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Title	Learning-Based Model Predictive Control For Smart Building Thermal Management
ID_Doc	34946
Authors	Eini R.; Abdelwahed S.
Year	2019
Published	HONET-ICT 2019 - IEEE 16th International Conference on Smart Cities: Improving Quality of Life using ICT, IoT and AI
DOI	http://dx.doi.org/10.1109/HONET.2019.8908098
Abstract	This paper proposes a learning-based model predictive control (MPC) approach for the thermal control of a four-zone smart building. The objectives are to minimize energy consumption and maintain the residents' comfort. The proposed control scheme incorporates learning with the model-based control. The occupancy profile in the building zones are estimated in a long-Term horizon through the artificial neural network (ANN), and this data is fed into the model-based predictor to get the indoor temperature predictions. The Energy Plus software is utilized as the actual dataset provider (weather data, indoor temperature, energy consumption). The optimization problem, including the actual and predicted data, is solved in each step of the simulation and the input setpoint temperature for the heating/cooling system, is generated. Comparing the results of the proposed approach with the conventional MPC results proved the significantly better performance of the proposed method in energy savings (40.56% less cooling power consumption and 16.73% less heating power consumption), and residents' comfort. © 2019 IEEE.
Author Keywords	Artificial neural network; Heating/cooling system; Learning-based model predictive control; Model-based control; Occupancy estimation; Smart building management and control

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