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

Algorithm 1

Dynamic energy scheduler with comfort constraints (DES-CC).

Process: For each time interval, do

 (1) Input:

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

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

   (c) The user’s temperature of comfort constraints at each

   location, that is, 𝑇_𝑖^min,𝑇_𝑖^max,𝑖=1,…,𝑀 in (4).

 (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) Iterate this model to populate the vectors of minimum

   and maximum predicted temperatures (6).

 (4) Optimization Step

   (a) Substitute the predicted temperatures (5) and the

   quadratic cost function (1) into the optimization problem (4)

   (b) Solve (4) using Branch and bound method [23].

 (5) Output

  The configuration of HVACs turned on/off that optimizes (4).