Reduce Building Energy Consumption Research Articles

Net-positive office commissioning and performance gap assessment: Empirical insights

During commissioning activities, many decisions can be made to reduce building energy consumption and help building operation to meet design goals. Decisions made in this period include reducing the temperature in unoccupied spaces such as mechanical equipment rooms, reducing the setpoints on specific equipment (e.g., amount of fresh air entering the building) and resizing equipment, such as pumps, to better meet operational and user needs. Energy meter data from January to December 2019 was compared with the first six months of 2022 to quantify the impact of commissioning decisions on building energy consumption. Interviews with key informants such as the building operator and energy advisor, were conducted to gain a holistic understanding of operational decisions. It was approximated that building commissioning activities, primarily HVAC, reduced building energy consumption (BEC) by 15% per year. Lastly, it was found that building operator expertise and the tools (e.g., live data from energy meters) available to them improved the efficiency and effectiveness of commissioning activities and ongoing operational decisions.

Optimizing the Air Conditioning Layouts of an Indoor Built Environment: Towards the Energy and Environmental Benefits of a Clean Room

Reducing energy consumption in buildings has received intensified research impetus since the introduction of the decarbonization goals set in the Paris agreement. Many domestic and specialized applications require clean rooms (indoor built environments) for safe and clean operation. Energy efficiency in clean room spaces depends on maintaining livable or required conditions such as temperature, humidity, and particle concentration with minimal use of energy and new carbon dioxide (CO2) emissions. In the literature, parameters such as temperature, relative humidity, particle concentrations, and CO2 emissions are not able to be properly controlled in clean room systems. The designed system in the literature involves high energy consumption and high economic costs. All these factors add novelty to this research, which was a significant research gap in previous studies. This clean room is directly linked to environmental parameters such as ambient temperature, relative humidity, etc. The clean room is also related directly to the building and infrastructure in such a way that there are certain regulatory requirements for designing a clean room. For designing and constructing the controlled environment in a clean room, the English (EN) documents, ISO 9000, and various other standards allow for clean rooms for different types of products. In this research, the designed control configurations properly control the system. Additionally, this system is energy efficient, with positive environmental aspects regarding CO2 emissions. Three control configurations were designed in this research, option A, option B, and option C, and three parameters are controlled in the study. These parameters are room temperature, relative humidity, and CO2 emissions (outside the room). CO2 emissions are controlled outside the room (in the environment). In the last research phase, a comparative analysis of these three control configurations was performed to find an energy-efficient system with fewer CO2 emissions. Control configuration B (option B) provides reliable results regarding an energy-efficient system and fewer CO2 emissions emitted to the environment. In this study, an optimized configuration for the air conditioning system was developed for a clean room (volume 185.6 m3) with a required temperature of 23 °C, relative humidity of 40%, and a particle size of less than 0.3 μm. Three different design configurations were analyzed using TRNSYS simulation software. The minimization of energy use and CO2 emissions were the objective functions. Energy loads were calculated for each of the configurations by varying the fixed air change per hour and the minimum outdoor air flow rate. The results of a whole year simulation run for control configurations A, B, and show that, on the one hand, the ambient weather conditions of temperature and relative humidity (RH) is varied throughout year and, on the other hand, the clean room temperature was maintain at exactly 23 °C, which is the required set point temperature, for all the three configurations (A, B, and C). Furthermore, the clean room relative humidity was maintained at 36% for configuration A, below the 40% which was the set point for clean room relative humidity, and at 40% for configurations B and C. Configuration B exhibited the minimum energy use (7300 kWh), at a fixed air change per hour value of 20 and a minimum outdoor air flow rate of 150 L/s, with the least amount of CO2 emissions, offering an overall 25% improvement over configurations A and C.

A Robust Artificial Intelligence Approach with Explainability for Measurement and Verification of Energy Efficient Infrastructure for Net Zero Carbon Emissions

Rapid urbanization across the world has led to an exponential increase in demand for utilities, electricity, gas and water. The building infrastructure sector is one of the largest global consumers of electricity and thereby one of the largest emitters of greenhouse gas emissions. Reducing building energy consumption directly contributes to achieving energy sustainability, emissions reduction, and addressing the challenges of a warming planet, while also supporting the rapid urbanization of human society. Energy Conservation Measures (ECM) that are digitalized using advanced sensor technologies are a formal approach that is widely adopted to reduce the energy consumption of building infrastructure. Measurement and Verification (M&V) protocols are a repeatable and transparent methodology to evaluate and formally report on energy savings. As savings cannot be directly measured, they are determined by comparing pre-retrofit and post-retrofit usage of an ECM initiative. Given the computational nature of M&V, artificial intelligence (AI) algorithms can be leveraged to improve the accuracy, efficiency, and consistency of M&V protocols. However, AI has been limited to a singular performance metric based on default parameters in recent M&V research. In this paper, we address this gap by proposing a comprehensive AI approach for M&V protocols in energy-efficient infrastructure. The novelty of the framework lies in its use of all relevant data (pre and post-ECM) to build robust and explainable predictive AI models for energy savings estimation. The framework was implemented and evaluated in a multi-campus tertiary education institution setting, comprising 200 buildings of diverse sensor technologies and operational functions. The results of this empirical evaluation confirm the validity and contribution of the proposed framework for robust and explainable M&V for energy-efficient building infrastructure and net zero carbon emissions.

MTL-Deep-STF: A multitask learning based deep spatiotemporal fusion model for outdoor air temperature prediction in building HVAC systems

Buildings consume large quantities of energy. Reducing building energy consumption is essential to achieving carbon neutrality goals. Building energy consumption is strongly influenced by various meteorological factors for which outdoor air temperature is a proxy. This study proposes a data-driven multitask-learning deep spatiotemporal fusion model (MTL-Deep-STF) that predicts outdoor air temperature to provide baseline data against which improvements to conserve energy in heating, ventilation, and air conditioning (HVAC) systems can be proposed and assessed. MTL-Deep-STF is a generic multiple input–multiple output model that predicts temperatures for successive time points in separate high-level tasks. Each task uses a multilayer perceptron to combine meteorological data from neighboring observation stations. CNN-GRU is used to extract spatially local long-term and short-term temporal features that are shared between different tasks via CNN. Comparative validation experiments using real world datasets for complex terrains showed that the model was highly accurate in both single step and multistep predictions. One-step-ahead predictions for winter (MAE = 0.428 °C, RMSE = 0.705, Acc ≤2 °C = 97.832%) and summer (MAE = 0.467 °C, RMSE = 0.706, Acc ≤2 °C = 97.899%) datasets were particularly accurate. This accuracy was coupled with high robustness ( R 2 = 0.976 for winter and R 2 = 0.981 for summer). Use of the model can conserve building energy. • The spatiotemporal fusion model MTL-Deep-STF uses data from neighboring stations to predict air temperature at multiple future time steps. • MTL-Deep-STF treats temperature prediction for successive prediction horizons as distinct tasks. • MTL-Deep-STF extracts global spatial features of multiple stations through fully connected networks. • MTL-Deep-STF is extremely accurate and robust in predicting outdoor temperatures and is therefore suitable for controlling HVAC systems, particularly in complex terrains.

Preparation and dynamic moisture adsorption of fiber felt/silica aerogel composites with ultra-low moisture adsorption rate

Understanding the effects of preparation parameters on thermal insulation materials is important for reducing building energy consumption. In this study, fiber felt/silica aerogel composites with ultra-low moisture adsorption rate were prepared by the sol–gel and atmospheric drying technology. The effects of relative humidity and preparation parameters (e.g., silica sol ratios, volume ratios of the sol to fiber felt, aging duration, and modification duration) on the performance (thermal conductivity and moisture adsorption rates) of silica aerogel composites were explored. Furthermore, an isothermal adsorption model of silica aerogel composites for water vapor was established. The results showed in the environment with high relative humidity of 90 %, the moisture adsorption rate and thermal conductivity of the composites were approximately 1 % and 0.02 W·m−1·K−1, respectively. However, the moisture adsorption rate and thermal conductivity of the fiber felt were up to 20 % and 0.1687 W·m−1·K−1, respectively. With the change in the sol ratio, the moisture adsorption rate of the composites varied between 0.7 % and 0.8 % after reaching the adsorption equilibrium. The isothermal adsorption process was described by the D–A isothermal adsorption equation with the correlation of more than 0.99. The prepared fiber felt/silica aerogel composites with ultra-low moisture adsorption rate can be widely used for building insulation, especially in humid areas. This study provides the theoretical data reference for applying new aerogel composites insulation materials in high-humidity environments.

Preparation of hierarchical porous microspheres composite phase change material for thermal energy storage concrete in buildings

Phase change materials with high latent heat significantly reduce building energy consumption. However, the serious leakage issue, low thermal conductivity, and the poor photothermal response utterly hinder their wide application in architecture. We reported an effective strategy for constructing hierarchical porous composite microspheres (PCN) through spray drying, calcination, and acid activation, using palygorskite (Pal) as the raw material. PCN presented a spherical hierarchical porous structure constructed by crosslinking Pal nanofibers and cellulose nanocrystals in a particular proportion. Paraffin-PCN (P-PCN) composite phase change materials (PCMs) with high shape stability, excellent photothermal conversion ability and latent heat storage capacity were synthesized. The heat preservation time of the P-PCN natural cooling from 35 °C to 30 °C is approximately twice that of the P-Pal. The P-PCN indicates obvious advantages in building thermal management, with the melting enthalpy promoted to 130.2 J/g. Further, the P-PCN-based building materials (P-PCN-B) with the P-PCN and the building aggregate maintain superior light-thermal energy conversion and thermal properties after multiple cycles. The P-PCN-B indicates outstanding mechanical properties (compression strength reaching 14.2 MPa) and flame-retarded properties. This work provides an innovative design strategy for developing multifunctional intelligent energy storage concrete and paves the way for the sustainable utilization of energy storage materials.

Positivity and difference of influence of built environment around urban park on building energy consumption

Built environment can improve thermal comfort and reduce building energy consumption. Among these, urban parks are considered as a major strategy for reducing building energy consumption. Understanding the relationship of built environment around the urban park on building energy consumption is thus critical. This study established grid groups with different built environment layouts around urban park from the perspective of 3D building characteristics, land cover and road network. Then the correlation relationship between built environment and building energy consumption was examined by using the spatial regressive model, and the positivity and difference of building environment effect on building energy consumption were analyzed and compared emphatically. The results showed that water body and road intersection can weaken the positive effect of impervious surface area (ISA) in compact high-rise building area, and water body can promote the negative effect of form factor (S/V) in sparse high-rise building area. Additionally, we found that the sparse high-rise buildings were conducive to the spread of cold air in the park compared to compact high-rise, which promoted the negative effects of S/V. Therefore, the built environment should be incorporated into urban planning to reduce building energy consumption.

Near Infrared Reflection and Hydrophobic Properties of Composite Coatings Prepared from Hollow Glass Microspheres Coated with Needle-Shaped Rutile Shell

Infrared thermal reflective coating is an effective material to reduce building energy consumption and carbon emission. In this work, needle-shaped-rutile-shell-coated hollow glass microbeads (HGM) were prepared by surface modification of HGM and thermohydrolysis of TiCl4, and the possible shell formation mechanism was also proposed. The near infrared (NIR) reflectance of the coated HGM reached 93.3%, which could be further increased to 97.3% after the rutile shell crystallinity was improved by heat treatment. Furthermore, HGM/styrene-acrylic composite reflective coating was prepared on the surface of gypsum board by facile blending and coating methods, and the thermal insulation performance was measured by an indigenously designed experimental heat set-up. The results show that the composite coating prepared by HGM coated with rutile shell shows better NIR reflectance and thermal insulation performance than that prepared by pure organic coating and uncoated HGM. Meanwhile, it also shows better surface hydrophobicity, which is conducive to long-term and stable infrared reflection performance.

Synthesis and evaluation of phase change material suitable for energy-saving and carbon reduction of building envelopes

Phase change materials (PCMs) reduce building energy consumption, but practical errors often occur due to material mismatches with the application environment. In this paper, taking Guangzhou as an example, to prepare PCMs with a phase transition temperature (TP) close to 26 °C, the Schrader equation is used, and therefore capric acid (CA) and stearic acid (SA) are selected as raw materials. The experimental results show that at a mass ratio (CA/SA) of 9:1, the PCMs can form a eutectic structure with TP and enthalpy of 24.9 °C and 157.3 J/g, respectively. Furthermore, the simulation results confirm that in the whole life cycle the energy-saving rate of proposed PCMs in Guangzhou is high at about 20 %. The study also provides recommendations for optimizing PCMs use in the hot summer and warm winter climates, i.e. on the outside of the envelope, especially on the west wall and roof, increasing the PCMs amount can better save energy. These results are a guideline on building envelope energy efficiency by using PCMs, which can be applied in the optimization of PCMs selection in different climatic zones.

Multiobjective optimization of building energy consumption and thermal comfort based on integrated BIM framework with machine learning-NSGA II

Detailed parametric analysis and measurements are required to reduce building energy usage while maintaining acceptable thermal conditions. This research suggested a system that combines Building Information Modeling (BIM), machine learning, and the non-dominated sorting genetic algorithm-II (NSGA II) to investigate the impact of building factors on energy usage and find the optimal design. A plugin is developed to receive sensor data and export all necessary information from BIM to MSSQL and Excel. The BIM model was imported to IDA Indoor Climate and Energy (IDA ICE) to execute an energy consumption simulation and then a pairwise test to produce the sample data set. To study the data set and develop a prediction model between building factors and energy usage, 11 machine learning algorithms are used. The best algorithm was Group Least Square Support Vector Machine (GLSSVM), later employed in NSGA II as the building energy consumption fitness function using Dynamo software. An NSGA II multi-objective optimization model is designed to reduce building energy consumption and optimize interior thermal comfort (measured by the predicted percentage of dissatisfied (PPD)). The Pareto front is calculated, and the optimum point approach is used to find the best combination of building envelope characteristics, HVAC setpoints, shading parameters, lighting, and air infiltration. The feasibility and effectiveness of the developed framework are demonstrated using a case study of an upper secondary school building in Norway; the results show that: (1) The GLSSVM has a unique capacity to forecast building energy use with high accuracy: R2 of 0.99, an RMSE of 1.2, MSE of 1.44, and MAE of 0.89; (2) Building energy consumption and thermal comfort may be successfully improved by the GLSSVM-NSGA II hybrid technique, which reduces energy consumption by 37.5% and increases thermal comfort by 33.5%, respectively.

Selectively Harvesting Nonvisible Photons in Hybrid Solar Lighting Systems for Power Generation in Buildings

Hybrid solar lighting (HSL) systems reduce building energy consumption by supplementing conventional indoor lighting with solar light that is channeled into the building using optic cables. Herein, it is demonstrated that HSL systems can be improved by harnessing the nonvisible portion of sunlight to generate electric power. HSL systems are integrated with parabolic‐ and elliptic‐shaped solar spectrum splitters (SSSs) that partition collimated solar radiation into its visible and nonvisible parts. In the proposed SSS, luminescent materials and a bifacial photovoltaic cell capture and convert light energy in the spectral ranges from 280 to 435 nm and 675 to 900 nm into electricity. The visible light is coupled to a waveguide to be used in indoor lighting systems. The proposed HSL system is analyzed for a 0.2 m2 projected area of solar radiation and the results show that a nonvisible‐light‐to‐electricity conversion efficiency of up to ≈13.4% can be achieved for light entering the SSS. The power generated in the system can be used to power the electrical components of the HSL system and indoor lighting during off‐sun hours.

Recent Progress on Preparation Strategies of Liquid Crystal Smart Windows

Liquid crystal (LC) smart windows that are able to regulate natural light by changing the optical transmittance in response to external stimulus have become an effective way to reduce building energy consumption. The rapid development of technology has brought out a variety of responsive smart windows suitable for daily life, including electrical-, thermal-, and photo-responsive ones. In this review, the recent progress in LC smart windows that switch between transparent and opaque states by different stimuli is overviewed. The preparation strategies for single-/dual-responsive smart windows are outlined, exclusively concentrating on the functional design and working principle. Furthermore, the advantages and current drawbacks of smart windows for each response mode are briefly described. Finally, a perspective on the direction of future responsive LC smart windows is discussed.

Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends

Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas emissions. Furthermore, HVAC energy demand is expected to rise in the future. Therefore, advancements in HVAC systems’ performance and design would be critical for mitigating worldwide energy and environmental concerns. To make such advancements, energy modeling and model predictive control (MPC) play an imperative role in designing and operating HVAC systems effectively. Building energy simulations and analysis techniques effectively implement HVAC control schemes in the building system design and operation phases, and thus provide quantitative insights into the behaviors of the HVAC energy flow for architects and engineers. Extensive research and advanced HVAC modeling/control techniques have emerged to provide better solutions in response to the issues. This study reviews building energy modeling techniques and state-of-the-art updates of MPC in HVAC applications based on the most recent research articles (e.g., from MDPI’s and Elsevier’s databases). For the review process, the investigation of relevant keywords and context-based collected data is first carried out to overview their frequency and distribution comprehensively. Then, this review study narrows the topic selection and search scopes to focus on relevant research papers and extract relevant information and outcomes. Finally, a systematic review approach is adopted based on the collected review and research papers to overview the advancements in building system modeling and MPC technologies. This study reveals that advanced building energy modeling is crucial in implementing the MPC-based control and operation design to reduce building energy consumption and cost. This paper presents the details of major modeling techniques, including white-box, grey-box, and black-box modeling approaches. This paper also provides future insights into the advanced HVAC control and operation design for researchers in relevant research and practical fields.

An office building energy consumption forecasting model with dynamically combined residual error correction based on the optimal model

Accurate forecasting of energy consumption in office buildings is of great importance for optimal management of energy consumption and reduction of building energy consumption. A variety of combination forecasting models (FMs) have become the current research hotspots in the field of building energy consumption forecasting. For the problems of large systematic errors and poor generalization ability of existing combination FMs, this paper proposes a dynamic combination residual forecasting model (FM) with the optimal combination approach. Firstly, support vector regression (SVR) is selected as the basic FM, and the SVR residual errors are forecasted by the dynamic combination FM based on the weights, and the SVR forecast value is finally corrected. Further, the basis for the selection of the single FM in the combination model and the optimal number of combination terms are given by mathematical proof in this paper. A case study in Xi’an shows that the dynamic combined residual errors correction FM with the optimal number of terms proposed in this paper can reduce the mean absolute error (MAE) of the basic model from 1918.59 kW to 349.37 kW, the mean absolute percentage error (MAPE) from 15.80% to 2.96%, and the root mean square error (RMSE) from 2278.74 to 471.44.

Seasonal variation in leaf area index and its impact on the shading effects of vertical green facades in subtropical areas

Vertical green facades (VGFs) are considered promising design strategies for reducing building energy consumption. The leaf area index (LAIv) has been considered a critical parameter in evaluating the thermal behaviour of VGFs. The LAIv is not static for a plant and changes over seasons. However, research on the seasonal variation of the LAIv and its impact on the shading effects of VGFs is limited. The present study addresses this research gap. First, a shading coefficient (SC) model was developed to describe the capability of VGFs to intercept solar radiation. Then, the SC and one-year LAIv variations of two deciduous plants and one evergreen plant were observed to verify the accuracy of the SC model and develop the seasonal LAIv prediction (SLP) model. Finally, the SLP model combined with local climate parameters was employed to analyse the solar heat gain on the east, south, west, and north walls with and without VGFs. The results indicated that the VGFs composed of deciduous plants could be considered a self-adjusting shading system, intercepting over 90% of incident solar radiation in summer and transmitting approximately 75% of incident solar radiation in winter.

In situ synthesis of highly dispersed VO2(M) nanoparticles on glass surface for energy efficient smart windows

Thermochromic VO2-based smart windows are considered as a highly promising building envelope to reduce building energy consumption. However, the inherent unsatisfactory optical performance hindered its practical applications and nanocomposite films are testified to be the most promising strategy addressing this issue, while uniformly dispersing nanoparticles is often a challenge. Herein, a facile in situ method was proposed to directly fabricate highly dispersed VO2 nanoparticles (HD-VN) on a glass surface from the VOC2O4·xH2O aqueous solution to achieve the high-optical performance VO2 film (HD-VN film). The forming mechanism of HD-VN was fully elucidated and HD-VN was generated via the sintering process from the irregular VO2 particles derived from the decomposition of polyvinyl pyrrolidone (PVP). The HD-VN film showed excellent luminous transmittance (Tlum) of 72.5% and solar energy modulation efficiency (ΔTsol) of 10.1%, the former was attributed to the reduced refractive index which led to a reduced reflectance of HD-VN film, and the latter was ascribed to the localized surface plasmon resonance (LSPR) behavior of the HD-VN. The simple and cost-efficient synthesis strategy would promote the application of VO2 film in energy-efficient glazing, and also shed light on the structures’ design applied to the field of optics, catalysts, and sensors.

Tripartite Evolutionary Game Analysis of Stakeholder Decision-Making Behavior in Energy-Efficient Retrofitting of Office Buildings

Environmental degradation is significantly influenced by the construction industry. Energy-efficient retrofitting of existing office buildings has become an effective means of reducing building energy consumption. Implementation of retrofits requires the support and cooperation of stakeholders. However, existing studies on the dynamics of decision-making behavior among stakeholders are still relatively limited. This study constructed a tripartite evolutionary game model including the government, property owners, and end users, to better understand the behavioral evolution and evolutionary stabilization strategies of stakeholders. The results show that: stakeholders’ decision-making behavior has obvious mutual influence; benefits and costs are the dominant factors in stakeholders’ decision making; the effects of government supervision policies depend on the profitability of the project; and government behavior appears to be influenced by public willingness. In addition, targeted countermeasures were proposed for the development of the energy-efficiency retrofit market. This study provides a generic model that fits various contexts and can be used to inform a reference for scientific decision making by stakeholders.

Evaluation of energy-saving retrofits for sunspace of rural residential buildings based on orthogonal experiment and entropy weight method

The retrofit of building envelopes in rural areas is a necessary measure to reduce building energy consumption. However, the traditional retrofit strategy is mostly a subjective decision, therefore, a scientific and objective evaluation method is proposed, which is a multi-factor and multi-objective preferred method based on orthogonal tests and entropy weights. The construction strategy for an additional sunspace of a typical rural residence in severe cold areas is studied. The results show that the glass type and the window-to-wall ratio have a remarkable influence on the optimization of the sunspace, accounting for 64.03 % and 17.42 %, respectively. Compared with the original rural building, the heating energy consumption of building with retrofit sunspace reduces by 12.82 %. Considering comprehensive energy saving, incremental cost, cost-benefit ratio and carbon emission reduction, the optimal construction strategy is the scheme 13, which has three layers of insulating glass with a window-to-wall ratio of 0.9, a depth of 0.9 m, 60 mm foamed concrete insulation board on the roof, 20 mm extruded polystyrene thermal insulation board on the ground and 120 mm foamed concrete insulation board on the adjacent south wall. The weights of the aforementioned parameters are 0.2967, 0.1916, 0.2021, and 0.3095, respectively, which are calculated by the entropy weight method.

Long-wave infrared radiation properties of vertical green façades in subtropical regions

Applying vertical green facades (VGFs) on building envelopes is an effective design strategy to reduce building energy consumption and mitigate the urban heat island effect. Although many studies have investigated the cooling and energy-saving effects of VGFs, research on the long-wave infrared radiation properties of VGFs remains scarce, limiting the understanding and application of heat-transfer models of VGFs. Therefore, in this study, theoretical analyses and field measurements were performed in the subtropical city of Guangzhou, China, to examine the long-wave infrared radiation properties of VGFs. First, an observation platform was established using six plants commonly used for VGFs in subtropical regions. The long-wave infrared radiation and leaf temperature of these plants with different coverage rates were measured to investigate the effect of coverage rate on the radiation transfer of the vegetation canopy. A Fourier spectrometer was used to analyse the spectral features of the leaves in the long-wave infrared radiation band. Based on the observation data and radiation balance of the vegetation canopy, an empirical equation for transmitting long-wave infrared radiation was established. Moreover, the monthly variations in the leaf area index and coverage rate of the VGFs were recorded throughout 2021, and an exponential function was used to determine the relationship between them. A quantitative method was used to assess the accuracy of the long-wave infrared radiation model, and the results indicated that the calculated values were in good agreement with the measured values.

An energy analysis of translucent concrete embedded with inclined optical fibers

Translucent concrete (TC) is a kind of energy-efficient building envelope, which is able to transmit daylight into the interior of a building efficiently with improved thermal performance. Previous research shows that a kind of novel TC embedded with inclined optical fibers (OFs) is promising to achieve better climate-responsive transmittance and improved energy-saving potential compared with conventional TC. However, the newly proposed TC differs from traditional one in both light and heat transfer processes, and its distinctive optical and thermal performance have not been fully revealed. To bridge this knowledge gap, an improved optical and thermal model of the novel TC was developed and validated with experimental data. A parametric study was conducted to reveal the effects of optical fiber inclination angle and numerical aperture (NA) on the TC. The results show that the climate-responsive transmittance of TC can be improved by inclining OFs, and this can serve as a feasible method for TC transmittance adjustment especially when OFs with high NA values are not available or not cost-effective. The optimal optical fiber inclination angle is about 10° for a south-facing TC wall in Beijing, which is able to reduce building energy consumption by about 4.98% to 21.88% depending on the coefficient of performance (COP) of heating, ventilation and air conditioning systems.