Variable Demand Research Articles

Task-relevant stimulus design improves P300-based brain-computer interfaces.

In the pursuit of refining P300-based brain-computer interfaces (BCIs), our research aims to propose a novel stimulus design focused on selective attention and task relevance to address the challenges of P300-based BCIs, including the necessity of repetitive stimulus presentations, accuracy improvement, user variability, and calibration demands. In the oddball task for P300-based BCIs, we develop a stimulus design involving task-relevant dynamic stimuli implemented as finger-tapping to enhance the elicitation and consistency of event-related potentials (ERPs). We further improve the performance of P300-based BCIs by optimizing ERP feature extraction and classification in offline analyses. With the proposed stimulus design, online P300-based BCIs in 37 healthy participants achieve an accuracy of 91.2% and an information transfer rate (ITR) of 28.37 bits/min with two stimulus repetitions. With optimized computational modeling in BCIs, our offline analyses reveal the possibility of single-trial execution, showcasing an accuracy of 91.7% and an ITR of 59.92 bits/min. Furthermore, our exploration into the feasibility of across-subject zero-calibration BCIs through offline analyses, where a BCI built on a dataset of 36 participants is directly applied to a left-out participant with no calibration, yields an accuracy of 94.23% and the ITR of 31.56 bits/min with two stimulus repetitions and the accuracy of 87.75% and the ITR of 52.61 bits/min with single-trial execution. When using the finger-tapping stimulus, the variability in performance among participants is the lowest, and a greater increase in performance is observed especially for those showing lower performance using the conventional color-changing stimulus. Signficance. Using a novel task-relevant dynamic stimulus design, this study achieves one of the highest levels of P300-based BCI performance to date. This underscores the importance of coupling stimulus paradigms with computational methods for improving P300-based BCIs.

Cooling Energy Challenges in Residential Buildings During Heat Waves: Urban Heat Island Impacts in a Hot-Humid City

Ignoring Urban Heat Island (UHI) effects may lead to an underestimation of the building cooling demand. This study investigates the impact of the UHI on the cooling demand in hot-humid cities, employing the Local Climate Zones (LCZs) classification framework combined with the Urban Weather Generator (UWG) model to simulate UHI effects and improve building performance simulations. The primary aim of this research is to quantify the influence of different LCZs within urban environments on variations in the cooling energy demand, particularly during heat waves, and to explore how these effects can be incorporated into building energy models. The findings reveal significant discrepancies in both the average and peak cooling demand when UHI effects are ignored, especially during nighttime. The most intense UHI effect was observed in LCZ 2.1, characterized by compact mid-rise and high-rise buildings, leading to a cooling demand increase of more than 20% compared to suburban data during the heat waves. Additionally, building envelope thermal performance was found to influence cooling demand variability, with improved thermal properties reducing energy consumption and stabilizing demand. This research contributes to the theoretical understanding of how urban microclimates affect building energy consumption by integrating LCZ classification with UHI simulation, offering a more accurate approach for building energy predictions. Practically, it highlights the importance of incorporating LCZs into building energy simulations and provides a framework that can be adapted to cities with different climatic conditions, urban forms, and development patterns. This methodology can be generalized to regions other than hot-humid areas, offering insights for improving energy efficiency, mitigating UHI effects, and guiding urban planning strategies to reduce the building energy demand in diverse environments.

Integrating Energy Generation and Demand in the Design and Operation Optimization of Energy Communities

The optimization of the energy system serving users’ aggregations at urban level, such as Energy Communities, is commonly addressed by optimizing separately the set of energy conversion and storage systems from the scheduling of energy demand. Conversely, this paper proposes an integrated approach to include the demand side in the design and operation optimization of the energy system of an Energy Community. The goal is to evaluate the economic, energetic, and environmental benefits when users with different demands are aggregated, and different degrees of flexibility of their electricity demand are considered. The optimization is based on a Mixed-Integer Linear Programming approach and is solved multiple times by varying (i) the share of each type of user (residential, commercial, and office), (ii) the allowed variation of the hourly electricity demand, and (iii) the maximum permitted CO2 emissions. Results show that an hourly flexibility of up to 50% in electricity demand reduces the overall system cost and the amount of energy withdrawn from the grid by up to 25% and 31%, respectively, compared to a non-flexible system. Moreover, the aggregation of users whose demands match well with electricity generation from renewable sources can reduce CO2 emissions by up to 30%.

Technical Note—Revenue Management with Calendar-Aware and Dependent Demands: Asymptotically Tight Fluid Approximations

Revenue Management with Dependent Demands In the revenue management literature, a standard approach to model the demand is based on dividing the selling horizon into a number of small time periods such that there is at most one customer arrival at each time period and that the customer arrivals at different time periods are independent of each other. Under this demand model, if the mean demand for a product increases with rate T, then the standard deviation of the demand must increase with rate square root of T. In other words, large demand volume and large demand variability cannot coexist. In “Technical Note—Revenue Management with Calendar-Aware and Dependent Demands: Asymptotically Tight Fluid Approximations,” Li, Rusmevichientong, and Topaloglu consider demand models that can simultaneously incorporate large demand volume and large demand variability while also allowing dependence between the demands over nonoverlapping time intervals. Under such demand models, they give efficiently computable policies that admit performance guarantees.

Designing a sector-coupled European energy system robust to 60 years of historical weather data

As energy systems transform to rely on renewable energy and electrification to mitigate climate change, they encounter stronger year-to-year variability in energy supply and demand. Yet, most infrastructure planning relies on a single weather year, risking a potential lack of robustness. In this paper, we optimize capacity layouts for a European energy system under net-zero CO2 emissions for 62 different weather years. Subsequently, we fix the layouts and optimize their operation in every other weather year to assess resource adequacy and CO2 emissions. Our analysis shows a variation of ± 10% in total system costs across weather years. Layouts designed for years with compound weather events prove more robust, achieving resource adequacy of 99.9% and net-negative CO2 emissions of −0.5% per year relative to 1990 levels. CO2-emitting backup generation regulated by a CO2 tax offers a cost-effective measure to enhance robustness. It increases emissions only marginally, keeping average emissions below 1% of 1990 levels over all layouts. Our findings underscore the need for policymakers and energy stakeholders to account for interannual weather variability in future infrastructure planning.

ENERGY MANAGEMENT IN HYBRID PV-WIND-BATTERY STORAGE-BASED MICROGRID USING MONTE CARLO OPTIMIZATION TECHNIQUE

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using three types of Monte Carlo simulation techniques. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing the Monte Carlo technique to extract maximum energy profit using the hybrid microgrid. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.

Optimum pricing decision for the retailer with warranty and price-dependent variable demand

Buying inventory on a credit basis is an effective pricing plan, and trade credit is a popular component of market transactions that increase demand. In a dynamic situation, most businesses offer different rewards and services to their consumers under certain terms and conditions during commodity sales. The accompanying companies provide a warranty period facility to increase consumer demand for products. The holding cost often increases over time. This study determines a production model wherein the production rate is proportionate to the price-warranty period based on the demand rate with time-dependent holding cost and trade credit policy. This work leads to three vital conventions: (I) the rate of product demand is considered to be price-warranty period dependent; (II) the non-linear function is considered for the rate of replacement failure, wherein the capital of the manufacturer depends on the warranty period; and (III) the rate of inflation is constant; moreover, the present time value of money also measured. This study aims to find the selling price, cycle time, and warranty period by using an algorithm to optimize the total profit function of the manufacturer. The results are validated by solving three numerical examples with their graphical representation based on different situations and the major parameters, and sensitivity analysis is analyzed with important decision-making implications.

Predicting Australian energy demand variability using weather data and machine learning

Abstract Managing energy systems requires understanding the variability of energy demand, which on daily timescales is driven primarily by the weather. Historical records of demand typically cover 1-2 decades which may be too short to capture the range of possible demand, particularly for a climate with high interannual variability such as that of Australia. Predicting demand using long records of weather data opens the possibility of more robustly estimating true demand variability. We estimate daily energy demand between 2010 and 2019 for Australian states in the National Electricity Market using machine learning with reanalysis weather variables as predictors. We assess the performance of these models and examine their behaviour to identify which weather variables are most important for predicting demand. We then use the models to estimate demand for the period 1959-2022. We use this 63-year record to quantify how the probability of high demand days can change compared to individual 10-year periods and when conditioned by the phase of the El Niño Southern Oscillation (ENSO). Energy demand can be accurately predicted with weather, with median errors of 2-4% on years omitted from the training. We show that the probability of extreme demand over different 10-year periods can vary from half to twice as likely, depending on the decade. When further conditioned on ENSO phase, the probabilities can be up to 7 times higher than when using the 63-year period, implying a risk of overestimating weather-related energy demand if shorter records are used. We conclude that machine learning methods can accurately predict energy demand using only weather data, enabling us to estimate demand variability over longer time horizons than is possible with demand observations. These longer records are important when attempting to quantify tail risks of demand, and so can help to inform the design of energy systems.

EU geographical islands as leaders of green energy transition

This paper reviews how European islands are taking the lead in the European Union (EU) Clean Energy Transition by reviewing the lessons learned in the EU Bridge initiative and in a number of EU co-funded projects such as NESOI, RE-EMPOWERED, REACT, IANOS, LOCALRES, MASTERPIECE, SINNOGENES, SMHYLES, STEPWISE, and ISLET. Islands encounter significant difficulties in the management of their energy systems, including strong seasonal variations in energy demand, high operational costs and GHG emissions for energy production, weak energy grids, lack of technical skills, and difficult access to finance. However, they also have positive features that make them ideal laboratories for energy transition, including high potential for renewables, small-scale and strong community structures, and high energy prices, which make most solutions cost-effective. Each of the projects contributing to the paper has been supporting the islands’ energy transition, leveraging different enabling technologies, such as renewable energy production systems, smart grids, advanced energy storage systems, and local energy community schemes. The results from these projects underline the need for tailored energy planning, considering geographical and socio-economic particularities, the need to engage the local population in the definition of the most suitable decarbonization pathways for the island, and a number of lessons learned on the technologies that have the highest potential for being tested on islands and then being replicated on the mainland. Therefore, this study concludes that renewable energy solutions coupled with different technologies (storage, mobility, district heating/cooling, etc.) and leveraging powerful community integration confirm that European islands can drive the decarbonization strategy of the EU.

Enhancing supply chain resilience with recovery strategies under electricity shortages

Electricity shortages triggered by extreme events have profoundly disrupted supply chains (SCs) in recent years. As SC operations heavily depend on electricity, simultaneous uncertainties may occur in supply and production capacities, demand, and transportation capacity if electric trucks are used. To enhance SC resilience, it is imperative to combine new recovery strategies (i.e. alternative energy sources and alternative transportation vehicles (ATVs)) with traditional ones to restore SC performance. However, these strategies have not been investigated quantitatively. Besides, only partial probability distribution information of uncertainties may be available under electricity shortages. Thus, this work improves SC resilience from a worst-case perspective, where the moment information is known. For the problem, a bi-objective distributionally robust chance-constrained optimisation model is constructed to balance the total cost and the service level. Then, an effective ϵ-model-based constructive heuristic (ϵ-MBCH) is developed. Compared with the ϵ-constraint method, the efficiency of ϵ-MBCH is improved by 99.81%. Based on stress tests and sensitivity analyses on a real case, key managerial insights include: (i) the ATV strategy is indispensable under varying severities of electricity shortages; (ii) the inventory management strategy helps reduce reliance on power generators and diesel trucks; and (iii) mitigating demand variability is essential for reducing costs.

Enhancing supply chain resilience through artificial intelligence: Analyzing problem-solving approaches in logistics management

In an increasingly complex and unpredictable global economy, supply chain resilience has emerged as a critical priority for organizations striving to adapt to disruptions and maintain operational continuity. Artificial intelligence (AI) has proven to be a transformative force in addressing the challenges of logistics and supply chain management, offering innovative solutions for forecasting, decision-making, and operational efficiency. This research explores how AI-driven approaches can solve specific supply chain challenges, including demand variability, disruption management, inventory optimization, transportation inefficiencies, and supplier relationship management. The review proposes a comprehensive analysis of AI technologies such as predictive analytics, real-time monitoring systems, optimization algorithms, and autonomous systems, highlighting their contributions to enhancing supply chain resilience. By leveraging AI, organizations can improve their ability to predict disruptions, optimize resource allocation, and respond dynamically to real-time challenges. The research incorporates case studies and real-world applications to illustrate successful implementations of AI in logistics, as well as lessons learned from failed attempts. Additionally, the review examines the benefits of integrating AI into supply chain operations, such as improved risk management, enhanced decision-making, and increased adaptability to uncertainties. However, it also addresses the challenges of AI adoption, including high implementation costs, data privacy concerns, and organizational resistance to change. By presenting actionable insights and recommendations, this research aims to provide a roadmap for organizations seeking to harness AI to strengthen their supply chain resilience. The findings underscore the transformative potential of AI technologies in fostering robust, adaptive, and efficient supply chain systems capable of withstanding future uncertainties and disruptions. Keywords: Supply Chain, Artificial Intelligence, Logistics Management, Review.

Optimal reconfiguration of a distribution network integrated photovoltaic and wind systems using blood-sucking leech optimizer

This paper presents a planning strategy for integrating renewable distributed generation (DG) units into a distribution network, incorporating network reconfiguration to enhance the network's technical, economic, and environmental performance. Utilizing a novel meta-heuristic algorithm, the Blood-Sucking Leech Optimizer (BSLO), the study addresses a multi-objective optimization problem aimed at determining the optimal placement and sizing of DG units, as well as the most effective network topology. This approach seeks to minimize active power losses, improve voltage profiles, reduce installation costs, and lower greenhouse gas emissions. The model accounts for variable load demands, climatic factors (such as ambient temperature, solar irradiation, and wind speed), and fluctuating energy prices, reflecting realistic operating conditions. Tested on the IEEE 69-bus distribution network, the BSLO algorithm demonstrated rapid convergence to the global optimum by effectively balancing exploration and exploitation phases. Compared to other meta-heuristic methods, such as the Grey Wolf Optimizer, Gorilla Troops Optimizer, Walrus Optimization Algorithm, and Artificial Hummingbird Algorithm, the BSLO consistently achieved superior accuracy and faster convergence, resulting in higher precision and optimization efficiency. The optimal deployment of two PV generators and two wind turbines, combined with selective line switch openings, resulted in an 87.66% reduction in active power losses, a 73.30% decrease in voltage deviation, a 51.91% reduction in overall system costs, and a 62.74% decrease in greenhouse gas emissions compared to the base case.

Rethinking the Pace of Productivity in Pharmacy Academia.

In response to rising levels of burnout and stress among pharmacy faculty, there is a growing call to reassess traditional approaches to productivity and well-being within the academy. We introduce a "slower" approach to faculty work one that advocates for a deliberate focus on quality over quantity, promoting sustainable practices that prioritize meaningful contributions and personal well-being. The aim of this commentary is to encourage pharmacy faculty to embrace a slow mindset while maintaining the ability to contribute meaningfully to the lives of their students, patients, colleagues, and the profession of pharmacy. Drawing from Cal Newport's book Slow Productivity, principles of Obsessing Over Quality, Doing Fewer Things, and Working at a Natural Pace are explained. Practical strategies are discussed, including reevaluating workload distribution, setting realistic project limits, and embracing seasonal variations in academic demands. These approaches aim to foster a supportive and balanced organizational culture within pharmacy academia, aligning individual aspirations with institutional goals. This commentary encourages pharmacy faculty to embrace a slower, more intentional approach to their work, promoting both personal and professional fulfillment.

Towards sustainable agricultural water management in Poland – How to meet water demand for supplemental irrigation?

The global challenge of water scarcity, particularly in agriculture, demands urgent attention due to the overexploitation of water resources and the escalating impacts of climate change. This study focuses on the unique challenges faced by Poland, experiencing increasing concerns related to droughts. It explores the utilization of supplemental irrigation, specifically in the context of Central Europe, where a distinctive approach known as supplemental irrigation is employed. The study emphasizes the need for sustainable water management practices and investigates the potential of small water retention measures (SWRMs), such as ponds and drainage water management, as solutions to enhance water availability in agriculture. A macro-scale water balance study is conducted using the Soil and Water Assessment Tool (SWAT) to estimate spatio-temporal variability of water demand for supplemental irrigation in Poland. The highest demand, approximately 2.5 billion m3 (for arable lands) and 1.3 billion m3 (for grasslands), occurred during the exceptionally dry year of 2015, characterized by severe agricultural drought effects. The study also assesses the efficiency of SWRMs in meeting irrigation demands at national level. The results highlight a paradox in their effectiveness during critical periods, specifically in dry years when water demands are the highest. The outcomes of the model experiment underscored concerns about the insufficiency of meeting the water needs of irrigated agriculture solely through the construction of small retention facilities during very dry years. The outcomes of this research contribute to a better understanding of irrigation water demands in temperate climate region, support evidence-based practices for sustainable water management, and inform policymakers and stakeholders involved in water governance.

Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user’s charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G’s high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user’s range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.

OptiPower AI: A deep reinforcement learning framework for intelligent cluster energy management and V2X optimization in industrial applications

Peer-to-peer (P2P) energy trading has emerged as a practical solution for efficient energy management, particularly with the rising affordability of renewable energy and electric vehicles (EVs). This paper introduces the Optimal Power Artificial Intelligence (OptiPower AI) algorithm, a significant advancement in Intelligent Cluster Energy Management (ICEM). OptiPower AI combines Double Deep Q-Network (DDQN)-based Deep Reinforcement Learning (DRL), Vehicle-to-Everything (V2X) technology, and P2P energy trading to optimize energy distribution among clusters of prosumers and consumers. The system efficiently manages Renewable Energy Sources (RES) and EVs, achieving a 19.18% reduction in energy costs and a 50.02% decrease in average energy prices across V2X and P2P scenarios.OptiPower AI uses DRL to dynamically allocate energy and implement real-time pricing, enhancing energy efficiency and user satisfaction. Simulations based on meteorological data from Tunisia validate the system’s ability to improve thermal comfort, increase energy savings, and lower costs. The model’s parameters enable accurate forecasting and allocation, showcasing OptiPower AI’s reliability in variable demand conditions. This work advances the application of DRL in decentralized, sustainable P2P energy management systems for industrial clusters, addressing critical challenges in energy distribution, efficiency, and cost reduction.

An assessment of Fuzzy n-Policy queues with infinite capacity

This study examines the application of fuzzy n-policy queues with infinite capacity to optimize queuing systems under uncertain and variable conditions. Traditional queuing models often assume precise data, but real-world scenarios frequently involve ambiguity in parameters such as arrival rates, service rates, and system thresholds. The fuzzy n-policy model offers a more flexible approach by allowing these parameters to be represented as fuzzy variables, capturing the inherent uncertainty in complex queuing environments. By analyzing infinite capacity queues within this framework, the study evaluates system performance metrics like expected queue length, waiting time, and server utilization under different fuzzy set assumptions. The results demonstrate that the fuzzy nnn-policy enhances system responsiveness by adjusting service initiation thresholds based on the degree of uncertainty in arrival and service processes. These findings contribute to the broader field of stochastic and fuzzy queuing systems, providing practical insights for industries where demand variability and service uncertainty are prevalent, such as telecommunications, healthcare, and logistics.

Zero-inventory plans, constant workforce, or hybrid approach? Analysing pure production strategies for enhancing factory resilience with demand variability

Historically the notion of resilience capability has primarily been conceptualised as a holistic construct in the domain of supply chain networks – referred to as SCRES. The field of SCRES has since evolved gaining prominence among scholars and practitioners. However, the existing SCRES literature inadequately delves into the embedded resilience harboured by their constituent components, practices, or internal routines, including the aggregate production planning (APP) process. Therefore, the purpose of this article is to examine the interplay between medium-term pure strategies for APP and the resilience of manufacturing facilities, assuming demand to be the sole source of uncertainty. To accomplish this, a realistic Monte-Carlo simulation model integrated with a robust heuristic procedure for pure strategies is merged with a factory resilience index-based Cobb–Douglas function. The results of this research suggest that manufacturing facilities achieve a higher level of resilience when certain ‘zero-inventory plans’ are implemented. Based on this key finding, production/demand planners might incorporate the resilience dimension alongside the customary manufacturing success factors. Thus, to the best of the authors’ knowledge, this study represents the first attempt to establish a clear linkage between the implementation of pure APP strategies and a quantitative measure of factory resilience.

Establishing best practices for modeling multi-day energy storage in deeply decarbonized energy systems

Abstract Multi-day energy storage (MDS, a subset of long-duration energy storage or LDES) may become a critical technology for the decarbonization of the power sector, as current commercially available Li-ion battery storage technologies cannot cost-effectively shift energy to address multi-day or seasonal variability in demand and renewable energy availability. MDS is difficult to model in existing energy system planning models (such as electricity system capacity expansion models), as it is much more dependent on an accurate representation of chronology than other resources. Techniques exist for modeling MDS in these planning models; however, it is not known how spatial and temporal resolution affect the performance of these techniques, creating a research gap. In this study we examine what spatial and temporal resolution is necessary to accurately capture the full value of MDS, in the context of a continent-scale capacity expansion model. We use the results to draw conclusions and present best practices for modelers seeking to accurately model MDS in a macro-energy systems planning context. Our key findings are: 1) modeling MDS with linked representative periods is crucial to capturing its full value, 2) MDS value is highly sensitive to the cost and availability of other resources, and 3) temporal resolution is more important than spatial resolution for capturing the full value of MDS, although how much temporal resolution is needed will depend on the specific model context.

Energetic Analysis of Passive Solar Strategies for Residential Buildings with Extreme Summer Conditions

This study investigates the implementation of passive design strategies to improve the thermal environment in the extremely hot climates of Brazil, Portugal, and Turkey. Given the rising cooling demands due to climate change, optimizing energy efficiency in buildings is essential. Using the Trace 3D Plus v6.00.106 software, typical residential buildings for each country were simulated to assess various passive solutions, such as building orientation, wall and roof modifications, glazing optimization options, window-to-wall ratio (WTWR) reduction, shading, and natural ventilation. The findings highlight that Brazil experienced the higher discomfort temperatures compared to Mediterranean climates, with indoor air temperatures exceeding 28 ºC all year round and remaining between 34 ºC and 37 ºC for nearly 40% of the time. Building orientation had a minimal impact near the equator, while Mediterranean climates benefited from an up to 10% variation in energy demand. Thermal insulation combined with white exterior paint resulted in Şanlıurfa experiencing annual energy savings of up to 26%. Optimal roof solutions yielded a 19% demand reduction in Évora, while WTWR reduction and double-colored glazing achieved up to a 35% reduction in Évora and 19% in other regions. Combined strategies achieved energy demand reductions of 44% for Évora, 40% for Şanlıurfa, and 32% for Teresina. The study emphasizes the need for integrated, climate-specific passive solutions, showing their potential to enhance both energy efficiency and the thermal environment in residential buildings across diverse hot climates.