## 2 Literature Review
The paper [40] presented a novel control approach for indoor temperature regulation in tertiary buildings, based on predictive functional control. The primary control objective is to reduce energy consumption, while maintaining the desired thermal comfort of the occupants. The proposed approach demonstrates the potential to perform effectively in the presence of disturbances, including changes in the occupancy profile, electrical equipment usage, indoor temperature, surrounding temperatures, and weather data. A model predictive control system with adaptive machine learning-based building models for building automation and control applications was proposed in [41]. The system incorporates an adaptive machine learning-based building dynamics modelling scheme that updates the building model on a regular basis, using online building operation data through a dynamic artificial neural network. Furthermore, the system utilizes a multi-objective function that may optimize both energy efficiency and indoor thermal comfort, two frequently contradictory demands. The proposed model predictive control system is implemented to regulate the HVAC and mechanical ventilation systems in two single-zone test beds, an office and a lecture theatre, situated in Singapore, for the purpose of experimental evaluation of its control performance. The implementation of a model predictive control system has been demonstrated to result in a reduction of 58.5% in cooling thermal energy consumption in the office environment and 36.7% in cooling electricity consumption in the lecture theatre, in comparison to the levels of consumption observed in the original control system. Furthermore, the introduction of the model predictive control system has also been shown to significantly enhance the indoor thermal comfort experienced by users in both testbeds. In the field of thermal energy storage systems and MPC, several references can also be considered. A preliminary study on the control of thermal energy storage in building cooling systems was presented in [42]. The focus of this study was on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. The paper [43] presented a systematic review of the application of model predictive control strategies to active thermal energy storage systems. This paper presented a discussion of the strengths and weaknesses of this technology, with particular emphasis on the difficulty of operating with complicated physical models as the key limitation to overcome. In reference [44], an MPC strategy was proposed as a means of improving the operation of a space-heating system that is coupled with renewable resources. This model employs a dynamic approach based on forecasting all energy inputs into the system over a specified period of time in advance, prior to the implementation of any operational decisions. The MPC strategy was implemented with the objective of reducing the annual energy costs associated with the heating system of a detached house, which was equipped with a heat pump, a thermal energy storage unit, and photovoltaic panels. The results demonstrated the efficacy of the model predictive control with a 24 h time horizon, indicating that energy cost savings of 58% could be achieved in comparison to the same heating system without smart control. The objective of the study [45] was to ascertain the feasibility of MPC strategy for commercial buildings in response to variations in occupancy and time-variant electricity prices, in comparison to a conventional, rule-based control (RBC) strategy. In reference [46], an MPC optimization framework that integrates a data-driven cooling load prediction model, a system physical model, and an advanced optimization algorithm was proposed and applied to a district cooling system (DCS) coupled with an ice-based TES in Beijing, China. The operational strategy of the DCS was optimized based on the predicted cooling load, with the objective of minimizing operating costs under the current time-of-use (ToU) tariff.