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![]() According to the receding horizon principle, only the first value of the optimal sequence will be applied to the HVAC system. This way, we are determining the control sequence that minimizes, at each sample instant, the economic cost over a PH, ensuring that the thermal comfort over this PH is ensured. ![]() J is an estimate of the economic cost at the ith sample, and Θ denotes the thermal comfort at sample j, and ∑ i = k + 1 k + P H J the total economic cost over PH. In order to enable the smooth operation of the system, each control action can only deviate by 3 ☌ around the previous control action, i.e., u ∈. In Equation (1), v P H denotes the set of allowable control action sequences in this case the control actions will be reference temperatures in degrees Celsius, or the value 0 that denotes that the HVAC is off. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. ![]() A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems.
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