## 2 Related Work  
Recent developments in building automation have focused on smart HVAC systems that incorporate artificial intelligence (AI), Internet of Things (IoT), and predictive models to enhance energy efficiency and thermal comfort [4]. Moreover, by considering the indoor environmental parameters, occupant comfort and energy efficiency can be enhanced by using predictive optimization [5]. AI-driven predictive control is an appealing substitute for rule-based conventional thermostats. [PERSON] et al. [6] presented an AI-based hierarchical approach for forecasting indoor air temperature (IAT) using Vision Transformers (ViTs) and Bi-LSTM models for energy-efficient HVAC scheduling. [PERSON] and [PERSON] [7] used Multilayer Perceptrons (MLPs) to perform short-term indoor temperature forecasting at multiple resolution levels in hours, thereby establishing AI models' suitability for deployment at the microcontroller level. [PERSON] et al. [8] extended AI-based temperature forecasting through comparative studies of multiple machine learning models to emphasize ANN and Extra Trees as the most accurate techniques for dynamic thermal behavior in indoor spaces. Besides blackbox approaches, wearable and human-in-the-loop systems have been implemented to enhance individualized comfort. [PERSON] et al. [9] proposed an AI-based wireless wearable sensor that collects body thermal data from skin temperature monitoring in real time to provide dynamic HVAC adaptation based on physiological feedback. The system achieved an accuracy of 93.9 in thermal comfort prediction, providing an AI-based, human-centric temperature control system that could potentially save energy by 20%. Reinforcement Learning (RL) has also been receiving attention for energy optimization. [PERSON] et al. [10] used an SAC Deep RL controller on an actual building environment, surpassing PI and MPC controllers by 50% in energy savings while enhancing indoor temperature control by 68%. This is indicative of the application potential of Reinforcement Learning in building control with non-linear dynamics and variability in occupant occurrences. An important aspect of smart control systems is a secure and scalable communications protocol. [PERSON] [11] developed an intelligent monitoring system with two tiers based on both Modbus/RTU and Modbus TCP, effectively achieving serial communications' high dependability in combination with TCP/IP-based speed and scalability. The use of Modbus TCP protocol is gaining ground in building automation for HVAC component control as well as for enabling real-time communications between distributed devices. The security requirement for such systems is highlighted by their vulnerability to cyberattacks. [PERSON] et al. [12] presented a simulation-based framework for authenticating Modbus TCP in SCADA systems based on ID-based encryption as well as light-weight IDS/IPS to harden communication integrity. As smart control systems play an increasingly greater role in contemporary buildings, protection of the Modbus layer is vital for safe and uninterrupted HVAC functioning. Integration between AI and IoT in building settings is further presented by [PERSON] et al. [13], who developed an intelligent thermal comfort control system based on PMV-based AI algorithms as well as multiple sensors in a network to optimize HVAC functioning. In similar lines, [PERSON] et al. [14] developed an economically viable mini-split AC control system using PLCs and NodeMCU-based modules to achieve energy saving by 10.63% while maintaining stable indoor temperatures at 22-24 \({}^{\circ}\)C.  
This is complemented by neural network-based forecasting techniques such as those by [PERSON] et al. [8], who utilized gray-box models combined with ANN to forecast IAT using a wireless sensor network in test buildings. [PERSON] et al. [15] and [PERSON] et al. [16] built on these by utilizing Elman neural network as well as SVM to forecast cooling load and temperature at varying meteorological inputs. From an architecture point of view, [PERSON] et al.