Air Conditioning Control Research Articles

Perceived Air Quality, Thermal Comfort and Health: A Survey of Social Housing Residents in Kazakhstan

Kazakhstan is in Central Asia and is the ninth-largest country in the world. Some socially vulnerable segments of the Kazakh population residing in subsidised social housing have experienced a range of problems due to the low quality of housing construction and its planning. Poor indoor environmental conditions in social housing contribute to occupants’ comfort, health, and general well-being. This study assessed social housing residents’ health and quality of life, focusing on their perceived indoor air quality and thermal comfort satisfaction. A cross-sectional survey in Kazakhstan was conducted to test the effects of environmental factors on social housing residents’ health and satisfaction. Four hundred thirty-one responses were analysed, and the SF12v2 questionnaire was used to measure the health-related quality of life. Multiple regression analysis showed that air quality negatively predicted the respondents’ physical (PCS) and mental (MCS) health. In addition, age, smoking, and employment status had a significantly negative effect on PCS, while education level had a predictive positive effect. Thermal conditions negatively predicted only MCS, as well as alcohol consumption. Next, the air-conditioning control factor had a negative effect. In contrast, low air circulation, low humidity, high solar gain, temperature imbalance, duration of the residence and alcohol consumption had a significantly positive effect on overall satisfaction with the temperature. The odour sources from tobacco, furniture and external sources were predictors of respondents’ overall air quality satisfaction, along with the duration of the residence, alcohol consumption and smoking status.

Developing a validated simulation model of micro-zonal air-conditioning to evaluate thermal comfort parameters

ABSTRACT Micro-Zonal Occupant-Centric Control (MZOCC) helps in reducing building energy consumption by conditioning only the occupied regions in a room. For this, MZOCC divides a large room such as open-plan offices into virtual micro-zones to allow independent diffuser level control. But, unplanned MZOCC can lead to heavy thermal gradients and deflection of air jets, leading to thermal discomfort. To evaluate thermal comfort at different regions of a room under MZOCC, there is a need to accurately model parameters of thermal comfort. Field studies, exploring the transient variations in air temperature, humidity and velocity at different regions of the room due to MZOCC, are limited. At the same time, developing validated simulation models or digital twins using experimental data helps in exploring micro-zonal strategies to arrive at the best strategy. The aim of this study is to create a digital twin of an open-plan office and model the parameters required to estimate thermal comfort. This study senses transient variations in air temperature, velocity and humidity using a wireless sensor network and develops a computational fluid dynamic (CFD) model that can accurately model the sensed transient variations. Field data, constituting external surface temperatures captured using thermal images, variations in air-conditioning control strategy, dynamic occupancy and location, are periodically updated in the digital twin. The CFD model accurately predicts the trends in variations in indoor temperature, velocity and humidity at different regions of the room. The developed digital twin can be used in future studies to evaluate thermal comfort and plan MZOCC control strategies.

Research on control strategy of rapid preheating for power battery in electric vehicle at low temperatures

Due to cold weather conditions, it is not advisable to use the remaining capacity of the power battery to heat it. Instead, the battery can only be preheated during charging when the battery power is almost consumed up. The traditional positive temperature coefficient (PTC) heating system combines the cockpit air conditioning and heating system with the low-temperature preheating system for the power battery cells. The PTC controller is integrated into the air conditioning control system. However, this integration requires the air conditioning to be activated in order to preheat the power battery. As a result, there is energy waste during the charging process. To address the aforementioned issues, this article proposes a rapid preheating system that utilizes an independent liquid circuit equipped with a high-voltage heater. The system achieves a preheating time of 2000 s for a ternary lithium battery with a capacity of 73kWh and a rated voltage of 770 V. Another important finding is that fluctuations in the current of the battery management system (BMS) occur during the rapid preheating process, leading to uncontrolled battery charging and discharging. The battery rapid preheating control strategy has been redesigned to rapidly heat the battery system by disconnecting the rapid charging relay of the high-voltage circuit, thereby prevents over-discharge and overcharge of the power battery. Experiments have shown that the BMS current increases or decreases in a stepwise manner, as expected by the design. The average temperature of the battery pack during onboard charge module slow charging and direct current fast charging is below 35 °C, and the maximum temperature difference is less than 6 °C. The proposed rapid preheating system and improved battery charging architecture can shorten the charging time and reduce energy consumption. This advancement will open up new possibilities for power battery protection and contribute to the development of lithium-ion batteries for electric vehicles at low temperatures.

Successful application of predictive information in deep reinforcement learning control: A case study based on an office building HVAC system

Reinforcement Learning (RL), a promising algorithm for the operational control of Heating, Ventilation, and Air Conditioning (HVAC) systems, has garnered considerable attention and applications. However, traditional RL algorithms typically do not incorporate predictive information for future scenarios, and only a limited number of studies have examined the enhancement and impact of predictive information on RL algorithms. To address the issue of coupling RL and predictive information in HVAC system operation optimization, we employed an open-source framework to examine the impact of various predictive information strategies on RL outcomes. We propose a joint gated recurrent unit (GRU)-RL algorithm to handle situations where a time-series exists in state space. The results from four classic test cases demonstrate that the proposed GRU-RL method can reduce operating costs by approximately 14.5% and increase comfort performance by 88.4% in indoor comfort control and cost-management tasks. Moreover, the GRU-RL method outperformed the conventional DRL method and was merely augmented with prediction information. In indoor temperature regulation, the GRU-RL algorithm improves control efficacy by 14.2% compared to models without predictive information and offers an approximately 5% improvement over traditional network models. Finally, all models were made open source for easy replication and further research.

Handling the Constraints in Min-Max MPC

One of the major sources of conservativeness in min-max model predictive control (MPC) is the handling of constraints, where the ellipsoidal robust invariant set is utilized for the proposal of sufficient and conservative conditions for the satisfaction of the constraints. In this article, in order to reduce the conservativeness due to the constraint handling, we add additional relaxation variables to the physical constraints and propose a two-step approach through relaxing the constraints by the amount which is determined by the calculating of the maximal admissible set (MAS). The constraints are relaxed in an iterative manner to avoid the constraints violation, and the constraint relaxation variables are degrees of freedom for relaxing the constraints and improving the control performance. Moreover, we show that under certain circumstance, the physical constraints can be removed without the constraint violation. The proposed approach is shown to be recursively feasibility and its effectiveness is verified through an air conditioning control in a building energy system. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Buildings are account for large percentage of worldwide energy consumptions. One of the most applicable method for optimization of building energy system subject to multiple constraints is the model predictive control (MPC). However, the industrial MPC is usually not recursively feasible, which implies that the optimization problem can become infeasible and the software will be terminated at some time. In order to apply the MPC synthesis approach (MPC with recursive feasibility guarantee), we have to overcome the conservativeness problem due to the handling of constraints. We propose a useful approach in this work by introducing relaxation variables which act as degrees of freedom for improving the control performance, while the physical constraints are still satisfied. The proposed approach is verified through an example of a 24m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^2$</tex-math> </inline-formula> office room located in Cyber-Physical Energy System (CPES) lab in Western China Science and Technology Innovation Harbour in Xianyang, China. The numerical results show the performance improve of the proposed approach.

The Sub-Metered HVAC Implemented for Demand Response Dataset

Abstract Closed-loop control of commercial building heating, ventilating, and air conditioning (HVAC) for demand response requires measurements used as feedback to the controllers. Demand response effectiveness is usually measured as a power deviation from baseline, but the building automation system (BAS) does not usually collect power measurements, and whole-building electric meters typically measure power at intervals of 15 min, which may be too slow for some types of demand response. Demand response strategies are sometimes focused on components of building HVAC systems, e.g., the response of supply/return fans to temperature set-point changes, but these components are usually not submetered. Fan power can be estimated from physics-based models leveraging BAS data, e.g., airflow measurements; but our ability to effectively close the loop on these estimates is not clear. In this paper, we introduce a massive dataset that contains both submetered fan power data and BAS data for several building HVAC systems during typical operation and demand response events. Through a case study we show that models leveraging BAS data alone do not provide accurate estimates of fan power during event transients, making it unlikely that closed-loop control of commercial building HVAC components for demand response would be effective using BAS data alone. This demonstrates the value of submetering HVAC components. More broadly, our dataset will enable future research bridging the gap between building control and power systems research.

A Comparison of Classical and Reinforcement Learning-based Tuning Techniques for PI controllers

This study compares two tuning techniques for Proportional-Integral (PI) controllers. The first strategy uses the Pole-Zero Cancellation method, a well-established technique in the field of dynamic systems control. The second strategy introduces an innovative approach by using a reinforcement learning-based technique to adaptively tune a PI controller. To compare these tuning methodologies, a Heating, Ventilation and Air Conditioning (HVAC) control system was selected as a case study to guarantee users’ thermal comfort. In both cases, the HVAC process was modelled as a first-order system without time delay. In addition, the performance of the proposed controllers analysed by evaluating temperature set-point tracking and disturbance rejection caused by the occupancy of people in the room. The results demonstrate that classical methods are efficient and quick to implement, while the use of RL also enables optimization of energy consumption and reduction of operating costs.

Smoothing cooling demand of buildings with PCM thermal batteries

Escalating energy tariffs and peak cooling demands due to climate change along with expanding use of variable renewable energy supply are presenting new challenges and opportunities for air conditioning system operation and control. This research presents the outcome of an investigation into the use of a thermal battery using salt-hydrate phase change material (PCM) in commercial buildings. A 1.2 m3 modular thermal battery using 15 °C melting temperature salt-hydrate PCM has been designed and fabricated. Its cooling performance and feasibility of integration into a chilled water-cooling system of commercial buildings has been comprehensively investigated. This storage unit can accommodate approximately 52 kWh of energy, featuring a rapid heat discharge rate of 32.58 kW during the initial 30 min to effectively address sudden cooling demands. The overall heat discharge rate closely aligns with simulation results, reaching approximately 96% accuracy. This has been achieved through optimisation of the heat exchanger design through mathematical simulation, detailed testing to match various operational scenarios and evaluation of economic and peak load shifting benefits. The results demonstrate the environmental and economic effectiveness of the PCM thermal battery as an independent component in building cooling systems. It provides a timely response to peak cooling demand and improves thermal comfort of the buildings.

Smart Lab: A Non-invasive IoT-centric Approach for Indoor Environment Automation

This work presents a non-invasive Internet of Things (IoT)-centric framework solution for automation in indoor environments (e.g., laboratories, offices, and houses). The platform allows for Internet-connecting electronic and non-electronic devices for low-cost and fast automation control rather than replacing them as market solutions propose. Moreover, the IoT solution comprises adaptive hardware modules, a mobile application, a database, and a Message Queuing Telemetry Transport (MQTT)-based broker server deployed in a real scenario for proof of concept. The mobile application functions manage the hardware modules to remote control lamps, air conditioners, TVs, and curtains. The commands are set through predefined touchscreen buttons or voice commands interpreted by a virtual assistant based on natural language processing (NLP) and natural language understanding (NLU) machine learning algorithms embedded in a framework called Wit.ai. The database handles user subscribing by affording administration functions. Finally, a real-time broker server manages the entities' communication based on the MQTT message exchange protocol, including a security layer based on the Transport Layer Security (TLS) protocol.

Demand Response and HVAC Controls in Smart Grid Integration

This extensive research paper meticulously investigates the intricate synergies between demand response strategies and Heating, Ventilation, and Air Conditioning (HVAC) controls within the dynamic realm of smart grid integration. As the global energy landscape undergoes transformative changes driven by technological advancements and sustainability imperatives, the study critically examines the symbiotic relationship between consumers, HVAC systems, and the smart grid. The paramount importance of this interaction is underscored, emphasizing its pivotal role in achieving elevated levels of energy efficiency, grid reliability, and sustainability. The research explores the multifaceted potential inherent in demand response programs and advanced HVAC controls, unraveling their collective capacity to reshape the energy ecosystem. It goes beyond the theoretical framework by delving into real-world applications, case studies, and cutting-edge technological advancements. By doing so, the study seeks to provide not only a theoretical understanding but also practical insights into the dynamic and evolving landscape of demand-side management. This investigation is poised to contribute significantly to the existing body of knowledge by illustrating how the harmonious coordination of consumer behaviors and HVAC systems can be strategically harnessed. This strategic collaboration aims to optimize energy consumption, mitigate peak loads, and contribute substantively to the establishment of a more sustainable, adaptive, and responsive energy infrastructure. As the global community grapples with the imperative to transition towards cleaner and more efficient energy systems, this research positions itself as a valuable resource for policymakers, researchers, and industry stakeholders seeking innovative solutions for a resilient and sustainable energy future.

Research on Micro-Turbine Operating State Characterization Based on Bearing Vibration Signals Analysis

An air flow-driven micro-turbine, widely used in air-condition control systems in aircraft cabins, train coaches, etc., exhibits complex vibration behaviors under stable or unstable inlet flow conditions, and especially has a certain correlation with speeds. In this paper, the vibration responses of the micro-turbine undergoing stable and unstable inlet flows measured on a test rig are analyzed and compared by using different signal processing methods, which include time and frequency domain methods, and statistical and nonlinear methods. First, the test rig system of the airflow-driven micro-turbine, the instrument system and four typical experimental cases are introduced. Then the measured vibration signals are analyzed and compared by time domain characteristic parameters (peak-to-peak value, RMS value and kurtosis value), statistical parameters (auto-correlation and BDS), and amplitude spectra in the frequency domain, statistic spectrum indicators (SSI) based on Welch's periodogram of power spectra, and the spectra of selected IMF components based on Hilbert-Huang Transform (HHT). In particular, some nonlinear feature analyzing methods, including Pseudo-Poincare mapping diagrams and Lempel-Ziv(LZ) complexity, are also used for analyzing measured vibration responses. The obtained results using the above multiple methods are compared and show that, when the inlet flow of the turbine fluctuates significantly, the nonlinear characteristics of the turbine bearings are significantly higher than those of the relatively stable inlet flow and speed conditions. Under these circumstances, commonly used time-frequency analysis methods cannot characterize the different speed operating state of the turbine, and LZ-Complexity and other nonlinear characterization methods should be used to better understand the characteristics of different speeds under unstable conditions.This study provides references for the aerodynamic stability monitoring of the micro-turbine and its design improvement.

Predicting Temperature and Humidity in Roadway with Water Trickling Using Principal Component Analysis-Long Short-Term Memory-Genetic Algorithm Method

The heat dissipated from high geo-temperature underground surrounding rocks is the main heat source of working faces, while thermal water upwelling and trickling into the roadway will notably deteriorate the mine’s climate and thermal comfort. Predicting airflow temperature and relative humidity (RH) is conductive to intelligent control of air conditioning cooling and ventilation regulation. To accommodate this issue, an intelligent technique was proposed, integrating a genetic algorithm (GA) and long short-term memory (LSTM) based on rock temperature, inlet air temperature, water temperature, water flow rate, RH, and ventilation time. A total of 21 input features including over 200 pieces of data were collected from an independently developed modeling roadway to construct a dataset. Principal component analysis (PCA) was conducted to reduce features, and GA was used to tune the LSTM and PCA-LSTM architectures for best performance. The following research results were yielded. The proposed PCA-LSTM-GA model is more reliable and efficient than the single LSTM model or hybrid LSTM-GA model in predicting the air temperature Tfout and humidity RHout at the end of the water trickling roadway. The importance scores (ISs) indicate that Tfout is mainly influenced by the surrounding rock temperature (IS 0.661) and the inlet air temperature (IS 0.264). While RHout is primarily influenced by the rock temperature in the water trickling section (IS 0.577), the inlet air temperature (IS 0.187), and the trickling water temperature and flow rate (total IS 0.136), and it has an evident time effect. In addition, we developed relevant equipment and provided engineering practice methods to use the machine learning model. The proposed model, which can predict the mine microclimate, serves to facilitate coal and geothermal resource co-mining as well as thermal hazard control.

Mixed air temperature reset by data-driven model for optimal economizer control

An economizer system means that outside air is introduced to reduce energy consumption when the outdoor air is sufficient to be used for cooling, and adjusts the outdoor, ventilation, and exhaust damper to maintain the mixed air temperature set value during heating, ventilation and air conditioning control. The set values for economizer control include high and low limit that determine whether to operate economizer, and mixed air temperature. At this time, the mixed air temperature is set to a fixed value. Such a fixed set value may cause problems such as energy waste because it does not consider changes in indoor and outdoor environments. Therefore, in this study, an optimal economizer control was developed that produces the maximum economizer effect by resetting the mixed air temperature set value considering the indoor and outdoor conditions. For this, a data-driven prediction model was developed, and based on the prediction model, an optimal economizer control was developed that resetting the mixed air temperature set value. And the developed control was evaluated by TRNSYS-Matlab co-simulator, and as a result, the mixed air temperature set value was changed in real time, and energy consumption was reduced by about 20% compared to the dry-bulb temperature control.

Application of Variable Theory Domain Fuzzy Control Algorithm for Room Temperature Control

With the development of automatic control in the HVAC industry, more and more buildings have adopted automatic control of air conditioning, HVAC systems, but most of the control results can not be satisfactory. The research object of this paper is the elevator machine room equipped HVAC, through accurate control of that room temperature can greatly reduce the elevator shutdown caused by the action of the temperature protection circuit, so as to improve the safety of elevator operation. This paper gives three different temperature control methods, establishes the math representation of the temperature system in the machine room, specifically designs the classical PID controllers, Fuzzy PID controllers and variable domain Fuzzy PID controller, and simulates and analyzes the three algorithms respectively, The results indicate that in comparing the above three algorithms, the variable domain fuzzy PID algorithm has an adjustment time of 63s, and the overshooting amount of 0.4%, which is the best among the three control algorithms.

A novel deep-learning framework for short-term prediction of cooling load in public buildings

Optimal control of heating, ventilation, and air conditioning (HVAC) systems, along with demand-side management, are both cost-effective methods in the process of energy conservation and carbon reduction. The successful implementation of these initiatives largely hinges on accurate cooling load predictions. Due to the complex nonlinear and dynamic time-varying nature of demand loads, however, it is a formidable challenge to accurately predict the cooling load. To address these issues, a novel deep learning-based prediction framework, aTCN-LSTM, is proposed. First, a gate-controlled multi-head temporal convolutional network is designed to capture the inherent nonlinear and local temporal features from the time series of cooling loads. Second, a sparse probabilistic self-attention mechanism is integrated with a bidirectional long short-term memory (BiLSTM) network to extract the long-term dependencies within the cooling load sequences. Finally, through integration with the proposed two components, the framework is developed and validated through a 14-month real cooling load forecasting problem for a 51-story hotel building in Guangzhou, China. Experiments and comparison studies demonstrate the effectiveness and superiority of the proposed method. The mean absolute percentage error of the proposed method's 1-step, 6-step, and 12-step prediction results is reduced by 27.48%, 14.05%, and 13.38%, respectively, compared with the state-of-the-art baseline model. Consequently, it stands poised to serve as an effective guide for HVAC chiller scheduling and demand management initiatives.

Energy-Saving Control Method for Factory Mushroom Room Air Conditioning Based on MPC

The energy consumption of the mushroom room air conditioning system accounts for 40% of the total energy consumption of the mushroom factory. Efficient and energy-efficient mushroom factories and mushroom houses are the development direction of the industry. Compared with maintenance structure transformation and air conditioning equipment upgrading, energy-saving technology based on regulation methods has the advantages of less investment and fast effectiveness, which has attracted attention. The current methods for regulating air conditioning in edible mushroom factories include simple on/off thermostat control or PID. In the field of energy efficiency in commercial building air conditioning, a large number of studies have shown that compared with traditional control algorithms such as classic on/off or PID control, model predictive control can significantly improve energy efficiency. However, there is little literature mentioning the application of MPC in factory mushroom production rooms. This paper proposes a data-driven MPC and PID combined energy-saving control method for mushroom room air conditioning. This method uses the CNN-GRU-Attention combination neural network as the prediction model, combined with prediction error compensation and dynamic update mechanism of the prediction model dataset, to achieve an accurate prediction of indoor temperature in mushroom houses. Establish an objective function for air conditioning control duration and temperature, use the non-dominated sorting genetic algorithm II (NSGA-II) to solve for the optimal control sequence of the air conditioning in the control time domain, and use the entropy weight method to determine the optimal decision quantity. Integrate rolling optimization, feedback mechanism, and PID to achieve precise and energy-saving control of the mushroom room environment. The experimental results show that compared with the on/off thermostat and PID controller, the designed controller reduces power consumption by 12% and 5%, respectively, and has good application and demonstration value in the field of industrial production of edible mushrooms.

Development of personalized predicted mean vote based on a real-time clothing insulation recognition system

As people’s standards for indoor environmental quality and life are getting higher and higher, many studies try to find ways to improve indoor thermal comfort through experiments and simulations. Predictive mean voting (PMV) is a widely used indicator, but personal factors used to calculate PMV are not easy to monitor. This study aimed to develop a real-time clothing insulation identification (R-CLO) system to estimate personal PMV. This study is mainly divided into three stages. The first stage is to issue questionnaires to obtain the value of the personal clothing level and analyze the correlation between different external temperatures and the clothing insulation (Icl). The second stage is to build a real-time clothing recognition model and analyze its recognition accuracy. The third stage is to use ANN to establish a clothing insulation transition model and use the outdoor temperature and the results of clothing recognition to estimate Icl. This research also established an experimental environment to verify the system’s feasibility. The results show that the system has more than 80% accuracy rate for clothes recognition, and the accuracy rate of short-sleeved tops, shorts, trousers, and skirts is more than 90%. Moreover, most of the Icl estimated by the ANN have less than 0.1 differences from the value reported by occupants. In the future, this system can also combine occupant behavior recognition to strengthen PMV prediction results to achieve more precise, comfortable air-conditioning control.

Transfer learning for occupancy-based HVAC control: A data-driven approach using unsupervised learning of occupancy profiles and deep reinforcement learning

Model-free heating, ventilation, and air conditioning (HVAC) control systems have demonstrated promising potential for adjusting indoor setpoint temperature based on dynamic occupancy patterns in smart buildings. Although these control systems offer the advantage of not needing building or occupancy models, the involved trial-and-error learning process can cause considerable thermal discomfort for occupants, particularly during the initial learning period. Given the critical importance of thermal comfort, this limitation is a major barrier to the practical implementation of such systems. To address this challenge, the present study proposes a framework to enhance the learning process of the model-free HVAC controllers. Specifically, a transfer learning (TL) technique is adopted based on a similarity analysis of occupancy patterns using an unsupervised learning of occupancy profiles. This control framework leverages a k-means clustering algorithm with dynamic time warping to match the most similar households in terms of occupancy patterns within 26 residential units. The results demonstrate that the proposed method significantly improves the performance of the HVAC control system. It enhances the jumpstart performance and total rewards by nearly 25% and 5%, respectively, compared to a conventional model-free controller. Furthermore, it reduces the deviation period and mean temperature deviation by approximately 4% and 68%, respectively. Overall, this framework presents a promising approach to enhancing the performance and practicality of model-free HVAC control systems by reducing the thermal discomfort during the learning process.

Development of intelligent control system of hvac by using machine learning

The article is devoted to the problem of regulation of heat supply and air conditioning in the room. An automated system for monitoring the dynamic characteristics of such sensors is described, which is a software and hardware complex for setting up a test bench and analyzing the parameters of sensors for dynamic temperature control and air conditioning. The main purpose of this system is automation of one room by controlling air conditioning and supporting a temperature. The system performs the functions of controlling the Google Coral USB Accelerator, configuring the ADC and determining the amplitudefrequency and phase-frequency characteristics of temperature sensors, switches, leak sensors and air conditioning based on the results of their experimental studies on the stand for monitoring the dynamic characteristics of sensors and monitoring in the SCADA Genesis64 program. The scheme of the test bench, the generalized algorithm of the system operation and the screen form of the program operation are presented. The software of the automated system for temperature control and air conditioning in the room is developed on the basis of ModBus TCP, OPC UA and SCADA programs.

Retrofit Analysis of City-Scale Residential Buildings in the Hot Summer and Cold Winter Climate Zone

The rising energy consumption in residential buildings within the hot summer and cold winter (HSCW) climate zone, driven by occupants’ pursuit of improved thermal comfort, necessitates effective energy conservation measures. This study established urban building energy models for 32,145 residential buildings in Changsha City, China, and conducted a comprehensive retrofit analysis of seven energy conservation measures (ECMs). Additionally, the study assessed the impact of residents’ conscious energy-saving behaviors concerning air conditioner (AC) control. The research commenced by creating six baseline models representative of the diverse building stock. Identifying seven commonly used ECMs, the study examined the potential of each measure for enhancing energy efficiency. To facilitate the analysis, a dedicated toolkit, AutoBPS-Retrofit, was developed to efficiently modify the baseline model for each ECM. Furthermore, the investigation delved into the investment cost of implementing the ECMs and evaluated their simple payback year (PBP) and net present value (NPV). The results demonstrate that tailored retrofit plans are essential when addressing envelope improvements, varying according to building types and ages. Retrofits targeting lighting systems offer both promising energy savings and favorable economic viability, albeit subject to residents’ preferences. Alternatively, upgrading the AC systems emerges as the most energy-efficient approach, yet the economic assessment raises concerns. The study’s findings offer practical insights for governments seeking to establish effective carbon reduction goals and policies. Moreover, the research can assist energy-saving institutions, real-estate companies, and stakeholders involved in renovation projects by offering guidance in making informed decisions to enhance energy efficiency in city-scale residential buildings.