Prediction Of Building Performance Research Articles

A review of complex window-glazing systems for building energy saving and daylight comfort: Glazing technologies and their building performance prediction

The increasing energy consumption and detrimental CO2 emissions contributing to global warming underscore the urgent necessity for energy conservation, especially within buildings. Among different building components, fenestration plays a pivotal role as it accounts for the majority of heat transfer across the building envelope. This emphasises the significance of window-glazing technologies in enhancing their thermal performance. Furthermore, window-glazing systems can lead to overheating issues, particularly in summer, and glare issues, especially in winter. These challenges have spurred the development of various advanced glazing systems. This paper provides a comprehensive review of these advanced glazing technologies based on their functionalities and working principles, with a focus on parameters such as U-value, solar heat gain coefficient and visible transmittance. Among these technologies, vacuum and aerogel glazing systems exhibit superior thermal insulation properties, with U-values below 1 W/m2 K, making them suitable for heating-dominated climates. Smart window systems, such as electrochromic windows, are ideal for cooling-dominated climates due to their low solar heat gain coefficient (0.09–0.47) and visible transmittance (0.02–0.62). Photovoltaic window systems not only provide effective thermal insulation and solar shading but also produce additional power for on-site use. Some of these glazing systems feature complex structures, which present challenges when integrating them into existing building simulation software to assess their impact on building performance. Therefore, this paper also examines techniques for conducting energy and daylight performance simulations for buildings that make use of complex window systems. Ultimately, the authors propose an approach to characterise the thermal, optical and electrical properties of a complex photovoltaic window system within existing building simulation software, such as EnergyPlus. This approach facilitates a thorough investigation into the effects of complex window systems on building energy efficiency and indoor comfort.

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.

Transfer of building retrofitting evaluations for data-scarce conditions: An empirical study for Sweden to China

Evaluating and comparing the performances of different strategies is critical for energy-efficient building retrofitting. Data-driven modelling based on large building performance datasets is an effective method for such evaluations. However, it could be challenging to apply this approach to buildings from data-scarce areas where local building performance datasets have not been well-established, which means the data falls short of the high demand for building retrofitting on a global level. To address this, a transfer learning approach is proposed in this study that can evaluate the performance of buildings without local well-established building performance datasets. The proposed approach is applied in the Swedish-Chinese empirical study that relies on the Swedish dataset to transfer and predict the building performance in China without well-established datasets. It was achieved by applying fuzzy C-means clustering and a neural network (FCM-BRBNN) to pre-train the evaluation model based on the Swedish dataset. Then, the proposed approach collects a small sample of Chinese buildings in the data-scarce area and transfers the model to local building performance prediction. The results show that the transfer learning approach can reliably predict the performance of building retrofitting in data-scarce areas with only hundreds of local building samples. As such, this study provides a novel methodology that can support the evaluation and comparison of retrofitting strategies in data-scarce regions and countries with only limited local data. It could efficiently assist designers in optimizing energy-efficient designs in the pre-retrofit stage. Crucially, the methodology enables the transfer of knowledge regarding building performance across different countries and regions, being pivotal for the international collaboration required to stimulate the global energy-efficiency transformation.

Building Performance Prediction Model Using CAD Technology and Recurrent Neural Networks

Building Performance Prediction Model Using CAD Technology and Recurrent Neural Networks

Identification of influential factors for combined energy consumption and indoor environmental quality in residential buildings

The development of an overall building performance simulation model requires a multitude of input parameters which can be a challenging and resource-heavy task for building modellers. Furthermore, some parameters have little impact on a building’s overall performance and contribute little towards model prediction accuracy. Feature selection has been employed to identify the most influential input parameters to reduce complexity and computational time. However, previous studies focused mainly on identifying parameters that impact energy consumption in residential buildings, neglecting the important relationship between energy consumption and indoor environmental quality (IEQ). Therefore, this study proposes a novel simulation framework that integrates occupancy-based building archetypes, parametric simulation, and machine learning techniques to develop an overall building performance prediction model. Using this framework, the study generates a synthetic dataset of 40,000 simulations and performed embedded feature selection using two machine learning algorithms, Random Forest (RF) and Gradient Boosting Technique (GBT), to identify parameters that impact heating energy consumption, thermal discomfort hours, and CO2 concentration simultaneously. The results demonstrate that the ranking for importance and the number of required parameters vary depending on the target variable. Also, the set of parameters for combined analysis differs from individual target variable analysis. The GBT algorithm with embedded feature selection provides the most accurate prediction results with lower root mean square error (RMSE) and absolute error (AE) for individual and combined analyses. This study provides valuable insights for accurate parameter selection and analysis of overall building performance.

Internet of Things (IoT) in Buildings: A Learning Factory

Advances towards smart ecosystems showcase Internet of Things (IoT) as a transversal strategy to improve energy efficiency in buildings, enhance their comfort and environmental conditions, and increase knowledge about building behavior, its relationships with users and the interconnections among themselves and the environmental and ecological context. EU estimates that 75% of the building stock is inefficient and more than 40 years old. Although many buildings have some type of system for regulating the indoor temperature, only a small subset provides integrated heating, ventilation, and air conditioning (HVAC) systems. Within that subset, only a small percentage includes smart sensors, and only a slight portion of that percentage integrates those sensors into IoT ecosystems. This work pursues two objectives. The first is to understand the built environment as a set of interconnected systems constituting a complex framework in which IoT ecosystems are key enabling technologies for improving energy efficiency and indoor air quality (IAQ) by filling the gap between theoretical simulations and real measurements. The second is to understand IoT ecosystems as cost-effective solutions for acquiring data through connected sensors, analyzing information in real time, and building knowledge to make data-driven decisions. The dataset is publicly available for third-party use to assist the scientific community in its research studies. This paper details the functional scheme of the IoT ecosystem following a three-level methodology for (1) identifying buildings (with regard to their use patterns, thermal variation, geographical orientation, etc.) to analyze their performance; (2) selecting representative spaces (according to their location, orientation, use, size, occupancy, etc.) to monitor their behavior; and (3) deploying and configuring an infrastructure with +200 geolocated wireless sensors in +100 representative spaces, collecting a dataset of +10,000 measurements every hour. The results obtained through real installations with IoT as a learning factory include several learned lessons about building complexity, energy consumption, costs, savings, IAQ and health improvement. A proof of concept of building performance prediction based on neural networks (applied to CO2 and temperature) is proposed. This first learning shows that IAQ measurements meet recommended levels around 90% of the time and that an IoT-managed HVAC system can achieve energy-consumption savings of between 10 and 15%. In summary, in a real context involving economic restrictions, complexity, high energy costs, social vulnerability, and climate change, IoT-based strategies, as proposed in this work, offer a modular and interoperable approach, moving towards smart communities (buildings, cities, regions, etc.) by improving energy efficiency and environmental quality (indoor and outdoor) at low cost, with quick implementation, and low impact on users. Great challenges remain for growth and interconnection in IoT use, especially challenges posed by climate change and sustainability.

Review of Methods to Create Meteorological Data Suitable for Moisture Control Design by Hygrothermal Building Envelope Simulation

Hygrothermal simulations have become essential for sustainable and resilient building design because moisture is the major cause of problems in buildings. Appropriate meteorological input data are important to obtain meaningful simulation results. Therefore, this article reviews different methods to create Hygrothermal Reference Years (HRY) as severe or average climate inputs. The current standards define HRYs solely based on outdoor temperature, although moisture problems are caused by a combination of climate parameters, including driving rain and other loads. Therefore, there are also methods considering several impact parameters. The existing methods can be classified into two categories: construction-independent and construction-dependent methods. The former determines HRY based on a weather data analysis and is useful for large-scale parametric studies comprising many climatic parameters acting on buildings. The latter is based in addition to computer simulations to verify the HRY also in the context of specific construction types. It is a more comprehensive approach since the moisture responses of constructions are the decisive outcome for performance predictions. The advantages and disadvantages of the different methods are summarized and compared. Lastly, further research questions and simplifications aimed at practitioners are pointed out to arrive at reliable hygrothermal building performance predictions.

The Effect of Probabilistic Window Opening Behavior of Occupants on Adaptive Thermal Comfort (The Case of Courtyard House in Yazd, Bandar Abbas, and Tabriz)

Considering the global energy crisis and the need to reduce energy consumption while providing thermal comfort to occupants, building performance prediction using building simulation programs requires higher accuracy of output data. Therefore, it seems necessary to study the impact of occupant behavior, which is the main source of uncertainty in residential buildings. The traditional courtyard houses, which are recognized as a successful passive house model, respond to different climatic conditions. Therefore, this research focuses on this building type to analyze occupant window opening control scenarios and determine which control works better. For this purpose, several probabilistic controls and their effects on the adaptive thermal comfort of occupants in zones around a central courtyard were compared in the three cities of Yazd, Bandar Abbas, and Tabriz. Energy Plus was used as a simulation program for the application of Grasshopper's energy management system (EMS) along with the Ladybug and Honeybee environmental plugins. The results show that the window control algorithms can increase the adaptive thermal comfort of occupants by 25.7%, 32.2%, and 20.3% in each of the climates of Yazd, Bandar Abbas, and Tabriz cities, respectively. Indoor and outdoor temperature were the most significant variables for opening windows in the warm and cold seasons, respectively.

Building performance simulation of a photovoltaic façade enhanced with latent heat storage: Model validation and power generation prediction

Photovoltaic solar-based façade concepts are considered one of the promising representatives in the overall energy-saving campaign. The presented study aims at the simulation approach and its validation relative to experimental measurements of a double-skin building-integrated photovoltaic (BiPV) concept coupled with phase change material (PCM) in climate-responsive façade design. A comparative study of the thermo-responsive reactions and electricity production of two BiPV façades (with and without PCM layer) was conducted using the building energy simulation (BES) method to reveal the complexity of building performance predictions. An empirical validation of the BES tool working under the EnergyPlus computational engine is conducted in this connection. The consistency between the simulation results and the experimental data obtained via calorimetry and dynamic outdoor tests is comprehensively investigated. The current zonal modelling approach of the BES method is suitable when predicting the thermo-responsive capabilities of a PCM-based BiPV façade. Accordingly, the best agreement is found in the PCM data based on Triple-layer calorimetry (3LC). Using PCM in a BiPV system can increase the maximum peak electricity production from 4.3 to 4.8 % obtained experimentally with a 10–14 K decrease in PV panel operating temperature. In contrast, a difference, from 3.8 to 5.4 %, is observed with the equivalent one-diode model between the simulation results for solar panels based on copper‑indium‑gallium-selenide (CIGS) technology. Hence, the performance prediction of PV electrical conversion efficiency is calibrated using a semiconductor band gap at a value of 1.4 eV.

Data-driven prediction and optimization of residential building performance in Singapore considering the impact of climate change

Research on the performance of tropical residential buildings requires careful consideration of the effects of the changing weather conditions. Based on machine learning and multi-objective genetic optimization algorithms, this study proposes an integrated framework for predicting and optimizing the performance of residential buildings in Singapore. The framework was used for two Singapore residential building types, the point block building and the slab block building, to examine their optimal performance in terms of daylight performance, energy efficiency, and thermal comfort under future climate conditions in the short term (2021–2040), middle term (2041–2060), and long term (2061–2100). The results show that the XGBoost algorithm and transfer learning method perform well in building performance prediction, specifically in the source domain (point block buildings) with R2 = 0.95 and in the target domain (slab block building) with R2 = 0.87. The general trend in the optimal design parameters for residential buildings is to enhance the thermal insulation of building walls and envelopes to better cope with future warming climate conditions, while providing adequate indoor lighting. This study contributes to the field of climate-resilient residential buildings in Singapore. This is closely linked to the Singapore government's quest for sustainable development and to enhance the well-being of its residents.

On the robustness of thermal comfort against uncertain future climate: A Bayesian bootstrap method

Climate change mitigation and adaptation warrants their synergetic consideration in the building design process, yet past decades have witnessed an unbalanced focus on the mitigation of energy and carbon. In redressing the imbalance, the major challenge lies in the accurate prediction of future building performance via building energy modelling, which is considerably hindered by uncertainties in future climate data. Robustness analysis is a promising technique to inform uncertainty-based decision-making, but its application to future thermal comfort has yet to be sufficiently explored in the built environment. From the perspective of domestic overheating, this paper represents an initial investigation into the implementation of the Bayesian bootstrap method, to quantitatively evaluate the robustness of thermal comfort against uncertain future climate. This is demonstrated using a case study of two typical post-retrofit dwellings in England, where the Bayesian bootstrap also enables the statistical comparison of their expected future overheating risk with climate uncertainty considered. The main findings reveal the magnitude of both overheating risk and its variability experienced during nocturnal occupancy in regulation-compliant dwellings, respectively comprising nearly 15 and 12 times greater than during daytime in extreme cases. Results also imply that adaptive ventilation is potentially the key measure to enhance the robustness of thermal comfort against climate uncertainty. Overall, the Bayesian bootstrap is shown to provide a systematically consistent approach to the robustness assessment of future thermal comfort, which can facilitate the comparability of design alternatives that is vital to the building design decision-making process integrating both mitigation and adaptation strategies.

Carbon emission and thermal comfort prediction model for an office building considering the contribution rate of design parameters

In recent years, increasing climate change has triggered the development of various techniques and strategies for predicting building energy consumption and thermal comfort. However, many features could affect prediction efficiency, and the coupling of appropriate learning algorithms with different sampling methods and input parameters requires further research. In this study, we evaluated 25 features of an office building model. Then, the comprehensive effects of five different sampling methods and cumulative contribution rates (CCR) of input parameters on the prediction performance of building carbon emission (BCE) and indoor discomfort hour (IDH) were explored in 10 machine learning algorithms. The results indicated that the Sobol sampling method could achieve the best prediction effect in the combinations of different contribution rates of features and machine learning algorithms. Meanwhile, an artificial neural network (ANN) was the best learning algorithm when the CCR was 100%. However, the optimal machine learning method corresponding to each CCR stage differed. When the CCR was reduced to 50%, only three influence factors were considered most important, and K-nearest neighbors (KNN) was the best prediction algorithm in this scenario, indicating that the input parameters were reduced by 88%, and the R2 value was only reduced by 6.1%. Therefore, we proposed a new strategy idea for the research of building performance prediction by determining the most important building impact factors and suitable machine learning algorithms, which could simplify the prediction process and improve prediction efficiency while reducing building parameters.

Data-driven, long-term prediction of building performance under climate change: Building energy demand and BIPV energy generation analysis across Turkey

This research presents a methodological framework for lifetime energy demand and PV energy generation predictions for a given building considering the CC impacts through multivariate regression models. As a case study, a hypothetical office building in Turkey was selected. An existing linear morphing methodology was utilized to generate future weather files for all 81 cities in Turkey. For each year and city, corresponding weather metrics were calculated, and heating/cooling demand and PV energy generation values were computed through building energy simulations. Obtained data were used to develop two sets of multivariate regression models: (i) models to predict future weather metrics and (ii) models to predict future energy demand and generation. These models allowed lifetime energy demand and generation analysis (including associated GWP and cost) of the building considering CC impacts using only the current weather metrics of its location. For a lifetime of 60 years, considering CC impacts yielded substantially higher cooling (averaging at +0.5 MWh/m2 in the warmest region) and lower heating loads (averaging at −0.4 MWh/m2 in the coldest region). For Turkey, the carbon intensity and the unit cost of cooling are much higher than those of heating. Therefore, the shift from heating to cooling has significant consequences in lifetime GWP and cost values (averaging +212 kg CO2-eq/m2 and +27 $/m2, respectively, for the warmest region), emphasizing the importance of the decarbonization of the energy sector. The impact of CC on PV energy generation was limited (all-city average of +0.02 MWh/m2 for the building lifetime). Our regression-based approach can be further expanded to include not only various building parameters and types, but also supply-demand matching potentials.

An Intelligent Optimization for Building Design Based on BP Neural Network and SPEA-II Multiobjective Algorithm.

With the continuous development of the field of building optimization, more and more optimization methods have sprung up, among which there are many kinds of intelligent optimization algorithms. This kind of intelligent optimization algorithm usually relies on the traditional building performance simulation method to obtain the building performance index for optimization. However, intelligent optimization algorithms generally require large-scale calculations. At the same time, the time required for building performance simulation is often limited by the complexity of building models and the configuration of computers, which leads to a long time for performance optimization, which cannot give efficient and accurate feedback to designers in engineering. Building performance optimization methods based on intelligent optimization algorithms are mainly used in scientific research and are difficult to put into practical projects. Therefore, this paper builds an accurate and efficient platform for building performance prediction and optimization to help designers make decisions combined with BP neural network and the SPEA-II multiobjective optimization algorithm. Besides, the optimization results of the case are quantitatively and qualitatively analyzed and presented in visual form based on the BP neural network prediction model. Quantitative analysis includes the evolution process of solution set, convergence process, and comprehensive quality evaluation of solution set. Qualitative analysis includes Pareto frontier and optimal architectural scheme analysis. Finally, the conclusion shows that the platform prediction and optimization can give accurate and reliable optimal solution, and the optimal building scheme is reasonable and has high engineering application value.

Regression-Based, Long-Term Prediction of Building Performance Under Climate Change: Building Energy Demand and Bipv Energy Generation Analysis Across Turkey

Regression-Based, Long-Term Prediction of Building Performance Under Climate Change: Building Energy Demand and Bipv Energy Generation Analysis Across Turkey

Utilizing High Performance Computing to Improve the Application of Machine Learning for Time-Efficient Prediction of Buildings’ Daylighting Performance

Architects often investigate the daylighting performance of hundreds of design solutions and configurations to ensure an energy-efficient solution for their designs. To shorten the time required for daylighting simulations, architects usually reduce the number of variables or parameters of the building and facade design. This practice usually results in the elimination of design variables that could contribute to an energy-optimized design configuration. Therefore, recent research has focused on incorporating machine learning algorithms that require the execution of only a relatively small subset of the simulations to predict the daylighting and energy performance of buildings. Although machine learning has been shown to be accurate, it still becomes a time-consuming process due to the time required to execute a set of simulations to be used as training and validation data. Furthermore, to save time, designers often decide to use a small simulation subset, which leads to a poorly designed machine learning algorithm that produces inaccurate results. Therefore, this study aims to introduce an automated framework that utilizes high performance computing (HPC) to execute the simulations necessary for the machine learning algorithm while saving time and effort. High performance computing facilitates the execution of thousands of tasks simultaneously for a time-efficient simulation process, therefore allowing designers to increase the size of the simulation’s subset. Pairing high performance computing with machine learning allows for accurate and nearly instantaneous building performance predictions.

Method application and analyses of visual and thermal-energy performance prediction in offices buildings with internal shading devices

In the design stage, many methods to analyse and improve the building performance prediction have been developed by the climate-based building simulation approach. This research aimed to propose a method to analyse the visual and thermal-energy performance of offices buildings with adjustable shading devices. The considered visual criteria were daylight glare probability, daylighting availability and excessive sunlight exposure. Window's solar heat gain and the cooling energy demand were the thermal-energy criteria. A modular building was submitted to the method application. The shading devices effects were compared and the relationships between the hourly variables were analysed. One location (Florianopolis, Brazil) and four façade directions were tested. At the controlled conditions, the occupant was considered in the most vulnerable position and view direction regarding daylight glare, chosen from three positions and five view directions tested at the uncontrolled condition. According to the proposed method, were applied eight shading devices control: two static conditions and three dynamic frequencies guided by two triggers. Three shading devices considering two proprieties sets presented the effects comparison and the better performance, such as the use of the clear roller shades operated by the daily daylight glare control. High correlation and determination coefficients (r e r2) and better defined-trends, were indicated firstly between illuminance and solar radiation and secondly between the daylight glare probability and the window's solar heat gain. The dynamic controls showed coefficients lower than the static conditions, demonstrating a correspondence reduction between the shading devices effects.

Augmenting building performance predictions during design using generative adversarial networks and immersive virtual environments

Existing building performance models (existing BPMs) often lack the capability for addressing human-building interactions in future buildings or buildings under design because they are mainly derived using data in existing buildings. The limitation may contribute to discrepancies between simulated and actual building performance. In a previous study, the authors discussed a framework using an artificial neural network (ANN)-based greedy algorithm which combines context-aware design-specific data obtained from immersive virtual environments (IVEs) with an existing BPM to enhance the simulations of human-building interactions in new designs. Although the framework has revealed the potential to improve simulations, it cannot determine the appropriate combination between context-aware design-specific data and the existing BPM.In this paper, the authors present a new computational framework (the GAN-based framework) to determine an appropriate combination based on a given performance target to achieve. Generative adversarial networks (GANs) are used to combine data of an existing BPM and context-aware design-specific data using a performance target as a guide to produce an augmented BPM. The effectiveness and the reliability of the GAN-based framework were validated using an IVE of a single occupancy office. Thirty people participated in an experiment on the simulation of artificial lighting switch uses using the IVE. Their light switch uses data under different work area illuminance were collected and analyzed. The building performance models (BPMs) proposed by Hunt and Da Silva were selected as the existing BPM and the performance target respectively. The data of each participant was used to generate an augmented BPM using the GAN-based framework and an updated BPM using the previous framework (i.e., ANN-based greedy algorithm framework). The thirty pairs of the augmented and updated BPMs were compared. Specifically, the errors measured between the updated BPMs and the performance target (E1) and the errors measured between the augmented BPMs and the performance target (E2) were analyzed using t-tests (α = 0.05). In 22 out of 30 cases, the performance of the augmented BPMs was significantly better than the updated BPMs, and in four cases, the performance of the two was similar. Only in four other cases, the performance of the updated BPMs was better. The results confirmed the efficacy of the framework. However, future research is needed to study the performance target and uncertainties associated with IVE experiments to better understand and control the reliability of the framework.

An Expandable, Contextualized and Data-Driven Indoor Thermal Comfort Model

Abstract Continuous discrepancies in building performance predictions creates an ongoing inclination to link contextualized, real-time inputs and users’ feedback for not only building control systems but also for simulation tools. It is now seeming necessary to develop a model that can record, find meaningful relationship and predict more holistic human interactions in buildings. Such model could create capacity for feedback and control with a level of intelligence. Fuzzy Logic Systems (FLSs) are known as robust tools in decision making and developing models in an efficient manner. Considering this capability, in this paper, FLSs is implemented to make a thermal comfort model in an educational building in the UK. Such implementation has an ability to respond to some identified desires of developers and performance assessors in addressing uncertainty in thermal comfort models. The results demonstrate the proposed method is practical to simulate the value of comfort level based on the input data.

A stepwise approach for assessing the appropriate occupant behaviour modelling in building performance simulation

ABSTRACTOccupant behaviour (OB) is recognized as a leading source of uncertainty in building performance predictions. Neglecting the potential influence of uncertainties on building performance could result in erroneous decision-making during the design phase. Therefore, it is essential that uncertainties are appropriately considered within building performance simulation (BPS) models. As for OB, there are various approaches to model occupant presence and actions in BPS tools. Literature shows that the appropriate modelling approach depends on the object and purpose of the simulation, which makes it difficult to favour a method over another a priori. Moreover, there is very little support for selecting the most appropriate modelling approach. As a result, OB is modelled in practice in various ways, mostly dictated by intuition and habit. This study builds on previous literature to introduce and test a complete approach for appropriate OB modelling. The method can be generalized and readily applied to design questions.