Learning-based Model Predictive Control for Smart Building Thermal Management

TL;DR

This paper introduces a learning-based MPC approach for smart building thermal management that combines neural network occupancy estimation with model predictive control to significantly reduce energy use while maintaining comfort.

Contribution

It presents a novel integration of neural network occupancy prediction with MPC, improving energy efficiency and comfort in smart building thermal control.

Findings

40.56% reduction in cooling energy consumption

16.73% reduction in heating energy consumption

Enhanced resident comfort compared to conventional MPC

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.