Total Building Energy Consumption Research Articles

Design, preparation, and property analysis of metal/dielectric multilayer film with wavelength selectivity

Metal/dielectric multilayer films have important applications in energy-saving glass, stealth materials, solar energy utilization and other fields. In the current study, the thickness of each layer of TiO2/Ag/TiO2/Ag/TiO2 film is optimized. The effects of the number of metal/dielectric multilayer films and the incident light angle on their optical properties were investigated. The TiO2/Ag/TiO2/Ag/TiO2 film was prepared by electron beam evaporation coating technology, and their reflectance and transmittance were measured. The measurement results show that the visible light transmittance (380–780 nm) of the film can achieve 68.7%, and the infrared reflectance (780–2500 nm) can reach 95.9%. Compared with the traditional dielectric/metal/dielectric three-layer film, the visible light transmittance of the film is higher, and the solar infrared reflectance is greatly improved. In the solar radiation band (280–2500 nm), the average error between the experimental reflectance and transmittance and the theoretical prediction results is less than 0.03. The distribution of electric and magnetic fields inside the film was simulated by finite-difference time-domain method. The simulation results show that the high visible light transmittance is due to the interference resonance of electromagnetic waves inside the film. Taking Shanghai as an example, under our calculation conditions, compared with ordinary SiO2 glass, TiO2/Ag/TiO2/Ag/TiO2 film can reduce the total energy consumption of buildings by 14.3% and refrigeration energy consumption by 17.2%.

Modelling building HVAC control strategies using a deep reinforcement learning approach

Heating, ventilation, and air-conditioning (HVAC) systems are responsible for a considerable proportion of total building energy consumption but are also vital for improved indoor temperature comfort, indoor air quality and well-being of building occupants. Thus, developing control strategies for HVAC systems is critical for the total life cycle of any building projects. Particularly, HVAC and building operations are not stationary but are filled with fuelled by environmental dynamisms and unexpected disruptions such as users' activities, weather conditions, occupancy rate, and operation of machinery and systems. This research aims to develop and propose a strategic control learning framework for HVAC systems using the deep reinforcement learning (DRL) approach. The results show that the proposed Phasic Policy Gradient (PPG) based method is more adaptive to changes in real building's environments. Notably, PPG performs better and more reliable than the conventional method for HVAC control optimization with about 2-14% in energy consumption reduction and indoor temperature comfort enhancement, along with a 66% faster convergence rate. Overall, our findings demonstrate that our proposed DRL approach is less resource intensive and much easier than the conventional approach in deriving solutions for HVAC control optimization driven by energy efficiency and indoor temperature comfort.

A comprehensive investigation of zeolite/polyurea cooling coating on concrete for building energy conservation

Passive daytime radiative cooling (PDRC) materials are beneficial to energy conservation and emission reduction of buildings. Currently, the design of PDRC coatings for exterior walls and roofs of buildings places excessive emphasis on improving the solar reflectance (Rs) and infrared emissivity (Ei) of the coating, while neglecting other important properties of building coatings, such as adhesion, waterproofness, corrosion resistance, and weather resistance. To address these deficiencies, this work developed a (calcined zeolite)/polyurea (CZ/PU) PDRC coating with excellent comprehensive performance for building exterior walls and roofs, using CZ and PU as raw materials. The CZ/PU coating is prepared using a method that is completely consistent with existing paint production and construction techniques, and can be applied to large-area building surfaces at scale. The Rs of the CZ/PU coating is 97.4 %, and the Ei is 94.4 %. Under direct sunlight in summer, the CZ/PU coating achieved significant PDRC effects under both adiabatic conditions (using test equipment to reduce heat conduction and convection) and natural conditions. The corresponding radiative cooling temperatures are 14.9 °C and 4.4 °C, respectively. In addition, the CZ/PU coating also exhibits excellent water resistance, reducing the capillary water absorption rate of a cement block by 78 %. In addition, the adhesion strength of the CZ/PU coating to the substrate reached the highest rating (rating 0). In particular, the optical properties, radiative cooling function, water absorption, and adhesion force of the CZ/PU coating showed ultra high stability in ultraviolet aging experiments. Energy consumption simulations show that the average energy saving efficiency of the CZ/PU coating on the annual total building energy consumption of ten typical Chinese cities is as high as 13.3 %. The CZ/PU-based coating developed in this work comprehensively considers various issues that may be faced in the practical application of radiative cooling architectural exterior wall coatings, providing an experimental foundation for promoting the application of radiative cooling coatings in buildings.

Nearly Zero-Energy Building Load Forecasts through the Competition of Four Machine Learning Techniques

Heating, ventilation and air conditioning (HVAC) systems account for approximately 50% of the total energy consumption in buildings. Advanced control and optimal operation, seen as key technologies in reducing the energy consumption of HVAC systems, indispensably rely on an accurate prediction of the building’s heating/cooling load. Therefore, the goal of this research is to develop a model capable of making such accurate predictions. To streamline the process, this study employs sensitivity and correlation analysis for feature selection, thereby eliminating redundant parameters, and addressing distortion problems caused by multicollinearity among input parameters. Four model identification methods including multivariate polynomial regression (MPR), support vector regression (SVR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) are implemented in parallel to extract value from diverse building datasets. These models are trained and selected autonomously based on statistical performance criteria. The prediction models were deployed in a nearly zero-energy office building, and the impacts of feature selection, training set size, and real-world uncertainty factors were analyzed and compared. The results showed that feature selection considerably improved prediction accuracy while reducing model dimensionality. The research also recognized that prediction accuracy during model deployment can be influenced significantly by factors like personnel mobility during holidays and weather forecast uncertainties. Additionally, for nearly zero-energy buildings, the thermal inertia of the building itself can considerably impact prediction accuracy in certain scenarios.

An energy-saving design method for residential building group based on convolutional neural network

Current building energy saving methods mainly focus on the individual building, seldom focus on the building group. Different from the energy saving design method which takes the individual building as the object, the energy saving design of building group also needs to consider the influence of microclimate on building energy consumption, because microclimate is the boundary condition for building energy consumption calculation. The purpose of this study is to investigate the influence of the building group form and geometric factors on the energy consumption of residential buildings in Beijing. First, this study developed a complex plane generator based on convolutional neural network (CNN), and used the generator to extract and classify the plane of residential buildings in Beijing, and generate three standard residential building models. Secondly, the microclimate conditions of the three standard models in summer and winter were calculated by ENVI-met, and the energy consumption of individual building with different geometric form was calculated by DesignBuilder. Based on the above simulation results, the influence of group forms and geometric factors on microclimate and the influence of geometric form on energy consumption of individual building were quantified, and the influence law of group forms and geometric factors on energy consumption of buildings was revealed. The obtained law can be used to quickly estimate the change of total building energy consumption when the group form or geometric factor changes at the initial stage of the design of residential buildings.

Problems of Building Energy Consumption in Rural Schools in Linzhi, Tibet

The energy-saving research of school buildings in villages and towns in the severe cold areas of Linzhi Township has not yet been widely carried out. The energy consumption of schools in villages and towns in the severe cold areas of Linzhi is generally high, and the heating energy consumption in winter accounts for a large proportion of the energy consumption of school buildings. With the promulgation and implementation of energy-saving design standards for public buildings in schools in large and medium-sized cities, energy-saving activities have been carried out. In the severe cold area of Linzhi, there are a large number of buildings in villages and towns, with primary schools in each village, secondary schools and central primary schools in each township. The total energy consumption of village and town school buildings accounts for the majority of the total energy consumption of village and town public buildings. From this, it can be seen that studying the characteristics of village and town school buildings in the severe cold area of Linzhi and researching energy-saving design strategies suitable for their own characteristics based on the characteristics of village and town school buildings in the severe cold area are of great significance for the promotion and application of clean energy in schools in the Linzhi area.

Impact of air-conditioning usage behavior of student groups on air-conditioning load simulation in university dormitories

During summers, air-conditioning (AC) accounts for a large proportion of the total energy consumption of university dormitory buildings, which is influenced by the occupant behavior. To quantitatively analyze the impact of the AC usage behavior of student groups on AC loads, this study regarded students as heterogeneous individuals, and classified them into four categories by taking group identity and individual preferred temperature as classification indicators, as follows: (i) low-group identity and low-preferred temperature, (ii) low-group identity and high-preferred temperature, (iii) high-group identity and low-preferred temperature, and (iv) high-group identity and high-preferred temperature. On this basis, a combined model composed of the student groups’ AC usage behavior simulation and the AC load calculation was developed. Results showed that the subject of low-group identity and low-preferred temperature was most sensitive to changes in indoor temperature. On a typical summer day, the AC load simulation results without considering differences in individual behavior increased by 9.7% compared with those considering differences in individual behavior, and the increased rate declined with the rise in outdoor temperature. Additionally, enhancing students’ group identity could significantly reduce AC loads.

Synthesis of model predictive control based on neural network for energy consumption enhancement in building

The problem addressed in this work is the enhancement of energy efficiency in buildings, primarily focused on heating control. The paper highlights the importance of addressing this challenge, given that HVAC systems are responsible for over 60% of total energy consumption in buildings, making them the largest energy consumers in both the residential and nonresidential sectors. This paper presents a methodology for developing a model predictive control (MPC) system based on artificial neural network (ANN) models, with the aim of improving the energy efficiency of buildings. This approach involves the creation of a training dataset using dynamic thermal simulation tools, enabling the learning of the ANN model that is responsible for predicting future states within an MPC optimization loop. Additionally, the implementation leverages advanced technologies such as Field-Programmable Gate Arrays (FPGAs), specifically the PYNQ board, Wi-Fi modules, and MQTT communication to enhance scalability, deployment, and adaptability. The study evaluates the proposed Neural Network Model Predictive Control (NNMPC) strategy implemented on the PYNQ, reporting a substantial reduction of 38.53% in heating energy consumption in buildings compared to a basic On/Off control approach for 24-hour simulation. This outcome underscores the solutions significant potential for energy savings in building heating systems.

HVAC Load Forecasting Based on the CEEMDAN-Conv1D-BiLSTM-AM Model

Heating, ventilation, and air-conditioning (HVAC) systems consume approximately 60% of the total energy consumption in public buildings, and an effective way to reduce HVAC energy consumption is to provide accurate load forecasting. This paper proposes a load forecasting model CEEMDAN-Conv1D-BiLSTM-AM which combines empirical mode decomposition and neural networks. The load data are decomposed into fifteen sub-sequences using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The neural network inputs consist of the decomposition results and five exogenous variables. The neural networks contain a one-dimensional convolutional layer, a BiLSTM layer, and an attention mechanism layer. The Conv1D is employed to extract deep features from each input variable, while BiLSTM and the attention mechanism layer are used to learn the characteristics of the load time series. The five exogenous variables are selected based on the correlation analysis between external factors and load series, and the number of input steps for the model is determined through autocorrelation analysis of the load series. The performance of CEEMDAN-Conv1D-BiLSTM-AM is compared with that of five other models and the results show that the proposed model has a higher prediction accuracy than other models.

Performance evaluation of different building envelopes integrated with phase change materials in tropical climates

The need to improve building envelope components and reduce energy consumption is becoming increasingly crucial. The use of phase-change material (PCM) technologies is a viable solution to reduce energy consumption in buildings and associated greenhouse gas emissions. However, the performance of PCMs in buildings is strongly dependent on the melting temperatures and the climate conditions of the building's location. Therefore, the present study presents an optimisation-based approach to assessing the performance of building walls integrated with PCMs at different melting temperatures. To achieve this goal, a multiobjective genetic algorithm is used in conjunction with EnergyPlus building energy models to determine the optimal balance between total building energy consumption, lifecycle cost, and CO2 emissions. The proposed approach is applied to a single-family residential building located in six locations in the Central African sub-region classified as tropical savanna climate (Aw), hot semi-arid climate (Bsh), tropical rainforest climate (Af), and tropical monsoon climate (Am). Two different PCM technologies (InfiniteRPCM and BiocPCM) are applied to four wall types (brick, concrete block, cast concrete, and earth), and their parametric models are developed in EnergyPlus to optimise the melting temperature, thickness, and location of each PCM layer simultaneously. An optimisation is conducted for each selected wall and each location, and the optimised buildings are systematically compared to the reference buildings. The optimisation results showed that regardless of the climate zone and wall type, the application of PCMs with different melting temperatures significantly reduced energy consumption and CO2 emissions. Moreover, the results showed a different set of optimal solutions for each climate zone and wall type. The optimal solutions reduced the total energy, life cycle cost, and CO2 emissions by up to 47.80 %, 29.62 %, and 52.96 %, respectively.

Designing energy-efficient and visually-thermally comfortable shading systems for office buildings in a cooling-dominant climate

Windows and their control elements, including shading systems, are among the most critical building components affecting both energy consumption and occupant comfort. With the increasing demand for full-glazed facades, designers and researchers are actively devising advanced control strategies to address the challenges posed by excessive sunlight penetration and heat transfer. These strategies aim to harmonize the benefits of natural light with the need for comfortable indoor environments and energy consumption reduction. This study investigates the impact of external shading configurations for an office room in Tehran, categorized as group B in the Köppen climate classification, to reduce total building energy consumption and improve occupant thermal and visual comforts. The shading parameters include shading angle, shading depth, and the number of shading slats. More specifically, this study analyzes and compares two design configurations for the considered office room. The first configuration consists of a single southern window with horizontal shading, whereas the second configuration consists of two windows on the south and west, one being horizontal shading on the southern side and the other being vertical shading on the western side. There are a total of 1330 models investigated from which 20 models are selected as the most suitable solutions. Finally, this paper examines the effect of each design parameter on the overall performance of each configuration in terms of energy efficiency and visual-thermal comfort.

Self‐Adaptive Colored Radiative Cooling by Tuning Visible Spectra

Traditional radiative cooling leads to overcooling in the winter season and increases the total energy consumption of buildings. And, the super white color limits its practical applications. In this work, a bilayer coating consisting of a polydimethylsiloxane (PDMS)@thermochromic dye film as the top layer and a super white polytetrafluoroethylene film as the bottom layer is designed to achieve self‐adaptive colored radiative cooling by tuning visible spectra. The mass ratio of PDMS to the black thermochromic dye is 1: 0.04 to balance the solar heating and radiative cooling performances although the thermal emittance is constant (0.95). The designed black adaptive coating can achieve modulations of visible and solar reflectance from 0.36 to 0.91 and from 0.62 to 0.92, respectively. And its net power can switch from 331 W m−2 at −10 °C for heating to 70 W m−2 at 50 °C for cooling down. In the indoor and outdoor experiments, it is shown that adaptive coatings can achieve heating performance in a cold environment while cooling performance in a hot environment. Further energy consumption calculation indicates that adaptive coatings can save 5%−44% of energy consumption in different climates. Various colors and critical transition temperatures can be regulated by different thermochromic dyes for various scenarios.

Passive Window Energy Performance in Buildings: Modeling of Apartment Buildings in Indonesia

Along with urban growth in urban areas and energy consumption, which continues to increase every year, the selection of windows in the initial design is essential to obtain buildings that are not energy intensive. Selecting the correct window in the blueprint design reduces building energy consumption. Smart windows, especially thermochromic windows, are one of the most promising window technologies because they are the most economical and have passive control with zero energy input, which holds good promise for energy-saving applications. Apart from that, double-glazing windows are also frequently used in energy-saving applications. Therefore, a study compared the energy-saving potential of thermochromic and double-glazing windows to clear glass windows, using computer modeling through EnergyPlus, in high-rise apartment buildings in cities throughout Indonesia's diverse climates. From the modeling results, total energy consumption can be reduced by around 8.91% to 10.96% of total building energy consumption by replacing the conventional clear glass with double-glazing windows or more able to reduce about 20.22% to 24.19% by replacing the conventional clear glass with thermochromic windows. Furthermore, this potential varies depending on geometric shapes, materials, building facades, local climate, and building orientation. Nevertheless, considering the potential benefits, these windows are highly suitable for application in buildings seeking to reduce their energy consumption and improve energy efficiency.

Thermodynamic performance analysis of air-to-air hybrid heat recovery unit driven by pump combined with booster

The air conditioning energy consumption for fresh air handling in buildings increases yearly, especially in low energy buildings. This energy consumption can be greatly reduced when exhaust air is recovered for preheating or precooling fresh air. Then, significant savings in the total energy consumption in buildings will be achieved, which in turn helps achieve the carbon peak in the building field. In this paper, a booster/pump coupled energy recovery ventilator is proposed for energy recovery in buildings. The system performance, which depends on the loop number and combination strategies were analyzed and compared through model simulations. Results indicate that the heat transfer rate of different coupling systems increases with the temperature difference. In summer, the pump/booster driven dual-loop system has the highest average temperature effectiveness of 47.3% and average heat transfer rate of 4.97 kW. The booster/booster-driven dual-loop system has the highest average heat transfer rate of 5.07 kW and average temperature effectiveness of 47.1%. The average temperature effectiveness of the pump/booster/booster driven triple-loop system reaches up to 45% and average heat transfer rate of 4.73 kW. The booster/booster/booster-driven triple-loop system has the highest average heat transfer rate of 4.82 kW and average temperature effectiveness of 44.7%. In winter, the maximum average heat transfer rate and temperature effectiveness of the pump/booster driven dual-loop system are 8.54 kW and 44.44%, respectively. The maximum average heat transfer rate and temperature effectiveness of the pump/booster/booster driven triple-loop system are 7.90 kW and 41.5%, respectively. Throughout the year, the best coupling scheme of the dual-loop system is the pump/booster driven dual-loop system, and its average heat transfer rate and temperature effectiveness are 6.755 kW and 45.85%, respectively. The best coupling scheme of the triple-loop system is the pump/booster/booster-driven triple-loop system, and its average heat transfer rate and temperature effectiveness are 6.315 kW and 43.25%, respectively.

HVAC Systems Evaluation and Selection for Sustainable Office Buildings: An Integrated MCDM Approach

Heating, Ventilation, and Air-Conditioning (HVAC) systems are critical components of maintaining an indoor air quality that ensures the thermal comfort of occupants in diverse building types. However, HVAC systems are also responsible for a substantial portion of the total energy consumption of commercial and industrial office buildings. This paper presents an integrated approach of two powerful MCDM techniques: the Best-Worst-Method (BWM) and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in order to evaluate and rank HVAC systems to ensure the best selection toward designing a sustainable office building. A set of conflicting criteria reported by international sustainable assessment centers were employed along with various HVAC systems to develop what is called herein a BWM-based TOPSIS model in order to conduct such a novel research attempt. Within the context of the investigated office building herein, the mini-package ductless system appears to be the best choice. This study could be further enhanced by including additional criteria and a larger sample size in future studies.

A multi‐objective optimization approach to designing window and shading systems considering building energy consumption and occupant comfort

AbstractThe energy performance of a building has a significant impact on the environment as well as the comfort of its occupants. Given that window systems and control elements such as shading devices play a critical role in a building's energy consumption and comfort, this article presents an investigation into the optimization of windows in four different orientations, along the four cardinal directions, for a typical office room in Tehran. The objective is to reduce cooling, heating, and lighting energy consumption while improving occupant comfort simultaneously, using the NSGA‐II algorithm as a multi‐objective optimization method. To this end, first, an investigation into the effects of various parameters on total building energy consumption and occupant comfort is conducted. Subsequently, each parameter is evaluated in detail concerning its impact on each façade. As the final step, for each façade, the optimal solutions and the most undesirable scenarios are determined. The results, in which the possibility of glare is also considered, can be used by architects and designers for making informed design decisions. The results show that, in all orientations, the number of slats and their distance from the wall are the most effective parameters of shading configurations. Although the eastern and western windows are larger in this study, visual discomfort can be controlled using suitable shadings to achieve acceptable levels of visual comfort. Furthermore, north façades provide the least amount of thermal comfort and consume the least amount of energy while experiencing glare for longer periods.

Self-Powered Airflow Sensor Based on Energy Harvesting of Ventilation Air in Buildings

Heating, ventilation, and air conditioning (HVAC) systems account for one-third of the total energy consumption in office buildings. The use of airflow measurements to control the operation of HVAC systems can reduce energy consumption; thus, a sensor capable of monitoring airflow in a duct system is critical. Triboelectric nanogenerators (TENGs) can be utilized as self-powered sensors in airflow-driven TENGs (ATENGs) as self-powered sensors. By employing ferroelectric materials and surface modifications, the surface charges of TENGs can be increased. In this study, fibrous-mat TENGs were prepared using ferroelectric materials consisting of poly (vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) and polyamide 11 (nylon-11). And these materials were subsequently investigated. Poly (3,4-ethylenedioxythiophene):poly(styrenesulfonate) was added to PVDF-TrFE to enhance the ferroelectric crystalline phase. X-ray diffraction analysis revealed that this incorporation affects the β phase. In addition, the surface of nylon-11 was modified using the electrospray technique for post-treatment, thereby improving the interfacial adhesion between the fibers. These materials were then utilized in fibrous-mat ATENGs (FM-ATENGs) to demonstrate their practical application. The FM-ATENGs can be effectively used in an Arduino airflow-check sensor, showcasing their potential for application in HVAC systems, to enhance airflow control and energy efficiency.Graphical

Research on Passive Design Strategies for Low-Carbon Substations in Different Climate Zones

In the energy-saving design of substations, the building envelope thermal parameters, window-to-wall ratio, and shape factor are three crucial influencing factors. They not only affect the building’s appearance but also have an important impact on the total building energy consumption. In this paper, we applied the energy consumption simulation software DeST-c to study the influence of the above three elements on the total energy consumption of the building in a representative city with different thermal zones. The optimal envelope thermal parameters, optimal window-to-wall ratio, and optimal shape factor were derived through combination with economic analysis. Finally, the sensitivity analysis of different elements was carried out to determine the suitable passive design solutions for substations in different climate zones. It was found that the thickness of roof insulation has the greatest influence on the energy consumption of substation buildings among all envelopes. The optimal window-to-wall ratios were 0.4, 0.4~0.5, 0.3, 0.3~0.4, and 0.5 for severe cold, cold, hot summer and cold winter, hot summer and warm winter, and mild regions, respectively; and the optimal shape factors were 0.29, 0.30, 0.23, 0.31, and 0.33, respectively. The conclusions of this study can provide architects with energy-saving design strategies and suggestions for substations in different climate zones, and provide references for building energy-saving designs and air conditioning and heating equipment selection.

Impact of energy conservation measures in residential buildings in very remote communities in Australia

This study investigated the impact of various energy conservation measures (ECMs) on total annual electricity consumption on a single level, two-bedroom residential building in an arid climate and in a very remote area of Australia. Base case scenario of annual energy consumption profiles of the building and its existing systems were modelled and simulated using DesignBuilder software, and the results were validated by on-site measured electricity data. Two categories of ECMs (major intervention and minor intervention) were investigated and analysed. The findings show that the total annual energy consumption of the case study building can be reduced by up to 44% (2 kWh/m2·yr) when compared against the base model and measured data if selected ECMs are implemented. Significant savings from implementing selected ECMs can help alleviate the cost-of-living pressure and stress currently experienced by many households on low incomes especially for residents of very remote communities in Australia.

The Effect of Improper Interfering in The Historical Architecture on Energy Wasting (Case Study: Bibi-Roqayyeh House, Yazd, Iran)

Intelligent traditional architecture in Iran functions as a sustainable system with the goal to achieve thermal comfort. Buildings in Iranian architecture have a strong connection with the surrounding buildings, making the city an integrated whole. Generally, it is said that it is not possible to remove any part of this system without affecting the whole system’s performance. This research shows how removing some components of the system could affect the energy consumption of the whole. the question raised in our research and the answer obtained through this simulation is essential for controlling and optimizing interventions in historic cities. The case study is the Bibi-Roqayyeh traditional house in Yazd, Iran. First, the energy performance of the building under real conditions is simulated in Design-Builder software. Next, the energy performance of the building without the neighbors, the SABAT, and the traditional high Parapet are simulated. Overall, the simulation results show an increase in the total energy consumption of the main building after removing the selected components. The total energy consumption of the house is increased by 21.81% when all neighbors, the SABAT, and the old Parapet are removed. Each part affects the thermal comfort and energy consumption of the building, even if its primary function is not related to the thermal system.