optimization problem in (13) using Branch and bound method [23].

 (5) Output

  The configuration of HVACs turned on/off that optimizes (13).