Performance Optimizations Research Articles

Reinforcement Learning for Synchronization of Heterogeneous Multiagent Systems by Improved Q-Functions.

This article dedicates to investigating a methodology for enhancing adaptability to environmental changes of reinforcement learning (RL) techniques with data efficiency, by which a joint control protocol is learned using only data for multiagent systems (MASs). Thus, all followers are able to synchronize themselves with the leader and minimize their individual performance. To this end, an optimal synchronization problem of heterogeneous MASs is first formulated, and then an arbitration RL mechanism is developed for well addressing key challenges faced by the current RL techniques, that is, insufficient data and environmental changes. In the developed mechanism, an improved Q-function with an arbitration factor is designed for accommodating the fact that control protocols tend to be made by historic experiences and instinctive decision-making, such that the degree of control over agents' behaviors can be adaptively allocated by on-policy and off-policy RL techniques for the optimal multiagent synchronization problem. Finally, an arbitration RL algorithm with critic-only neural networks is proposed, and theoretical analysis and proofs of synchronization and performance optimality are provided. Simulation results verify the effectiveness of the proposed method.

Effect of surface square textures on the physical field of static and dynamic pressure thrust bearings and multi-objective optimization

Purpose The lubrication performance of static and dynamic pressure thrust bearings is improved by introducing texture on the sealing edge. Design/methodology/approach Through model building, meshing and boundary condition setting, the influence of square texture on oil film lubrication performance was simulated and analyzed, and an improved algorithm was applied to perform optimization of lubrication performance. Findings The findings of this study reveal that the optimum lubrication performance is attained when adjusting the parameters of the square texture to 0.12 mm, 0.1 mm, 1 mm and 34 mm. In such circumstances, the thrust bearing with square textures demonstrates an increase in loading capacity of around 19% and a temperature reduction of about 2ºC compared to a smooth thrust bearing. Originality/value The original Reynolds equation is revised, and the influence of square texture on the physical field of oil film is analyzed, considering the turbulence state and cavitation phenomenon. The multi-objective function under square texture parameters was established using BP neural network, and the improved multi-objective salp swarm algorithm was used to optimize the process parameters.

Operational characteristics and performance optimizations of the organic Rankine cycle under different heat source/condensing environment conditions

The fluctuating heat sources and seasonal condensing environments are critical to the operation of Organic Rankine Cycle (ORC) under off-design conditions. This paper presents a comprehensive steady-state mathematical model developed using a newly built experimental platform. Focusing on the operational characteristics, including the evaporation pressure, condensation temperature, mass flow rate, and the performance of key equipment during the regulation of the expander, working fluid pump, and cooling water pump. Results show that the net efficiency initially increases before decreases, with both evaporation pressure and mass flow rate rising alongside the working fluid pump frequency. The quasi-two-stage single screw expander achieves an isentropic efficiency above 65 %. Additionally, flexible adjustments to the cooling water pump effectively regulate the cooling system, with timely cleaning of cooling pipelines enhancing net efficiency by up to 3.27 %. Optimization using the particle swarm algorithm improves system performance, achieving net efficiency enhancements of 1.8 % and 12.3 % under specific conditions. The novelty of this study lies in directly regulation of the expander, working fluid pump, and cooling water pump, significantly enhancing the off-design performance. This research provides valuable insights for researchers and designers, enabling flexible regulation of ORC operations to ensure efficient performance under varying conditions.

Prediction Models for Perioperative Blood Transfusion in Patients Undergoing Gynecologic Surgery: A Systematic Review.

This systematic review aimed to evaluate prediction models for perioperative blood transfusion in patients undergoing gynecologic surgery. Given the inherent risks associated with blood transfusion and the critical need for accurate prediction, this study identified and assessed models based on their development, validation, and predictive performance. The review included five studies encompassing various surgical procedures and approaches. Predicting factors commonly used across these models included preoperative hematocrit, race, surgical route, and uterine fibroid characteristics. However, the review highlighted significant variability in the definition of perioperative periods, a lack of standardization in transfusion criteria, and a high risk of bias in most models due to methodological issues, such as a low number of events per variable, inappropriate handling of continuous and categorical predictors, inappropriate handling of missing data, improper methods of predictor selection, inappropriate measurement methods for model performance, and inadequate evaluations of model overfitting and optimism in model performance. Despite some models demonstrating good discrimination and calibration, the overall quality and external validation of these models were limited. Consequently, there is a clear need for more robust and externally validated models to improve clinical decision-making and patient outcomes in gynecologic surgery. Future research should focus on refining these models, incorporating rigorous validation, and adhering to standardized reporting practices.

Finite element analysis and surrogate-optimized design of a nature-inspired auxetic stent

Prior studies have revealed that the structural design of stents is critical to reducing some of the alarming post-operative complications associated with stent-related intervention. However, the technical search for stents that guarantee robustness against stent-induced post-intervention complications remains an open problem. Along this objective, this study investigates a re-entrant auxetic stent's structural response and performance optimizations. In pursuit of the goal, a nonlinear finite element analysis (FEA) is employed to uncover metrics characterizing the auxetic stent’s mechanical behavior. Subsequently, the non-dominated sorting genetic algorithm (NSGA-II) is implemented to simultaneously minimize the stent’s von Mises stress and the elastic radial recoil (ERR). Results from the FEA revealed a tight connection between the stent’s response and the features of the base auxetic building block (the rib length, strut width, and the re-entrant angle). It is observed that the auxetic stent exhibits a much lower ERR. Besides, larger values of its rib length and re-entrant angle are noticed to favor smaller von Mises stress. The Pareto-optimal front from the NSGA-II-based optimization scheme revealed a sharp trade-off in the simultaneous minimization of the von Mises stress and the ERR. Moreover, an optimal combination of the auxetic unit cell’s geometric parameters is found to yield a much lower maximum von Mises stress of ≈ 403 MPa and ERR of ≈ 0.4 % .

Performance optimization of a printed circuit heat exchanger for the recuperated gas turbine

To further improve the thermal efficiency of gas turbines, the recuperator needs to be employed and the printed circuit heat exchanger with airfoil fins meets the recuperator's requirements of high heat transfer capacity, low flow resistance, and high reliability. However, the effect of airfoil fin pitches is not clear and the structure optimization method should be developed. In this study, the heat transfer quantity and pressure drop per length of the flue gas in the airfoil channels are numerically investigated under different Reynolds numbers and airfoil fin pitches. The results show that the effect of the airfoil transverse pitch has a great effect on the performance of the flue gas and the heat transfer quantity is increased by 82.39 %–96.08 % when the transverse pitch is decreased from 6.0 mm to 1.2 mm. At the same time, the pressure drop per length is increased to 189.39–842.47 Pa/m. The heat transfer performance is varied not obviously when the airfoil longitudinal pitch is varied from 4 mm to 15 mm. Based on the numerical data, the multiple-objective genetic algorithm combined with the backpropagation neural network is proposed for the performance predictions and optimizations. The corresponding Pareto front is obtained and the technique for order preference by similarity to the ideal solution method is employed to obtain the optimal point. Under the current conditions, the heat transfer quantity of the optimal point is 521.25 kW/m2, which is decreased by 34.5 % compared to the highest value, and the pressure drop per length of the optimal point is 108.45 Pa/m, which is decreased by 85.2 % compared to the highest value.

Diagnostic models to predict structural spinal osteoarthritis on lumbar radiographs in older adults with back pain: Development and internal validation

ObjectiveIt is difficult for health care providers to diagnose structural spinal osteoarthritis (OA), because current guidelines recommend against imaging in patients with back pain. Therefore, the aim of this study was to develop and internally validate multivariable diagnostic prediction models based on a set of clinical and demographic features to be used for the diagnosis of structural spinal OA on lumbar radiographs in older patients with back pain. DesignThree diagnostic prediction models, for structural spinal OA on lumbar radiographs (i.e. multilevel osteophytes, multilevel disc space narrowing (DSN), and both combined), were developed and internally validated in the ‘Back Complaints in Older Adults’ (BACE) cohort (N ​= ​669). Model performance (i.e. overall performance, discrimination and calibration) and clinical utility (i.e. decision curve analysis) were assessed. Internal validation was performed by bootstrapping. ResultsMean age of the cohort was 66.9 years (±7.6 years) and 59% were female. All three models included age, gender, back pain duration and duration of spinal morning stiffness as predictors. The combined model additionally included restricted lateral flexion and spinal morning stiffness severity, and exhibited the best model performance (optimism adjusted c-statistic 0.661; good calibration with intercept −0.030 and slope of 0.886) and acceptable clinical utility. The other models showed suboptimal discrimination, good calibration and acceptable decision curves. ConclusionAll three models for structural spinal OA displayed lesuboptimal discrimination and need improvement. However, these internally validated models have potential to inform primary care clinicians about a patient with risk of having structural spinal OA on lumbar radiographs. External validation before implementation in clinical care is recommended.

Leveraging AI with Databricks and Azure Data Lake Storage

The integration of artificial intelligence (AI) with cloud-based analytics platforms has revolutionized data processing and decision-making. This research article explores the synergies between AI, Databricks, and Azure Data Lake Storage (ADLS), showcasing how organizations can harness AI capabilities to enhance data analytics workflows. Through a comprehensive analysis of real- world use cases, scalability assessments, performance optimizations, and cost efficiency evaluations, we demonstrate the transformative impact of AI-driven analytics on business outcomes.

CFD simulation and design of ceramic hollow fiber membrane stack for oxygen separation

Hollow fiber ceramic membrane technology demonstrates a great potential for high performance oxygen separation from air. Upscaling of single hollow fiber membrane for membrane stacks and modules is necessary toward practical applications. However, experimental methods are very time-consuming and highly cost. Mathematical modeling is a cost-effective technique and very flexible to evaluate different upscaling strategies. In this research, built upon the experimental results of a proof-of-concept hollow fiber membrane stack, a computational fluid dynamics-based Multiphysics stack model is developed and validated. Comprehensive simulations are conducted to understand the behaviors of stacks under different operating conditions. Different designs strategies are also evaluated toward optimizations of stack performance.

Multi-Objective Optimization for Complex Systems Considering Both Performance Optimality and Robustness

As engineering systems become increasingly complex, performance requirements rise, and tolerance for design parameter variations becomes more crucial due to increased uncertainty. Tolerance to parameter variation can be measured by the volume of the solution space. A larger solution space implies a higher tolerance to parameter changes and thus greater robustness. The box-shaped solution space, represented by intervals with respect to each design parameter, has the advantage of showing which design parameters can be decoupled. Therefore, this paper proposes a new multi-objective optimization problem to optimize both the performance and volume of the box-shaped solution space simultaneously. Often, optimal performance and maximum volume are conflicting objectives, indicating a trade-off between performance and robustness. Furthermore, the DIRECT-NSGA-II approach is proposed for solving this multi-objective optimization problem. The DIRECT algorithm evaluates the minimum value of the performance function within the box-shaped solution space, while the NSGA-II algorithm identifies Pareto-optimal solution spaces. Finally, two case studies are implemented to illustrate the effectiveness of the DIRECT-NSGA-II method. We can conclude that (I) the proposed DIRECT-NSGA-II approach is suitable for black-box performance functions, (II) any point within the obtained solution space is a good design point, and (III) the proposed optimization problem considers both performance optimality and robustness, enabling the identification of a representative set of Pareto-optimal solution spaces that balance these two factors.

Cost–Performance Optimization of a Renewable Energy Resources Based Multimicrogrid System

The growing energy demand, the increase in its price, and the increasing pollution worldwide are considered large problems that need unusual solutions. To overcome these problems, it is necessary to search for innovative solutions using renewable resources and energy management of the energy systems. Energy management means reducing the operating, maintenance, and generation costs of the system and enhancing system performance with methods such as power loss reduction and stability enhancement and alleviating the harmful emissions to the environment. Thus, the energy management of a micro-grid has become one of the most vital aspects of the power or energy system all over the world.The objective of this paper is to optimize a multiobjective problem of a renewable energy resources (RER) based multimicrogid (MMG) system considering the output variation of the photovoltaic (PV) and wind Turbine (WT) as renewable energy resources and variation of the load demand and electricity prices. In this paper three microgrids (MGs) are connected with IEEE 33-bus distribution system each MG consists of a PV and WT. A three objective functions are modeled for this system to optimize the total annual cost, the voltage deviation and the voltage stability index (cost –performance multiobjective function). The optimization problem is solved with particle swarm optimization (PSO) technique for the system with and without RER.A comparison is carried out with two other optimization techniques, mountain gazelle optimization (MGO) and gorilla troop optimization (GTO). The results of the simulation show that the system cost is considerably reduced and the performance optimized.

Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of Massive-Parallel Trajectory Calculations (MPTRAC) v2.6

Abstract. Lagrangian particle dispersion models are indispensable tools for the study of atmospheric transport processes. However, Lagrangian transport simulations can become numerically expensive when large numbers of air parcels are involved. To accelerate these simulations, we made considerable efforts to port the Massive-Parallel Trajectory Calculations (MPTRAC) model to graphics processing units (GPUs). Here we discuss performance optimizations of the major bottleneck of the GPU code of MPTRAC, the advection kernel. Timeline, roofline, and memory analyses of the baseline GPU code revealed that the application is memory-bound, and performance suffers from near-random memory access patterns. By changing the data structure of the horizontal wind and vertical velocity fields of the global meteorological data driving the simulations from structure of arrays (SoAs) to array of structures (AoSs) and by introducing a sorting method for better memory alignment of the particle data, performance was greatly improved. We evaluated the performance on NVIDIA A100 GPUs of the Jülich Wizard for European Leadership Science (JUWELS) Booster module at the Jülich Supercomputing Center, Germany. For our largest test case, transport simulations with 108 particles driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis, we found that the runtime for the full set of physics computations was reduced by 75 %, including a reduction of 85 % for the advection kernel. In addition to demonstrating the benefits of code optimization for GPUs, we show that the runtime of central processing unit (CPU-)only simulations is also improved. For our largest test case, we found a runtime reduction of 34 % for the physics computations, including a reduction of 65 % for the advection kernel. The code optimizations discussed here bring the MPTRAC model closer to applications on upcoming exascale high-performance computing systems and will also be of interest for optimizing the performance of other models using particle methods.

SimRNAweb v2.0: a web server for RNA folding simulations and 3D structure modeling, with optional restraints and enhanced analysis of folding trajectories.

Research on ribonucleic acid (RNA) structures and functions benefits from easy-to-use tools for computational prediction and analyses of RNA three-dimensional (3D) structure. The SimRNAweb server version 2.0 offers an enhanced, user-friendly platform for RNA 3D structure prediction and analysis of RNA folding trajectories based on the SimRNA method. SimRNA employs a coarse-grained model, Monte Carlo sampling and statistical potentials to explore RNA conformational space, optionally guided by spatial restraints. Recognized for its accuracy in RNA 3D structure prediction in RNA-Puzzles and CASP competitions, SimRNA is particularly useful for incorporating restraints based on experimental data. The new server version introduces performance optimizations and extends user control over simulations and the processing of results. It allows the application of various hard and soft restraints, accommodating alternative structures involving canonical and noncanonical base pairs and unpaired residues, while also integrating data from chemical probing methods. Enhanced features include an improved analysis of folding trajectories, offering advanced clustering options and multiple analyses of the generated trajectories. These updates provide comprehensive tools for detailed RNA structure analysis. SimRNAweb v2.0 significantly broadens the scope of RNA modeling, emphasizing flexibility and user-defined parameter control. The web server is available at https://genesilico.pl/SimRNAweb.

Sustainable and efficient scouring/bleaching strategy for cotton fabric using sucrose octaacetate/peroxide activated system: Mechanistic insights, performance optimizations, and environmental benefits

The activator/H2O2 system is able to achieve a low-temperature and near-neutral bleaching by producing of peroxyacids through activators' perhydrolysis. In order to develop a sustainable and efficient pre-treatment process, sucrose octaacetate (SOA) as a biomass-derived activator was initially introduced in the one-step scouring and bleaching for cotton fabric. Despite the documentation of various activators, limited researches focus on the perhydrolysis pathway and bleaching mechanism of bio-based activators. Consequently, the performances of SOA/H2O2 system were investigated by testing the color features, hydrophilicity, and tensile property of the treated fabric. Furthermore, density functional theory (DFT) and liquid chromatography-mass spectrometry (LC-MS) were employed to clarify the perhydrolysis pathway of SOA. Afterwards, a central composite design (CCD) experiment and mathematical modeling was conducted to optimize the bleaching process and analyse the factors significantly influencing the whiteness index (WI) of fabrics. Finally, the energy and water consumption as well as the life cycle assessment (LCA) of SOA/H2O2 bleaching was discussed. Results show that the addition of SOA further increased the fabric's WI from 74.96 to 78.78. The fabric's hydrophilicity enhanced distinctly after removing cotton wax, as confirmed by X-ray photoelectron spectrometer analysis. The bleached fabric exhibited less than 5% mechanical loss and undamaged morphology owing to the moderate SOA/H2O2 system. DFT and LC-MS analyses revealed that three acyl carbon atoms with higher atomic charge in SOA preferentially underwent perhydrolysis, resulting in the release of equal chemical equivalents of peracetic acid as the primary bleaching substance. Besides, the important role of HO· in SOA/H2O2 bleaching was confirmed through fluorescent labeling and radical trapping experiments. The most significant factor affecting WI was temperature, followed by H2O2 concentration, NaHCO3 concentration, and time. Implementing the SOA/H2O2 system led to approximately 35.5% energy savings, 36.4% water savings, and reduced environmental impact by the LCA. Considering the overall evaluation of CCD's outcome, fabric characteristics, and environmental benefits, the WI of 80.64 which is 93% higher than greige fabric, can be achieved when utilizing the optimized parameters (82.73 mmol/L H2O2, 64.14 mmol/L NaHCO3, a temperature of 70 °C, and a duration of 60 min).

Performance optimization for a scintillating glass electromagnetic calorimeter at the EIC

The successful realization of the EIC scientific program requires the design and construction of high-performance particle detectors. Recent developments in the field of scientific computing and increased availability of high performance computing resources have made it possible to perform optimization of multi-parameter designs, even when the latter require longer computational times (for example simulations of particle interactions with matter). Procedures involving machine-assisted techniques used to inform the design decision have seen a considerable growth in popularity among the EIC detector community. Having already been realized for tracking and RICH PID detectors, it has a potential application in calorimetry designs. A SciGlass barrel calorimeter originally designed for EIC Detector-1 has a semi-projective geometry that allows for non-trivial performance gains, but also poses special challenges in the way of effective exploration of the design space while satisfying the available space and the cell dimension constraints together with the full detector acceptance requirement. This talk will cover specific approaches taken to perform this detector design optimization.

Techno-economic and environmental investigation between a low-concentrating photovoltaic thermal collector and a regular photovoltaic flat plate thermal collector

Photovoltaic/thermal (PV/T) systems are highly efficient due to their simultaneous generation of heat and power. To achieve better efficiency of these systems, optimization in design and performance is required. In this research, a thermal flat collector coated with nanoparticles and equipped with concentrators was employed. Flat mirrors were used to reflect more solar radiation and a black graphene nanoparticle coating on the absorber was employed. The primary aim of using this coating is to increase the efficiency of thermal energy production enhancing heat transfer to the harp channels. The harp water channels, welded on the rear side of the absorber, were designed to efficiently pass water flow and absorb heat. To illustrate the performance of this system, it was compared to a regular PV system. A comparison indicated that the low-concentrating PV/T (LCPV/T) system had 41.01% percent more total efficiency than a regular PV/T system. In addition, a 71.90% improvement in the Nusselt number was observed. In this study, both systems were optimized via the Taguchi method in the Minitab software. Moreover, the LCPV/T system showed a 39.15% reduction in emissions and a 2.72-year shorter energy payback time (EPBT) due to the use of effective approaches. The present research involved an experimental performance and optimization comparison between PV/T and LCPV/T systems under constant flow rate conditions at various times of the day.

Can GPU performance increase faster than the code error rate?

Graphics processing units (GPUs) are the reference architecture to accelerate high-performance computing applications and the training/interference of convolutional neural networks. For both these domains, performance and reliability are two of the main constraints. It is believed that the only way to increase reliability is to sacrifice performance, e.g., using redundancies. We show in this paper that this is not always the case. As a very promising result, we found that most GPUs performance improvements also bring the benefit of increasing the number of executions correctly completed before experiencing a silent data corruption (SDC). We consider four different common GPUs’ performance optimizations: architectural solutions, software implementations, compiler optimizations, and threads degree of parallelism. We compare different implementations of a variety of parallel codes and, through beam experiments and applications profiling, we show that the performance improvement typically (but not necessarily) increases the GPU SDC rate. Nevertheless, for the vast majority of the configurations the performance gain is much higher than the SDC rate increase, allowing to process a higher amount of correct data. As we show, the programmer choices can increase up to 25×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$25\ imes {}$$\\end{document} the number of correctly completed executions without redesigning the algorithm nor including specific hardening solutions.

SeqKit2: A Swiss army knife for sequence and alignment processing.

In the era of ubiquitous high-throughput sequencing studies, there is a growing need for analysis tools that are not just performant but also comprehensive and user-friendly enough to cater to both novice and advanced users. This article introduces SeqKit2, the next iteration of the widely used sequence analysis tool SeqKit, featuring expanded functionality, performance optimizations, and support for additional compression methods. Retaining a pragmatic subcommand architecture, SeqKit2 represents substantial enhancement through the inclusion of 19 additional subcommands, expanding its overall repertoire to a total of 38 in eight categories. The new subcommands add functionality such as amplicon processing and robust, error-tolerant parsing of sequence records. In addition, three subcommands designed for real-time analysis are added for periodic monitoring of properties of FASTQ and Binary Alignment/Map alignment records and real-time streaming from multiple sequence files. The performance of SeqKit2 is benchmarked against the old version of SeqKit, Bioawk, Seqtk, and SeqFu tools. SeqKit2 consistently outperforms its predecessor, albeit with marginally higher memory usage, while maintaining competitive runtimes against other tools. With its broad functionality, proven usability, and ongoing development driven by user feedback, we hope that bioinformaticians will find SeqKit2 useful as a "Swiss army knife" of sequence and alignment processing-equally adept at facilitating ad hoc analyses and seamlessly integrating into larger pipelines.

Status Quo of Multi-Objective Design Optimization of Kinetic Facades: A Quantitative Review

Kinetic facades provide numerous advantages, such as improving the energy efficiency in buildings, effective control of daylight and natural ventilation, and the assurance of human comfort within indoor spaces. Either in the process of designing or programming the active control systems for kinetic facades, addressing the complicated nature of indoor climate necessitates advanced models that aid in determining optimal operations. However, the status quo of multi-objective (MOO) optimization in kinetic facade performance remains largely unexplored in the existing literature. This study delves into the trends in MOO methods and their broad applications through a scientific mapping and quantitative review process. The goal is to investigate the kinetic facade designers' contributions to expanding the application of computational MOOs. The findings indicate that researchers focused on kinetic facades have played a limited role in extending the MOO applications. This review paper is significant as it explores a less-explored facet of knowledge related to building design optimization, aiming to inspire researchers to stay abreast of evolving trends and integrate recent computational innovations into their facade designs.

Low carbon emission renovation of historical residential buildings

The urgency of renovating residential buildings in heritage conservation areas while addressing carbon emission reduction and energy performance has become a concern in the context of climate change. European directives stress the importance of enhancing existing buildings to achieve climate neutrality by 2050. An approximately 100-year-old wooden apartment building in the heritage conservation area in Võru, Estonia, was designed as a nearly zero-energy building (nZEB). This study examined the impact of different renovation scenarios, considering both embodied and operational carbon emissions. During the study, the technical requirements of the National Heritage Board were developed in several aspects. The results of this study indicate that it is possible to renovate buildings in heritage conservation areas and achieve great energy performance. In addition to improving energy performance, it is possible to make the restoration of a significantly damaged exterior with intermediate repairs more cost-effective for the owner. Deep renovation of a historical wooden building decreases its carbon footprint as it leads to more energy-efficient renovation solutions. This is because operational emissions dominate over embodied emissions in a cold climate. From an environmental perspective, it is more feasible to renovate historically valuable buildings more deeply than it is to upgrade the external appearance of buildings. Renovation work to achieve an nZEB building should include insulating the thermal envelope while restoring the historical appearance and preserving the original building profile. In conclusion, this study offers a comprehensive analysis of various scenarios, including carbon footprint, energy performance, and cost optimality, related to different renovation strategies for buildings located in heritage conservation areas. There is a need to align heritage board technical requirements with the environmental perspective. Given that major renovation is more cost-effective and better at preserving the exterior appearance compared to staged renovation, the former should be preferred to ensure the owner's willingness to pay.