Discomfort Hours Research Articles

Building performance optimization through sensitivity Analysis, and economic insights using AI

Optimizing building designs for energy efficiency and occupant comfort presents significant challenges due to the complex and often conflicting requirements of various stakeholders. Consequently, this study conducts a multifaceted sensitivity and economic impact analysis that aims to improve building performance in terms of energy efficiency and occupant comfort by implementing machine learning techniques. Using a broad dataset comprising of 12,000 energy simulation runs for Tvedestrand Upper Secondary School in Norway, several machine learning models were employed with Multi-Layer Perceptron outperforming the others. In addition, several sensitivity analysis methods were used to explore the influence of individual parameters on building performance. The analysis reveals that ventilation rate, room depth, U-value of the facade, and heat gains significantly affect energy consumption. Economic impact analysis was also carried out to compare the cost-effectiveness of traditional HVAC systems with Building Management System (BMS) HVAC solutions. The BMS HVAC system shows significantly lower operational costs over time, with investment costs averaging around 1200 Norwegian kroner (NOK)/m2 and operational costs of approximately 150 NOK/m2 per year. Sensitivity analysis under different economic scenarios highlights the economic viability of the BMS HVAC system. This study identifies optimal building parameters that balance energy efficiency and thermal comfort, achieving total energy consumption between 11.05 and 22.51 kWh/m2 and zero discomfort hours (h > 26 °C). In sum, the findings offer valuable insights for stakeholders, enabling informed decisions about sustainable building design and energy efficiency improvements, ensuring both technical soundness and financial viability under a wide range of conditions, while using the tested tools.

Urban heat islands’ effects on the thermo-energy performance of buildings according to their socio-economic factors

Urban areas experience the urban heat island (UHI) effect, which affects the thermal comfort and energy consumption of buildings. These consequences could vary depending on the socio-economic status of the neighbourhoods. Few studies have investigated how UHI affects socio-economically contrasting districts in thermal comfort and energy performance. Therefore, the primary goal of this research is to evaluate and compare the energy efficiency and thermal comfort conditions of residential buildings in the same city (Temuco, Chile) but located in socio-economically contrasting neighbourhoods. Urban weather files were first modelled in four urban zones using UWG software. Also, EnergyPlus building simulations were conducted to evaluate discomfort hours in adaptive comfort models and energy performance. The results showed annual average UHI intensities between 1.5 and 2.5 K. Urban–rural cooling energy load differences ranged between 12.47% and 38.92%, while heating energy load differences ranged between -20.47% and -81.95%. These distinctions depended on the urban zone, residence model analysed, or energy building standard applied. Similarly, urban-rural differences in thermal comfort times varied from 0.5% to 100%. Results illustrate that the risk of overheating could increase in socio-economically vulnerable areas. This issue could worsen if urban segregation continues to generate poor urban design in low-income districts.

Evaluating the Effect of Adaptive Reuse in the Energy Performance of Historic Buildings: A Case Study from Türkiye

The building sector accounts for 30% to 40% of total energy consumption, and historic buildings play an important role in this proportion. Historical buildings that do not meet the required comfort conditions for the residents are adaptively reused, with various revisions. Recognizing the energy design of a historical building in its original condition and comparing the current situation can help create future solutions. This study examines the changes that a historic house in a hot climate zone in Türkiye experiences, from its original state up until the current situation. Energy analyses of the pre- and post-restoration situation are carried out, and the effect of adaptive reuse decisions on the energy performance of the building is investigated. A dynamic thermal simulation created with DesignBuilder was used to identify the energy use, carbon emissions, and thermal comfort. TM59 adaptive thermal comfort was used for the pre-restoration and the Fanger model for the post-restoration phase. This building, which was repurposed from a three-block residence, consists of a four-block hotel. Although the preservation of its original value is at the forefront, various structural changes were observed. The analysis demonstrates a higher occurrence of discomfort hours during summer compared to winter, consistent across both phases. Furthermore, energy consumption increased significantly, predominantly for heating, representing a doubling of energy use during the post-restoration phase. This is attributed to the building’s conversion into a hotel and the use of mechanical systems. Future research is required to develop strategies to reduce the energy consumption, carbon emissions, and discomfort hours while maintaining the value of the historic building and its materials.

Robust retrofits for rural house envelope considering construction quality and occupant behavior uncertainties: A MOO-integrated Taguchi method

Due to the underdeveloped socio-economic environment, the uncertainties in construction quality and occupant behavior are more prominent for rural house than urban residential buildings. If these uncertainties are not considered, there are higher risks of inaccurate performance prediction, unrealistic retrofit decisions and causing serious performance gap. To address this challenge, this paper proposes a robust envelope retrofitting workflow that combine deterministic multi objective optimization (MOO) and Taguchi method. This workflow stands out for its utilizing mature deterministic MOO to fast approximate Alternative schemes, and utilizing Taguchi method to handle uncertainties, reducing some key disadvantages of traditional robust design optimization (RDO), such as heavy calculating burden, overly conservative solution and difficulty to predefine proper robust objectives or performance target value. The proposed workflow consists of four main steps, formulating Alternative schemes by deterministic MOO, screening highly sensitive uncertain parameters by uncertainty and global sensitivity analysis, formulating Typical uncertain scenarios by orthogonal experiments (OE) and calculating the comprehensive robustness indicators. Results show that by employing the proposed workflow, the selected robust optimal solution not only proves to be robust but also leads to preferable performance enhancements. Compared with the reference mode, the energy efficiency has been improved by 33.2% to 49.4%, the discomfort hours have been reduced by 0.4% to 26.5%, and the robustness has been enhanced by 36.1%.

Integrated optimization of the building envelope and the HVAC system in office building retrofitting

Increasing the energy efficiency of existing buildings is the major strategy to reach carbon neutral targets, given that the building sector is a major emitter. In particular, one major part of building energy usage is the building heating, ventilation and air-cooling system, which are highly depending on the thermal performance of building envelope and increase the challenge of selecting optimal packages of energy efficiency measures. This paper thereby presents a method for optimizing the building envelope and heating, ventilation and air-cooling system. The simulation-based NSGA-III approach developed in this paper is based on coupling the dynamic simulation models created in EnergyPlus software with the non-dominated sorting genetic algorithm, as the engine that performs an optimization process. A large domain of energy efficiency packages combined with heating, ventilation and air-cooling systems and building envelope are investigated to minimize energy consumption, retrofit cost and thermal discomfort in indoor environments, which including ideal loads air system, four pipe fan coil system and variable refrigerant flow system, insulation materials and insulation thickness for external walls and roof, different types of external windows, and window-wall ratio. An office building in Chongqing was chosen as a case study to demonstrate the practical implementation of the proposed method. The results show that the four pipe fan coil system was superior in the diversity and performance of the Pareto front. Then, the optimal packages were generated by a synthetic method, which have 58.57 kWh/m2 of energy consumption, 30.76 CNY/m2 of retrofit cost and 40.67 % of the percentage of thermal discomfort hours. The results also indicate that the yearly energy savings rates are 17.30 %, 23.05 % and 21.10 % separately for ideal loads air system, four pipe fan coil system and variable refrigerant flow system after performing optimization. These results highlight the importance and necessity of performing an optimization procedure for existing buildings to select the optimal retrofit measures.

Developing simplified metamodels for assessing urban park cooling effect on the thermal behavior of the residential buildings: A case study in Nantes, France during a heat wave

The objective of this research is twofold. First, it aims to evaluate the impact of an urban park on the thermal behavior of residential buildings. Second, it focuses on developing and validating metamodels for predicting the cooling needs and summer thermal comfort of three types of residential buildings: single-family houses (Low-rise), small collective housing (Mid-rise), and large collective housing (High-rise). The metamodels are created through regression analysis, using a set of dynamic thermal simulations conducted with TRNSYS software. The number of dynamic simulations is efficiently reduced using the design of experiments method, and the accuracy of the metamodels is further validated through additional simulations. This study was conducted considering the climate weather conditions of the city of Nantes, France. The results demonstrate that an urban park can achieve a maximum reduction of 12.53 % in cooling needs and a 12.50 % maximum reduction in degree hours of discomfort, particularly when the building is protected from solar radiation. Furthermore, developed metamodels show high accuracy with regression coefficients over 0.96. This study aimed to create a decision support tool for local government departments to optimize urban cooling, improve thermal comfort, and reduce cooling loads in buildings by guiding the design of parks and their surrounding built environment.

On the Emergence of Shipping Container Homes: Adaptation to Future Climate Projections

Upcycling shipping containers for housing is increasingly recognised as a sustainable solution to housing challenges, particularly in developing economies like Nigeria. This study evaluates the thermal comfort and energy efficiency of shipping container homes under climate conditions projected for 2080. Using meteorological data for Abuja and Lagos under the Representative Concentration Pathway 8.5, a stand-alone shipping container home was simulated. The study incorporated fabric optimisation techniques, using polyurethane foam and polyisocyanurate board insulation, high-performance window glazing, and shading strategies. The findings reveal that without intervention, container homes would experience significant thermal discomfort, with annual discomfort hours exceeding 28°C in both cities. However, with insulation and shading, annual discomfort hours were reduced by up to 87%, and energy consumption decreased by 76%. These results highlight the critical role of insulation and shading in enhancing thermal comfort and reducing energy demand, making container homes a viable solution for sustainable housing in hot climates. The study underscores the need for policy support to promote the integration of advanced insulation and adaptive design strategies to ensure the resilience and sustainability of container homes in future climates.

Study of The Energy, Economic, Environmental, and Thermal Comfort Impact of The Integration of Hemp Concrete and Hemp Plaster in a Residential Building Envelope in Morocco

In the building sector, the majority of efforts are aimed at achieving greater energy efficiency, a low carbon footprint, and optimum thermal comfort to make buildings more efficient, more sustainable, and more pleasant to live in. In this context, this paper aims to assess the impact of integrating environmentally friendly and green materials (lime-hemp plaster and hemp concrete) into the envelope of an existing residential building. A numerical simulation study was carried out using TRNSYS 18, in four Moroccan climates: Mediterranean, cold semi-arid, hot semi-arid, and hot and dry desert, to study the energy, economic, and environmental impact, as well as that of the thermal comfort. The proposed construction scenarios are compared with the reference scenario in terms of heating and cooling requirements, electricity bills, carbon footprint, and percentage of annual thermal discomfort. The results show that energy savings are highest in the cold semi-arid climate (Oujda) at around 24%. Hemp concrete construction is more effective in reducing heating requirements, with a reduction of up to 39.4%. For cooling, the reduction is only 15.5%. The economic and environmental study shows that using materials such as hemp concrete in an optimal construction and climatic context can reduce electricity bills by 25% and CO2 emissions by around 23.7%. However, the reduction in terms of hours of discomfort is not yet significant enough. Hence the need to combine this technique with other strategies based on bioclimatic design. The use of hemp plaster and concrete for renovating existing buildings or constructing new ones represents a very promising alternative from several points of view: energy, economic, and environmental.

Optimal design of building envelope towards life cycle performance: Impact of considering dynamic grid emission factors

Building-related carbon emissions in the construction and operation stages account for more than one-third of global energy-related emissions. Optimizing during the early design stage is an effective way to reduce building carbon emissions. However, current studies adopt a constant grid emission factor (GEF) to assess the environmental impacts of buildings while ignoring the dynamic changes of the future electricity mix. This will overestimate the operational carbon emissions of the building in the context of the increasing share of renewable energy in power generation. In this study, a dynamic life cycle assessment model based on dynamic GEF is constructed to evaluate the life cycle environmental impacts of buildings. On this basis, a building optimization design framework considering dynamic GEF is proposed, and the impact of considering future variations of GEF on the optimal design of the building envelope is explored. The study is conducted in three steps. First, dynamically changing GEFs are predicted under the future electricity mix based on scenario analysis. Second, a multi-objective optimization framework is established to minimize the life cycle carbon emissions (LCCE) of the building while optimizing its life cycle costs (LCC) and indoor discomfort hours (IDH). Finally, the proposed optimization framework is applied to a prototype office building in Shanghai, and a comparative analysis is conducted for the optimal schemes between a static and two dynamic scenarios. The result reveals that ignoring future variations will lead to overestimating the operational carbon emissions by 49.0%-64.8%, which in turn will result in an over-insulated design of the envelope (i.e., additional material consumption). In addition, compared to the static scenario, consideration of dynamic GEF results in a 38.7%-51.6% reduction in LCCE, a 5.2%-6.1% reduction in LCC, and a 5.3%-9.5% increase in IDH for the optimal solutions. This research illustrates the importance of considering dynamic GEF in identifying the optimal design parameters and can help decision-makers reasonably choose the optimal schemes during early-stage design.

Dynamic Cooling – A concept of time-sensitive thermal regulation to cut cooling energy demand in office buildings

This paper focuses on how the existing sealed office typology can adapt to a warming world while minimizing the high energy demand for space cooling, through Dynamic Cooling, by adjusting the cooling temperature profile towards the average human circadian rhythm to mitigate heat stress during times of heightened thermal sensitivity. This concept is explored through a study in which we compared the effects of Dynamic Cooling, constant cooling, and no cooling on the core body temperature of 21 participants as well as a thermal dynamic simulation of 270 office space configurations under the same cooling profiles, comparing the total energy demand, peak loads, and overall hours of discomfort. The results of the study suggest the different conditioning profiles have no direct impact on the overall change in core body temperature throughout the day (difference < 0.04 K) but do influence the overall range throughout (from 0.37 K under constant cooling to 0.53 K under Dynamic Cooling). Additionally, there is no clear correlation between elevated indoor temperatures and CBT during the morning hours, questioning the necessity and effectiveness of pre-lunch cooling. This is underlined by the simulation findings, which reveal a non-linear relationship between savings in cooling demand and a decline in thermal comfort, with a 51.3 % reduction in ideal cooling energy demand and a 25.2 %–36 % increase of Over-Temperature-Kelvin-Hours, from a steady state cooling profile to a profile of Dynamic Cooling. The results of the study and simulation challenge traditional cooling standards and highlight the need for a balance between minimizing thermal discomfort and the necessary cooling energy demand.

Assessing the effectiveness of bioclimatic strategies in improving thermal comfort and reducing energy consumption in a house located in a desert climate

This article examined the effectiveness of bioclimatic strategies to enhance thermal comfort and decrease energy consumption in a desert-climate residence. The study assessed the impact of thermal insulation, natural ventilation, and phase-change materials (PCMs) over a year, with each strategy evaluated both independently and in combination. Monthly energy bills were analyzed to determine the impact of these strategies. The results indicated substantial reductions in energy costs, with decreases of up to 50% during transitional seasons; however, the complete elimination of heating and cooling systems was not feasible due to significant thermal phase differences between indoor and outdoor environments. Further analysis of thermal discomfort hours revealed that increasing insulation thickness during specific seasons could mitigate peak heat intensity and delay its occurrence, thus extending periods of comfort and reducing discomfort hours. Despite these improvements, some periods of thermal distress persisted during the warmest months, underscoring the necessity for a balanced approach to climate adaptation strategies. Overall, the implementation of these strategies proved effective in improving comfort and reducing energy usage in desert-climate homes, although they do not fully negate the need for traditional heating and cooling systems.

A physics-informed deep learning-based urban building thermal comfort modeling and prediction framework for identifying thermally vulnerable building stock

PurposeDue to the increasing frequency of extreme weather and densifying urban landscapes, residences are susceptible to heat-related discomfort, especially those in a naturally ventilated built environment in tropical climates. Indoor thermal comfort is thus paramount to building sustainability and improving occupants' health and well-being. However, to assess indoor thermal comfort considering the urban context, it is conventional to use questionnaire surveys and monitoring units, which are both case-centric and time-intensive. This study presents a dynamic computational thermal comfort modeling framework that can determine indoor thermal comfort at an urban scale to bridge this gap.Design/methodology/approachThe framework culminates in developing a deep learning model for predicting the accurate hourly indoor temperature of urban building stock by the coupling urban scale capabilities of environment modeling with single-building dynamic thermal simulations.FindingsUsing the framework, a surrogate model is created and verified for Dharavi, India's informal urban settlement. The results indicated that the developed surrogate model could predict the building's indoor temperature in several complex new urban scenarios with different building orientations, layouts, building-to-building distances and surrounding building heights, using five different random urban representative scenarios as the training set. The prediction accuracy was reliable, as evidenced by the mean bias error (MBE) and coefficient of (CV) root mean squared error (MSE) falling between 0 and 5%. The findings also showed that if the urban context is ignored, estimates of annual discomfort hours may be inaccurate by as much as 70%.Social implicationsThe developed computational framework could help regulators and policymakers engage in more informed and quantitative decision-making and direct efforts to enhance the thermal comfort of low-income dwellings and informal settlements.Originality/valueUp to this point, majority of literature that has been presented has concentrated on building a body of knowledge about urban-based modeling from an energy management standpoint. In contrast, this study suggests a dynamic computational thermal comfort modeling framework that takes into account the urban context of the neighborhood while examining the indoor thermal comfort of the residential building stock.

Enhancing thermal comfort in buildings through the integration of phase change material on the building envelope: a simulation study

Abstract According to the report published by the International Energy Agency (IEA), active space cooling and air conditioning systems consume approximately 16% of the building sector’s final electricity consumption and contribute 3.94% of global greenhouse gas emissions. In this regard, the use of low-cost but effective passive solutions, such as organic phase change materials (PCM) on the building envelope, can provide high thermal mass and thus can lower the temperature fluctuation inside the building. In the current study, the potential of PCM-enhanced building envelopes to enhance thermal comfort has been studied. The study has been performed using the conduction finite difference (ConFD) algorithm in EnergyPlus software. A comparative study has been done for a traditional reinforced cement concrete (RCC) reference test room of size (5m x 6m x 3m) and another room of the same size with PCM-enhanced walls for the hot and humid climate zone of Guwahati (latitude 26.1, longitude 91.7), in India. A PCM thickness of 20 mm is used at the external surface of the wall between the red clay burnt brick (120 mm in thickness) and the cement plaster (25mm thick). The PCM used for the study is a biocomposite PCM, named WH-PCM, with a melting point of 31.5°C and thermal conductivity of 0.27 W/mK. The simulation results reveal that the R-value of the external wall of the room with PCM walls has been increased by 84.90% compared to the reference room with the incorporation of WH-PCM, resulting in a mean zone temperature difference of 0.3°C throughout the year. Also, the indoor thermal discomfort hours have been annually reduced by 15.82% with the use of WH-PCM when compared with the reference room. A similar temperature difference trend, which is 1.93°C, is also observed in the single PCM brick experimental result under identical testing conditions as a regular brick of the same size.

Design optimization of an assisted living facility to improve summer thermal comfort in warming climates

Assisted living facilities with optimal summer thermal comfort are necessary to ensure improved quality of life for the older individuals in warming climates. Older individuals are exposed to higher mortality risks during periods of excessive heat. This study aims to identify and optimize key design variables for a passive house certified assisted living facility in Belgium with high comfort expectations using EnergyPlus to improve indoor thermal comfort during extreme climates. The sensitivity analysis used the Standard Regression Coefficient method with Latin Hypercube Sampling for eighteen design variables under four categories: layout, envelope, operation, and system. The analysis identified flat roof and external floor insulation as the most influential parameters, whereas lighting gain and cooling setback temperature were the least influential parameters. Furthermore, optimizing the reference building with the most sensitive design variables using a genetic algorithm based on the NSGA-II method found ideal configurations that would ensure minimum thermal discomfort hours during extreme summers. The study findings provide an evidence-based approach for building engineers and designers for early-stage design of assisted living facilities that maintain optimal comfort in mixed humid climates of Europe.

Optimising Building Energy and Comfort Predictions with Intelligent Computational Model

Building performance prediction is a significant area of research, due to its potential to enhance the efficiency of building energy management systems. Its importance is particularly evident when such predictions are validated against field data. This paper presents an intelligent computational model combining Monte Carlo analysis, Energy Plus, and an artificial neural network (ANN) to refine energy consumption and thermal comfort predictions. This model addresses various combinations of architectural building design parameters and their distributions, effectively managing the complex non-linear relationships between the response variables and predictors. The model’s strength is demonstrated through its alignment with R2 values exceeding 0.97 for both thermal discomfort hours and energy consumption during the training and testing phases. Validation with field investigation data further confirms its accuracy, demonstrating average relative errors below 2.0% for total energy consumption and below 1.0% for average thermal discomfort hours. In particular, an average underestimation of −12.5% in performance discrepancies is observed when comparing the building energy simulation model with field data, while the intelligent computational model presented a smaller overestimation error (of +8.65%) when validated against the field data. This discrepancy highlights the model’s potential and reliability for the simulation of real-world building performance metrics, marking it as a valuable tool for practitioners and researchers in the field of building sustainability.

Improving the efficiency of the trombe wall by integrating multi-fold glazing and sustainable materials: Ifrane, Morocco as a case study

The Trombe wall is renowned for its effectiveness in passive heating during winter, but it can lead to overheating in summer. To address this issue, this study investigates a Trombe wall with fully opening glazing during the summer season, employing multi-fold glazing. The research, conducted in Ifrane, Morocco, focuses on a residential building using the Conduction Finite Difference Method within the DesignBuilder software, and examines three configurations: a reference configuration, classic Trombe wall, and Trombe wall with multi-fold glazing. The results demonstrate that the last configuration achieves a 63 % (24,94 kWh/m2) reduction in total energy consumption compared to the reference configuration and a 34 % (7,62 kWh/m2) reduction compared to the classic Trombe wall. Additionally, the implementation of sustainable ecological biomaterials, such as timber instead of concrete, is explored to reduce CO2 emissions. Further analysis of the Trombe wall with multi-fold glazing is conducted by considering five parameters: glazing type, emissivity, vent size, insulation, and PCM thicknesses. These parameters are examined across five different variables. Reducing emissivity leads to reductions of 51 % and 30 % in heating energy consumption and discomfort hours, respectively. Modifying the type of glazing results in an 11 % reduction in heating energy consumption. Increasing insulation thickness leads to notable reductions of 15 % in heating energy consumption and 6 % in discomfort hours. Conversely, increasing PCM thickness and vent size yields had minimal impact on total energy consumption, each resulting in a 1 % reduction. This research provides insights into optimizing Trombe walls by integrating multi-fold glazing to address summer overheating issues and by exploring additional parameters to enhance system performance.

Deterministic and probabilistic occupant-centric control's impacts on the indoor environment in free-running households

PurposeOccupant behavior can lead to considerable uncertainties in thermal comfort and air quality within buildings. To tackle this challenge, the use of probabilistic controls to simulate occupant behavior has emerged as a potential solution. This study seeks to analyze the performance of free-running households by examining adaptive thermal comfort and CO2 concentration, both crucial variables in indoor air quality. The investigation of indoor environment dynamics caused by the occupants' behavior, especially after the COVID-19 pandemic, became increasingly important. Specifically, it investigates 13 distinct window and shading control strategies in courtyard houses to identify the factors that prompt occupants to interact with shading and windows and determine which control approach effectively minimizes the performance gap.Design/methodology/approachThis paper compares commonly used deterministic and probabilistic control functions and their effects on occupant comfort and indoor air quality in four zones surrounding a courtyard. The zones are differentiated by windows facing the courtyard. The study utilizes the energy management system (EMS) functionality of EnergyPlus within an algorithmic interface called Ladybug Tools. By modifying geometrical dimensions, orientation, window-to-wall ratio (WWR) and window operable fraction, a total of 465 cases are analyzed to identify effective control scenarios. According to the literature, these factors were selected because of their potential significant impact on occupants’ thermal comfort and indoor air quality, in addition to the natural ventilation flow rate. Additionally, the Random Forest algorithm is employed to estimate the individual impact of each control scenario on indoor thermal comfort and air quality metrics, including operative temperature and CO2 concentration.FindingsThe findings of the study confirmed that both deterministic and probabilistic window control algorithms were effective in reducing thermal discomfort hours, with reductions of 56.7 and 41.1%, respectively. Deterministic shading controls resulted in a reduction of 18.5%. Implementing the window control strategies led to a significant decrease of 87.8% in indoor CO2 concentration. The sensitivity analysis revealed that outdoor temperature exhibited the strongest positive correlation with indoor operative temperature while showing a negative correlation with indoor CO2 concentration. Furthermore, zone orientation and length were identified as the most influential design variables in achieving the desired performance outcomes.Research limitations/implicationsIt’s important to acknowledge the limitations of this study. Firstly, the potential impact of air circulation through the central zone was not considered. Secondly, the investigated control scenarios may have different impacts on air-conditioned buildings, especially when considering energy consumption. Thirdly, the study heavily relied on simulation tools and algorithms, which may limit its real-world applicability. The accuracy of the simulations depends on the quality of the input data and the assumptions made in the models. Fourthly, the case study is hypothetical in nature to be able to compare different control scenarios and their implications. Lastly, the comparative analysis was limited to a specific climate, which may restrict the generalizability of the findings in different climates.Originality/valueOccupant behavior represents a significant source of uncertainty, particularly during the early stages of design. This study aims to offer a comparative analysis of various deterministic and probabilistic control scenarios that are based on occupant behavior. The study evaluates the effectiveness and validity of these proposed control scenarios, providing valuable insights for design decision-making.

Sensitivity Analysis and Optimization of Energy-Saving Measures for Office Building in Hot Summer and Cold Winter Regions

To effectively reduce building energy consumption and enhance indoor thermal comfort, this study developed a sensitivity analysis and multi-objective optimization method for an office building in Hangzhou considering the effects of parameter interactions. We utilized Latin hypercube sampling (LHS) to sample the parameters and subsequently simulated the energy consumption and discomfort hours for the building’s four orientations. Following this, Gaussian process regression models were established, and the feature importance ranking method (FIRM) alongside an interaction effects method were employed to quantify the sensitivity of 10 parameters. Lastly, a multi-objective optimization was performed for total energy consumption and discomfort hours, yielding a set of Pareto-optimal solutions. Through the VIKOR method, the optimal solution within the Pareto set was identified, resulting in three sets of optimal solutions.

Feasibility and performance analysis of a net-zero energy residential building in tropical climates: A case of Congo-Brazzaville

Current building techniques in developing countries have become extremely critical. The net-zero energy concept has emerged as an essential strategy for minimising energy consumption and greenhouse gas emissions. However, its implementation, particularly in developing countries, remains a critical challenge. The purpose of this work is to assess the feasibility of achieving a net-zero energy building by combining energy-efficient design practices and renewable energy systems under the climatic conditions of the Republic of Congo. To achieve the stipulated objectives, DesignBuilder software was utilised for building modelling, energy load assessment and multiobjective optimisation of building energy efficiency measures, and the multicriteria energy optimisation was carried out using the HOMER tool. The optimisation results showed that the implementation of energy efficiency measures resulted in 39.15% energy savings, 43.45% thermal load savings and 55.68% discomfort hours reduction. Furthermore, the renewable energy system can provide 100% of the building’s energy load and a total savings of 3341.84 kgCO2eq/year. The system’s levelised cost of energy was estimated at 0.256 $/kWh, with a net present cost of $20,231, while the overall life cycle cost was calculated at 188.91 $/m2.

Parametric design of photovoltaic louver integrated shading devices for west facade windows of office buildings in central China

ABSTRACT In order to improve the shading performance and power generation effect of Photovoltaic shading integrated devices (PVSDs) on the west facade of buildings in China, a multi-objective optimization design method is proposed in this paper. Taking a west-facing office building in Zhengzhou, Henan Province, as an example, a parametric model of photovoltaic (PV) louvers for windows on the west facade is established, and parametric design research is carried out on the energy-saving potential of PV louver shading systems. Based on the optimization objectives of power generation of PV louvers, indoor thermal discomfort hours, building cooling load and lighting energy consumption, the performance of the west facade office with PVSD under different design strategies was simulated, and the optimal design strategy was determined according to the preference of decision makers. Compared with the basic type without a non-shading model, the indoor lighting energy consumption of the optimized west facade PVSD is slightly increased. However, overall performance is improved, with a 35% reduction in cooling load, a decrease of 804.59 kWh in total energy consumption, and a shortened summer thermal discomfort time of 404 hours. The power generation of PV louvers can replace 46% of the total energy consumption of the room. The results show that the installation of PVSD on the west facade window is important for alleviating the overheating inside the room and improving the energy-saving and power generation potential of the room.