Energy-efficient Architectural Design Research Articles

Energy-Efficient Architectural Design of a Banquet Hall with Integrated Tunnel Ventilation: Monitoring Performance During the Transitional Season in China

The construction industry, a significant contributor to global energy consumption and greenhouse gas emissions, is under considerable pressure to adopt transformative approaches. Public buildings, which account for a substantial portion of total energy usage, must balance high standards of thermal comfort with ventilation efficiency. In China, many public buildings are part of urban landscapes, where façade designs often limit natural ventilation. Consequently, technologies like earth-to-air heat exchangers and wind towers are increasingly essential for enhancing natural ventilation. However, research on the efficacy of these systems remains sparse. This study examines the transitional seasonal environment by evaluating the thermal-humidity index of a banquet hall equipped with an earth-to-air heat exchanger system. Using DeST software [DeST 2.0], the study simulates indoor natural ventilation, calculates ventilation rates, and assesses residual heat removal efficiency. The system’s performance is also modeled under various thermal design zones. Results demonstrate that under natural ventilation, the system can achieve a residual heat removal efficiency of up to 490%. Simulations across different climate zones indicate that the system performs best in regions with extreme temperature fluctuations, particularly those with hot summers and warm winters. In these areas, the system reduces the annual temperature difference by up to 56.7%, significantly improving thermal comfort and reducing dependency on air conditioning. In contrast, performance in milder regions like Kunming achieves only a 37.5% reduction in temperature difference. Overall, this study provides valuable insights into energy-efficient design strategies and thermal optimization for banquet halls, with significant potential for energy savings and enhanced occupant comfort.

Sustainable Thermal Comfort by Age Group in Shopping Malls: Multi-Year Winter Surveys in a Severely Cold Region

The current Chinese standard utilizes a single thermal comfort index guide to regulate indoor parameters for public buildings. However, heating, ventilation, and air conditioning (HVAC) settings often do not align with the occupant’s needs. To address this, a 2-year winter field survey was conducted in four large-scale shopping malls across severely cold regions of China, considering the complex age structure of mall visitors. Physical environmental parameters were measured, and a subjective questionnaire yielded 1464 valid responses. Neutral temperatures for different age groups were 17.4 °C for children (0–12 years of age), 19.3 °C for early youth (13–24), 20.0 °C for mature youth (25–44), and 23.3 °C for middle-aged adults (45–59). The limit of the 80% acceptable temperature range for the children and early youth was lower than the current Chinese GB 50736 standard, suggesting that HVAC temperatures for the corresponding shopping malls can be appropriately reduced for the target consumer groups. Significant differences occurred between customers’ demand for thermal environments in shopping malls and various standards. These findings provide valuable insights into energy-efficient architectural design and operational management of shopping malls in the Shenyang area, promoting the sustainable development of human thermal comfort environments.

Harnessing Deep Learning and Reinforcement Learning Synergy as a Form of Strategic Energy Optimization in Architectural Design: A Case Study in Famagusta, North Cyprus

This study introduces a novel framework that leverages artificial intelligence (AI), specifically deep learning and reinforcement learning, to enhance energy efficiency in architectural design. The goal is to identify architectural arrangements that maximize energy efficiency. The complexity of these models is acknowledged, and an in-depth analysis of model selection, their inherent complexity, and the hyperparameters that govern their operation is conducted. This study validates the scalability of these models by comparing them with traditional optimization techniques like genetic algorithms and simulated annealing. The proposed system exhibits superior scalability, adaptability, and computational efficiency. This research study also explores the ethical and societal implications of integrating AI with architectural design, including potential impacts on human creativity, public welfare, and personal privacy. This study acknowledges it is in its preliminary stage and identifies its potential limitations, setting the stage for future research to enhance and expand the effectiveness of the proposed methodology. The findings indicate that the model can steer the architectural field towards sustainability, with a demonstrated reduction in energy usage of up to 20%. This study also conducts a thorough analysis of the ethical implications of AI in architecture, emphasizing the balance between technological advancement and human creativity. In summary, this research study presents a groundbreaking approach to energy-efficient architectural design using AI, with promising results and wide-ranging applicability. It also thoughtfully addresses the ethical considerations and potential societal impacts of this technological integration.

Research on the Application of Low Carbon and Energy Saving in Green Buildings

This article discusses the principles and recommendations for green and energy-efficient architectural design. Firstly, it is suggested to optimize architectural design and orientation to fully utilize natural resources and lighting. Secondly, using environmentally friendly and energy-efficient materials can effectively reduce energy consumption. Thirdly, by incorporating smart controls and renewable energy applications, the energy efficiency of buildings can be further improved. Additionally, strengthening organizational leadership and establishing sound mechanisms for energy-saving in construction projects, as well as enhancing energy-saving supervision throughout the entire construction process, are also necessary. Lastly, the application of low-carbon energy-saving technologies in the architectural design phase is explored. By implementing the above principles and recommendations, the development of green and energy-efficient buildings can be promoted, contributing to sustainable development.

The Applicability of Biogeography-Based Optimization and Earthworm Optimization Algorithm Hybridized with ANFIS as Reliable Solutions in Estimation of Cooling Load in Buildings

The foundation of energy-efficient architectural design is modeling heating and cooling loads (HLs and CLs), which defines the heating and cooling apparatus constraints necessary to maintain a suitable interior air environment. It is possible that analytical models for energy-efficient buildings might offer an accurate evaluation of the influence that various building designs would have. The implementation of these instruments, however, might be a process that requires a significant amount of manual labor, a significant amount of time, and is reliant on user experiences. In light of this, the authors of this paper present two unique methods for estimating the CL of residential structures in the form of complex mathematical concepts. These methodologies include an evolutionary web algorithm (EWA), biogeography-based optimization (BBO), and a hybridization of an adaptive neuro-fuzzy interface system (ANFIS), namely BBO-ANFIS and EWA-ANFIS. The findings initiated from each of the suggested models are evaluated with the help of various performance metrics. Moreover, it is possible to determine which model is the most effective by comparing their coefficient of determination (R2 ) and its root mean square error (RMSE) to each other. In mapping non-linear connections between input and output variables, the observed findings showed that the models used have a great capability. In addition, the results showed that BBO-ANFIS was the superior forecasting model out of the two provided models, with the lowest value of RMSE and the greatest value of R2 (RMSE = 0.10731 and 0.11282 and R2 = 0.97776 and 0.97552 for training and testing phases, respectively). The EWA-ANFIS also demonstrated RMSE and R2 values of 0.18682 and 0.17681 and 0.93096 and 0.93874 for the training and testing phases, respectively. Finally, this study has proven that ANN is a powerful tool and will be useful for predicting the CL in residential buildings.

Design and Performance Evaluation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring

This article proposes a proof-of-concept device to continuously assess the usage of handheld power tools and detect construction working tasks (e.g., different drilling works) along with potential misusages, e.g., drops, with an energy-efficient architecture design. The designed device is based on Bluetooth low energy (BLE) and NFC connectivity. BLE is used to exchange data with a gateway, whereas NFC has been chosen as an energy-efficient wake-up mechanism. A temperature and humidity sensor is embedded to monitor storage conditions and an accelerometer for tool usage monitoring. The ARM Cortex-M4 core embedded in the BLE module is exploited to process the information at the edge. A Tiny Machine Learning (TinyML) algorithm is proposed to process the data directly on board and achieve low latency and high energy efficiency. The TinyML algorithm has been developed embedded in the proposed device to detect four different usage classes (tool transportation, no-load, metal, and wood drilling). A dataset containing more than 280 min of three-axis accelerations during different activities has been acquired with the device attached to a construction rotary hammer drill and used to train and validate the algorithm. A neural architecture search has been performed to optimize the trade-off between accuracy and complexity, achieving an accuracy of 90.6% with a model size of roughly 30 kB. The experimental results showed an ultralow power consumption in sleep mode of 550 nA and a peak power consumption of 8 mA while running TinyML on the edge. This results in a balanced combination of edge processing capabilities and low power consumption, enabling to obtain a smart Internet of Things (IoT) device in the field with a long lifetime of up to four years in operation and 17 years in shelf mode with a standard 250-mAh coin battery. This work enables a long battery lifetime operation of device degradation and utility analysis, further closing the gap between edge processing and fine granularity data evaluation.

IDENTIFICATION AND EVALUATION OF GREEN BUILDING ASSESSMENT INDICATORS FOR MYANMAR

ABSTRACT To accommodate its increasing population, the Myanmar government has planned to implement smart city projects in Yangon and Mandalay by 2021 and to build 1 million homes by 2030. However, such projected growth does not coincide with Myanmar’s current level of preparedness for sustainable development. Myanmar presently has no standards and specifications for green buildings; it solely relies on the adoption of those from overseas, which may not always be compatible with the unique context of Myanmar. Hence, this study was aimed to identify appropriate green building assessment indicators for Myanmar as an important first step for future rating system development. Nine categories and forty-eight criteria were initially identified by reviewing the widely adopted seven rating systems and investigating existing certified green buildings. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) was used to determine and rank the importance levels of the identified assessment indicators. Results showed that “energy efficiency” and “water efficiency” are the most crucial categories with weights of 17.48% and 13.95%, respectively. Compared to other rating system standards, “waste and pollution” was distinctively found as an important category for Myanmar. Energy-efficient architectural design was ranked as the highest priority among all criteria. These findings serve as a building block for the future development of a Myanmar green building rating system by revealing assessment categories and criteria that are most relevant to Myanmar’s built environment.

On methods of sustainable architectural design of bio-positive buildings in the low-rise residential development structure

The purpose of the author’s research is to determine the actual content of sustainable architectural design for suburban residential development. In accordance with the methodology of area sustainable development the traditional architectural design according to the rules and regulations is completed with additional approaches and methods. As a result, methods of bio-positive design of buildings have been studied and defined, including: the principle of planning transformations, the use of environmentally friendly, local building materials and design concepts, energy-efficient architectural design, the use of alternative energy in building operation, the design of the energy intake and accumulationsystems, the architectural and landscape design that ensures stable functioning of autonomous, sustainable biosystems on the site, non-waste functioning of architectural objects, introduction of waste disposal systems in the project.

Melia: A MapReduce Framework on OpenCL-Based FPGAs

MapReduce, originally developed by Google for search applications, has recently become a popular programming framework for parallel and distributed environments. This paper presents an energy-efficient architecture design for MapReduce on Field Programmable Gate Arrays (FPGAs). The major goal is to enable users to program FPGAs with simple MapReduce interfaces, and meanwhile to embrace automatic performance optimizations within the MapReduce framework. Compared to other processors like CPUs and GPUs, FPGAs are (re-)programmable hardware and have very low energy consumption. However, the design and implementation of MapReduce on FPGAs can be challenging: firstly, FPGAs are usually programmed with hardware description languages, which hurts the programmability of the MapReduce design to its users; secondly, since MapReduce has irregular access patterns (especially in the reduce phase) and needs to support user-defined functions, careful designs and optimizations are required for efficiency. In this paper, we design, implement and evaluate Melia , a MapReduce framework on FPGAs. Melia takes advantage of the recent OpenCL programming framework developed for Altera FPGAs, and abstracts FPGAs behind the simple and familiar MapReduce interfaces in C. We further develop a series of FPGA-centric optimization techniques to improve the efficiency of Melia, and a cost- and resource-based approach to automate the parameter settings for those optimizations. We evaluate Melia on a recent Altera Stratix V GX FPGA with a number of commonly used MapReduce benchmarks. Our results demonstrate that 1) the efficiency and effectiveness of our optimizations and automated parameter setting approach, 2) Melia can achieve promising energy efficiency in comparison with its counterparts on CPUs/GPUs on both single-FPGA and cluster settings.

The Tag Filter Architecture: An energy-efficient cache and directory design

Power consumption in current high-performance chip multiprocessors (CMPs) has become a major design concern that aggravates with the current trend of increasing the core count. A significant fraction of the total power budget is consumed by on-chip caches which are usually deployed with a high associativity degree (even L1 caches are being implemented with eight ways) to enhance the system performance. On a cache access, each way in the corresponding set is accessed in parallel, which is costly in terms of energy. On the other hand, coherence protocols also must implement efficient directory caches that scale in terms of power consumption. Most of the state-of-the-art techniques that reduce the energy consumption of directories are at the cost of performance, which may become unacceptable for high-performance CMPs.In this paper, we propose an energy-efficient architectural design that can be effectively applied to any kind of cache memory. The proposed approach, called the Tag Filter (TF) Architecture, filters the ways accessed in the target cache set, and just a few ways are searched in the tag and data arrays. This allows the approach to reduce the dynamic energy consumption of caches without hurting their access time. For this purpose, the proposed architecture holds the X least significant bits of each tag in a small auxiliary X-bit-wide array. These bits are used to filter the ways where the least significant bits of the tag do not match with the bits in the X-bit array. Experimental results show that, on average, the TF Architecture reduces the dynamic power consumption across the studied applications up to 74.9%, 85.9%, and 84.5% when applied to L1 caches, L2 caches, and directory caches, respectively.

Energy Design by Evolution: Applying Evolutionary Computing to Energy Efficient Architectural Design

In this paper, the authors want to show a method that allows customizing energy efficient buildings to the very task and to the very site by linking environmental data and design strategies through algorithmic processes. An optimum solution for the energy efficiency of a building can then be found by running an evolutionary solver.

Energy Concept Design of Zero Energy Buildings

The paper is oriented on the design of energy concepts of zero energy buildings. In the first phase of concept is important the good architectural design in terms of energy. In the residential building for four flats I calculated the annual energy consumption by computer simulation in the current condition and in the proposed reconstruction. I demonstrate that the unbalanced architectural design of flats in a residential building causes also after reconstruction a significant difference in energy consumption. This means that when developing energy concept of zero energy building the first essential step is energy efficient architectural design, taking into an account the energy aspects. The next step is to the use of technological aspects: passive and active use of solar gains, effective ventilation systems with heat recovery and new generation of heat pumps. For the formation of zero energy buildings is essential application of described progressive trends in the development of energy concepts.