Pareto Front Of Solutions Research Articles

Planning of electric vehicle charging stations: An integrated deep learning and queueing theory approach

This study presents a hybrid solution for the charging station location-capacity problem. The proposed approach simultaneously determines the location and capacity of charging stations (i.e., number of charging piles), and assigns piles to electric vehicles based on their level of charge. The problem is formulated as a bi-objective mixed-integer nonlinear programming model to minimize the total cost of establishing charging stations together with the average customers’ waiting time. The proposed solution combines queueing theory with mathematical modelling to estimate the average waiting time. A deep learning algorithm is then developed to enhance the precision of waiting time estimation. Another contribution is involving a deep neural network model in improving NSGA-II algorithm. Numerical experiments are conducted in Halifax, Canada to assess the performance of the proposed framework. The results demonstrate the strong predictive performance of the deep learning algorithm and highlight the limitations of traditional queueing models in estimating waiting times in charging stations (i.e., 99.8% improvement in computation time, as well as accuracy improvement of time estimations from 13% to 1.6% deviation). Several valuable insights are obtained to improve the operational performance of charging stations such as achieving a significant (i.e., 61.5%) drop in the average waiting time across the network by a modest (i.e., 29.2%) increase in the initial investments. Also, it reveals that the variability of service rate significantly impacts the average waiting time (i.e., a 50% increase in the variability of service rate causes a substantial 950.56% surge in the average waiting time). The findings underscore the need to control service rate fluctuations to reduce wait times and boost driver satisfaction. The improved NSGA-II algorithm shows 12.77% improvement in the Pareto front solutions. Finally, the proposed prioritization strategy based on the charging level of vehicles could reduce the average waiting time and cost compared to the first-come-first-served strategy.

A Resilient Approach to a Test Rig Setup in the Qualification of a Tilt Rotor Carbon Fiber-Reinforced Polymer (CFRP) Wing

The evolution of aircraft wing development has seen significant progress since the early days of aviation, with static testing emerging as a crucial aspect for ensuring safety and reliability. This study focused specifically on the engineering phase of static testing for the Clean Sky 2 T-WING project, which is dedicated to testing the innovative composite wing of the Next-Generation Civil Tiltrotor Technology Demonstrator. During the design phase, critical load cases were identified through shear force/bending moment (SFBM) and failure mode analyses. To qualify the wing, an engineering team designed a dedicated test rig equipped with hydraulic jacks to mirror the SFBM diagrams. Adhering to specifications and geometric constraints due to several factors, the jacks aimed to minimize the errors (within 5%) in replicating the diagrams. An effective algorithm, spanning five phases, was employed to pinpoint the optimal configuration. This involved analyzing significant components, conducting least square linear optimizations, selecting solutions that met the directional constraints, analyzing the Pareto front solutions, and evaluating the external jack forces. The outcome was a test rig setup with a viable set of hydraulic jack forces, achieving precise SFBM replication on the wing with minimal jacks and overall applied forces.

A multiobjective approach for weekly Green Home Health Care routing and scheduling problem with care continuity and synchronized services

Home Health Care (HHC) services are essential for delivering healthcare programs to patients in their homes, with the goal of reducing hospitalization rates and improving patients’ quality of life. However, HHC organizations face significant challenges in scheduling and routing caregivers for home care visits due to complex criteria and constraints. This paper addresses these challenges by considering both caregiver assignments and transportation logistics. The objective is to minimize the total travel distance and CO2 emissions while ensuring a balanced workload for caregivers, meeting patients’ preferences, synchronization, precedence, and availability constraints. To tackle this problem, we propose a multiperiodic Green Home Health Care (GHHC) framework. In the first stage, we utilize multiobjective programming and the NSGA-II algorithm to generate Pareto front solutions that consider travel distance and CO2 emissions. In the second stage, a Mixed-Integer Linear Programming (MILP) model is proposed to balance caregivers’ workload by assigning them to the patient routes generated in the first stage. The results highlight the trade-off between shorter routes and lower emissions. Furthermore, we examine the impact of prioritizing continuity of care and patient satisfaction. This research provides valuable insights into addressing the scheduling and routing challenges in HHC services, contributing to a more efficient and environmentally friendly healthcare delivery.

A novel feature selection method based on quantum support vector machine

Feature selection is critical in machine learning to reduce dimensionality and improve model accuracy and efficiency. The exponential growth in feature space dimensionality for modern datasets directly results in ambiguous samples and redundant features, which can severely degrade classification accuracy. Quantum machine learning offers potential advantages for addressing this challenge. In this paper, we propose a novel method, quantum support vector machine feature selection (QSVMF), integrating quantum support vector machines with multi-objective genetic algorithm. QSVMF optimizes multiple simultaneous objectives: maximizing classification accuracy, minimizing selected features and quantum circuit costs, and reducing feature covariance. We apply QSVMF for feature selection on a breast cancer dataset, comparing the performance of QSVMF against classical approaches with the selected features. Experimental results show that QSVMF achieves superior performance. Furthermore, the Pareto front solutions of QSVMF enable analysis of accuracy versus feature set size trade-offs, identifying extremely sparse yet accurate feature subsets. We contextualize the biological relevance of the selected features in terms of known breast cancer biomarkers. This work highlights the potential of quantum-based feature selection to enhance machine learning efficiency and performance on complex real-world data.

A metaheuristic particle swarm optimization for enhancing energetic and exergetic performances of hydrogen energy production from plastic waste gasification

In recent decades, there has been a surge in demand for the development of renewable energies, leading to extensive research efforts focused on the gasification process. Consequently, a wide range of studies has been conducted to explore different feedstocks for gasification, with particular emphasis on plastic waste. Although a wide range of studies conducted various optimization methods for the gasification process, there was no specific study to employ metaheuristic methods in the field of plastic waste gasification. This study aimed to perform a multi-objective particle swarm optimization (MOPSO) method to solve a multi-objective plastic waste gasification optimization problem for enhancing energetic and exergetic viewpoints. Moreover, exact methods including non-linear optimization and response surface methodology (RSM) were conducted to be compared with the results derived from MOPSO. Pareto-front solutions obtained from MOPSO were ranked using grey relational analysis (GRA) and the optimum responses were 517 kJ/kg, 84 %, and 57 % for lower-heating value, cold gas efficiency, and exergy efficiency, respectively, which were compared to optimum options obtained from multi-objective non-linear optimization and RSM techniques. While the MOPSO model may not have performed as well as other methods in this particular problem, it is important to acknowledge its capabilities. Future research can concentrate on improving the behavior and performance of the MOPSO method when dealing with a larger number of points or when applied to different problem domains.

Node search space reduction for optimal placement of pressure sensors in water distribution networks for leakage detection

This study presents a methodological framework for optimal placement of pressure sensors in Water Distribution Networks (WDNs) for leakage monitoring under uncertainty. Monte Carlo simulation is utilized to simulate leakages of different magnitudes at various nodes in the WDN taking into consideration background noise and minimum resolution of pressure sensors. A novel sensor preselection algorithm based on community detection and maximum entropy computation to reduce the search space of the pressure Sensor Placement Problem (SPP) is presented. The pressure SPP is formulated as a multi-objective optimization problem that seeks to maximize Joint Entropy, Coverage, and minimize Total Correlation. NSGA-II is used to solve the SPP and the solutions in the optimal Pareto front are ranked using a hybrid Entropy TOPSIS to eliminate potential bias and subjective human judgement in optimal sensor configuration implementation. The sensor preselection algorithm achieved a 67% reduction in the search space (possible sensor positions) of the case study, C-TOWN WDN, with only 2.78% reduction in coverage. The result of the pressure SPP indicates only 21 pressure sensors are needed to cover 95.45% of the WDN under study. Finally, the overall performance of the proposed methodological framework is presented and compared with other related works.

Narrowband Internet of Things via Low Earth Orbit Satellite Networks: An Efficient Coverage Enhancement Mechanism Based on Stochastic Geometry Approach.

With the development of IoT technology and 5G massive machine-type communication, the 3GPP standardization body considered as viable the integration of Narrowband Internet of Things (NB-IoT) in low Earth orbit (LEO) satellite-based architectures. However, the presence of the LEO satellite channel comes up with new challenges for the NB-IoT random access procedures and coverage enhancement mechanism. In this paper, an Adaptive Coverage Enhancement (ACE) method is proposed to meet the requirement of random access parameter configurations for diverse applications. Based on stochastic geometry theory, an expression of random access channel (RACH) success probability is derived for LEO satellite-based NB-IoT networks. On the basis of a power consumption model of the NB-IoT terminal, a multi-objective optimization problem is formulated to trade-off RACH success probability and power consumption. To solve this multi-objective optimization problem, we employ the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) method to obtain the Pareto-front solution set. According to different application requirements, we also design a random access parameter configuration method to minimize the power consumption under the constraints of RACH success probability requirements. Simulation results show that the maximum number of repetitions and back-off window size have a great influence on the system performance and their value ranges should be set within [4, 18] and [0, 2048]. The power consumption of coverage enhancement with ACE is about 58% lower than that of the 3GPP proposed model. All this research together provides good reference for the scale deployment of NB-IoT in LEO satellite networks.

Enhancing the vertical downward condensation heat transfer of a plate heat exchanger by electrochemical etching

This research employed electrochemical etching to generate microscale roughness on a plate heat exchanger (PHE) thereby enhancing condensation heat transfer in a vertical downward flow. The effects of the mass flux, vapor quality, heat flux, saturation temperature and microscale surface roughness on the condensation heat transfer coefficient (HTC) and frictional pressure drop (FPD) of R-513a flowing through a PHE was experimentally investigated. Experimental measurements were made under various heat fluxes of 5–15 kW/m2, mass fluxes of 60–80 kg/m2s, saturation temperatures of 40–50 °C, and mean vapor qualities of 0.1–0.7. HTC increased by up to 28, 26, 74, and 32.5 % as the heat flux, mass flux, average quality, and microscale surface roughness were respectively increased, but decreased by 19 % with the saturation temperature. FPD increased as the mass flux and average vapor quality increased, whereas it was reduced as the refrigerant saturation temperature increased. In contrast, the heat flux had little to no influence on FPD. Correlations for predicting the Nusselt number and friction factor were postulated by considering influential dimensionless numbers during heat transfer. Maximizing the Nusselt number and thermal performance factor was ultimately resolved using a multi-objective genetic algorithm to find a Pareto front solution. Consequently, the optimal surface roughness was found to be 9.94 μm.

Multi-Objective Robust Optimization of Integrated Energy System with Hydrogen Energy Storage

A novel multi-objective robust optimization model of an integrated energy system with hydrogen storage (HIES) considering source–load uncertainty is proposed to promote the low-carbon economy operation of the integrated energy system of a park. Firstly, the lowest total system cost and carbon emissions are selected as the multi-objective optimization functions. The Pareto front solution set of the objective function is applied by compromise planning, and the optimal solution among them is obtained by the maximum–minimum fuzzy method. Furthermore, the robust optimization (RO) approach is introduced to cope with the source–load uncertainty effectively. Finally, it is demonstrated that the illustrated HIES can significantly reduce the total system cost, carbon emissions, and abandoned wind and solar power. Meanwhile, the effectiveness of the proposed model and solution method is verified by analyzing the influence of multi-objective solutions and a robust coefficient on the Chongli Demonstration Project in Hebei Province.

A multi-objective optimization method for enclosed-space lighting design based on MOPSO

With the rapid development of underground spaces and manned cabins, the design of enclose-space has received attention. Humans working in enclosed-space for a long period of time are prone to fatigue, which in serious cases can lead to human errors and major accidents. Therefore, the optimized design of enclosed-space is of great research significance to enhance the efficiency and comfort of workers. The lighting environment is one of the important objectives of the optimized design of enclosed-space, and the current research on lighting design often focuses on single-objective reduction of energy consumption or improvement of visual effects, and lacks attention to non-visual effects. We propose multi-objective optimal control strategy for enclosed-space lighting systems, taking the visual effects, non-visual effects, and energy efficiency of lighting as the optimization objectives and constraints, combined with multi-objective particle swarm optimization algorithm. Firstly, we obtain a set of optimal Pareto front solution for the dimming level by improving the algorithm. Secondly, to select appropriate lighting strategies according to different working scenarios, we introduce the fuzzy integrated evaluation method of experts to find the best compromise. Finally, we used a manned simulated enclosed cabin based on DIALux evo for a case study to compare the optimization results with baseline solutions. The results show that when we employed the improved dimming strategy, the energy efficiency and visual comfort were significantly improved compared with the baseline lighting design. The proposed method can satisfy human non-visual effects requirements, providing a research reference for future enclosed-space lighting optimization.

A Semidefinite Programming Method Weighted Sum Based for Solving a Multi-area Emission (Environmental) Economic Dispatch Problem

This paper presents a semi-definite programming (SDP) optimisation approach for solving multi area, emission/economic dispatch (MAEED) problems in thermal power systems. The multi-objective problem was transformed into matrix form as an SDP relaxation problem and subsequently solved with a MATLAB programming suite. The system inequality and equality constraints were entered into a Self-Dual Minimisation (SeDuMi) parser. Simulations were tested on four (4) IEEE benchmark models to validate the efficiency of the proposed SDP method. However, in the generation of the Pareto-fr ont solutions, ideal minimum points were used in the determination of the maximum spread out of the Pareto solutions by the algorithm. This involves the use of a standard weighted sum method in generating the Pareto-optimal solution between two objective functions. Twenty-one (21) runs were carried out for the parameter value to explore the effect of control weight selection k1. A comparative study demonstrated the effectiveness of tie-line constraint and without tie-line constraint, respectively on the optimi sation model for the power dispatch problems solved by using the proposed SDP approach. The optimisation results for the minimum fuel cost using the SDP approach for the case when a tie-line constraint is included was US$ 2,151.7 /hr with minimum emission of 3.4687 Ton/hr. This is lower than US$ 2,162.2 /hr with minimum emission of 3.6662 Ton/hr obtained when the tie-line constraint is not included in the optimisation model on IEEE four-area, sixteen network. Additionally, the comparative results with all other methods reported in the literature for different test cases were also presented in this paper. Lower operational costs were obtained for both tie-lines and no tie-lines power transfer cases, showing that the SDP method performed well in accuracy compared to other methods. The SDP method was employed in this research to deal with the multi-area emission economic power dispatch problem due to its inherent advantages in achieving a global optimal solution and, this approach would also assist the power administrator to come up with an improved decision making in attaining an optimum power dispatch of power generation.

Energy-Efficient Flexible Flow Shop Scheduling Under Time-Of-Use Rates with Renewable Energy Sources

In response to climate change, industries strive to curtail energy consumption while maintaining production efficiency. Focused on time-dependent electricity prices, this paper addresses the scheduling challenge in a flexible flow shop machine environment. The goal is to minimize both makespan and total electricity costs in a grid-connected manufacturing setup with battery storage, solar power, and a strategic turn on/turn of machine approach. A multi-objective mixed-integer linear programming model is developed, employing the ϵ-constraint method to reveal the trade-of between objectives and present Pareto-front solutions. The ensuing experimental study allows for the validation of the model and identification of its limits.

A decision support system for autonomous ship trajectory planning

This study presents an approach to enhance human–machine collaboration in autonomous ship trajectory planning. A decision support system is developed, considering crucial environmental factors such as ocean currents, wind, and tidal information, alongside the integration of narrow channel geometry, squat effect, and Under-keel-clearance (UKC). The Dynamic Consequence Analysis (DCA) risk assessment method is utilized to establish dynamic safety domains, incorporating ship maneuvering characteristics and potential failure scenarios. The Multi-objective Particle Swarm Optimization (MOPSO) algorithm is then employed to generate alternative trajectories, optimizing five objective functions: minimizing safety domain violation, consecutive speed changes, path length, deviation from the initial plan, and deviation from the initial estimated time of arrival. Furthermore, two Multi-criteria Decision Making (MCDM) methods, Multi-Objective Optimization by Ratio Analysis (MOORA) with user preferences, and the Entropy Weight Method (EWM) with automatic weight allocation, are employed to rank alternative solutions from the Pareto front. Finally, a clustering method is employed on the Pareto front solutions. The outcomes that serve as representatives for these clusters are then merged with the highest-rated alternative solutions from MCDM methods. This combined set forms the basis for the final decision-making process carried out by the operator. While dynamic obstacles are not considered in this study, evaluation across three scenarios demonstrates the effectiveness of the proposed decision support system for trajectory planning.

Collaborative scheduling of energy-saving spare parts manufacturing and equipment operation strategy using a self-adaptive two-stage memetic algorithm

Previous studies on production scheduling predominantly focus on developing optimal production plans in the context of deterministic customer requirements, achieving certain optimization goals related to the manufacturer. However, little research has been conducted on scheduling customized spare parts manufacturing on the supply side and equipment operation strategy on the demand side simultaneously. In real-world engineering projects, an efficient collaborative scheduling solution not only helps the manufacturer develop optimal production plans for critical spare parts, but also guides equipment users to achieve maximum output. Therefore, this paper studies a collaborative scheduling problem that both considers energy-saving spare parts manufacturing in a flexible job shop and equipment operation strategy of distributed users. To solve this problem effectively, a self-adaptive two-stage memetic algorithm (STMA) is proposed to minizine total energy consumption of the manufacturer and maximize total operation utility of equipment simultaneously. In detail, four heuristic rules are designed to generate a high-quality initial population. In the first stage, the crossover and mutation are utilized for global exploration, and a self-adaptive local search operator is presented to enhance the local development ability of the STMA. In the second stage, several energy-saving and utility-improving strategies are proposed to further optimize the Pareto front solutions generated in the previous period. Extensive experiments are conducted, and the numerical results indicate that STMA is the most promising compared to other well-known algorithms.

Inherently safer design and multi-objective optimization of extractive distillation process via computer-aided molecular design, thermal stability analysis, and multi-objective genetic algorithm

The extractive distillation is a commonly used method for the separation of azeotrope in the chemical industry. In this article, we investigate the safety issue of the extractive distillation process for targeted audience process engineers from the perspective of process safety. We conduct inherently safer design and multi-objective optimization of extractive distillation processes for the separation of methyl acetate/methanol via computer-aided molecular design, thermal hazard analysis, and multi-objective genetic algorithm. Firstly, the entrainers with good safety and separation performances are pre-screened by computer-aided molecular design (CAMD) based on flash point and entrainer selectivity. Then the entrainers with top-ranking separation performance are manually screened based on vapor-liquid equilibria. Next, the Pareto front solution is obtained by optimizing both safety and economic objective functions using a multi-objective genetic algorithm. Finally, the thermal hazard of the entrainer is also investigated via DSC (Differential scanning calorimetry) test. Finally, the optimization result and thermal hazard investigation demonstrate that 1,3-propanediol is inherently safer than dimethyl sulfoxide (DMSO) as a common entrainer in terms of not only a fire hazard but also a thermal hazard for the separation of methyl acetate/methanol mixture. The optimal TAC using 1,3-propanediol as an entrainer is 3.72% lower than that of dimethyl sulfoxide, and the corresponding GISI is 57.73% lower than that of dimethyl sulfoxide. As for DMSO, thermal decomposition can occur near its normal boiling point (189 ℃) and 1,3-propanediol, can be thermally stable over 330 ℃.

Repurposing existing skeletal spatial structure (SkS) system designs using the Field Information Modeling (FIM) framework for generative decision-support in future construction projects

Skeletal spatial structure (SkS) systems are modular systems which have shown promise to support mass customization, and sustainability in construction. SkS have been used extensively in the reconstruction efforts since World War II, particularly to build geometrically flexible and free-form structures. By employing advanced digital engineering and construction practices, the existing SkS designs may be repurposed to generate new optimal designs that satisfy current construction demands of contemporary societies. To this end, this study investigated the application of point cloud processing using the Field Information Modeling (FIM) framework for the digital documentation and generative redesign of existing SkS systems. Three new algorithms were proposed to (i) expand FIM to include generative decision-support; (ii) generate as-built building information modeling (BIM) for SkS; and (iii) modularize SkS designs with repeating patterns for optimal production and supply chain management. These algorithms incorporated a host of new AI-inspired methods, including support vector machine (SVM) for decision support; Bayesian optimization for neighborhood definition; Bayesian Gaussian mixture clustering for modularization; and Monte Carlo stochastic multi-criteria decision making (MCDM) for selection of the top Pareto front solutions obtained by the non-dominant sorting Genetic Algorithm (NSGA II). The algorithms were tested and validated on four real-world point cloud datasets to solve two generative modeling problems, namely, engineering design optimization and facility location optimization. It was observed that the proposed Bayesian neighborhood definition outperformed particle swarm and uniform sampling by 34% and 27%, respectively. The proposed SVM-based linear feature detection outperformed k-means and spectral clustering by 56% and 9%, respectively. Finally, the NSGA II algorithm combined with the stochastic MCDM produced diverse “top four” solutions based on project-specific criteria. The results indicate promise for future utilization of the framework to produce training datasets for generative adversarial networks that generate new designs based only on stakeholder requirements.

A design stage, multi-objective assessment: material selection with environmental life-cycle analysis, labor, and material health considerations for building structure

During the schematic design stage of a building, assessing trade-offs of lifecycle impact of building elements and materials is crucial for the decision-making process when aiming to reduce negative impacts holistically. A multi-objective assessment is proposed, for environmental, labour exploitation risk (LER), and material health lifecycle impact of the structure and foundation elements in the schematic design stage. Using a real, mid-size, mass timber, educational building in Philadelphia, United States as a case study, we deployed multi-objective optimization (MOO) to consider the range of global warming potential (GWP), LER rating, and material health impact of nine product types over 1602 structure and foundation elements. The parametric workflow paired with Building Information Modelling (BIM) objects aims to provide designers a holistic overview on the cumulative, per material, and per building element impact upstream in the supply chain. The Pareto front solutions reveal that concrete floor with metal deck, hollow structural section framing, and concrete foundation slab, footing, and column pose the greatest risk overall for the case study building, and warrant design consideration to substitute with less impactful alternatives.

Multi-objective optimization of embodied and operational energy and carbon emission of a building envelope

Buildings significantly contribute to global annual energy consumption and carbon footprint in their operation and initial construction. Coming up with an eco-friendly and energy-efficient building design requires a comprehensive yet complicated optimization that involves a large domain of design measures and indicators due to operational-embodied energy and carbon emissions-energy trade-offs. This paper proposes a multi-phase and multi-objective genetic based framework that couples EnergyPlus to the embodied energy and embodied carbon databases to jointly assess and optimize both the operational and embodied energy and carbon emissions. It determines the most optimal building configuration by adjusting design measures, which encompass geometrical parameters like window-to-wall ratio and building orientation and envelope-related variables such as the construction materials in external walls and window glazing type. This framework is applied to an ASHRAE benchmark model of a small office building located in College Station, Texas, which falls in climate zone 2A. ASHRAE standards 90.1 is considered for creating the initial baseline design. The building is optimized in two phases. In the first phase, the building is optimized for operational and embodied energy to understand potential tradeoffs. In the second phase, the building is optimized to reduce operational and embodied energy as well as carbon emissions simultaneously. This two-phase optimization culminated into two different Pareto front solutions. The results of the first stage of the optimization lead to a 9.73% and a 37.29% reduction in the building's total operational and embodied energy, respectively from the initial baseline design. In the second phase, the building's operational Energy, embodied energy, operational carbon emissions and embodied carbon emissions are reduced by 9.51%, 37.44%, 17.43%, and 35.67%, respectively in comparison with the baseline model.

Study on flow and heat transfer characteristics in rectangular channels with lantern-shaped pin fin array: Part II—Global sensitivity analysis and optimization

Increasing gas turbine inlet temperatures are placing more stringent requirements on the efficiency of internal cooling. Pin fin arrays are a common tool for cooling electronic components or trailing edge cooling channels of gas turbine blades. A crucial concern for researchers is how to achieve a higher Nusselt number with constrained pressure loss to guarantee that the efficiency of the gas turbine will not be impacted by the power dissipation generated by the driving flow passing through the internal cooling loops. In Part I of this study, a lantern-shaped pin is proposed. To seek a more efficient design solution for the pin fin shape, the second part combines the surrogate model, Sobol’s method, and NSGA-II optimization method to perform the overall and spatial sensitivity analysis and optimization for the shape variables on the generatrix of different pin rows. The first-order and total-order sensitivity indices of different variables to Nu T and f are calculated using the surrogate model-based Sobol’s method. The variables that significantly affect the channel performance are identified. The overall and spatial sensitivity analysis results indicate that the variable X 3 i -1 contributes the most to Nu T and f, with total-order sensitivity indices of 70.59–64.26 and 58.16–59.09%, respectively. The overall optimization increases the Nu T and f by 6.95 and 9.91%, respectively, while the spatial optimization leads to an increase in the Nu T by 2.29% and a decrease in the f by 4.56%. Correspondingly, the two optimizations resulted in an increase in the TPI by 3.61 and 3.89%, respectively. The Pareto front solution obtained by the spatial optimization has a more concentrated distribution. Although the TPI obtained by the spatial optimization is only 0.21% higher than that of the overall optimization, it provides designers with a strategy considering the Nusselt number and pressure loss.

Multi-objective evolutionary optimization of photovoltaic glass for thermal, daylight, and energy consideration

The potential of fenestration systems is increased by incorporating photovoltaic technology into windows. This recently developed technology enhances the ability to generate energy from the building façade, improve the thermal and daylight performance of buildings, and visual comfort of occupants. Integrating an evolutionary optimization algorithm into this technology is one of the possible sustainable solutions to enhance building performance and minimize environmental impact. This paper uses a genetic evolutionary optimization algorithm to explore the optimum performance of photovoltaic glass in an architecture studio regarding annual energy consumption, energy generation, and daylight performance. Design variables include a window-to-wall ratio (i.e., window size and location) and amorphous-silicon thin-film solar cell transparency to generate optimum Pareto-front solutions for the case building. Optimization objectives are minimizing annual thermal (i.e., heating and cooling) loads and maximizing Spatial Daylight Autonomy. Optimized results of low-E semi-transparent amorphous-silicon photovoltaic glass applied on the façade show that the spatial daylight autonomy is increased to 82% with reduced glare risk and higher visual comfort for the occupants. Photovoltaic glass helped reduce the selected room's seasonal and annual lighting loads by up to 26.7%. Lastly, compared to non-optimized photovoltaic glass, they provide 23.2% more annual electrical energy.