Research Article
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 and of the energy cost function defined in (1). | (c) The user’s temperature of comfort at each location, that is, in (13). | (d) The parameter controlling the comfort relaxation in (13). | (2) Estimation Step | Estimate and 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 and into the prediction model (7). | (b) For to | For to | Compute and store the vector of predicted temperatures | in (14) using (7). | End For | End For | (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). |
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