Journal articles on the topic 'Buildings – Energy conservation – Mathematical models'
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1
Wang, Yiqiong. "Application of Deep Learning Model in Building Energy Consumption Prediction." Computational Intelligence and Neuroscience 2022 (August 8, 2022): 1–9. http://dx.doi.org/10.1155/2022/4835259.
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In order to achieve China’s energy conservation and emission reduction goal of peaking carbon dioxide emissions around 2030, it is of great significance. An important means of building energy conservation and emission reduction is the fine management of building energy consumption, which is based on the accurate prediction of building energy consumption, so as to support the optimal management of building operation and achieve the goal of energy conservation and emission reduction. This paper puts forward the evaluation indexes of the results of the building energy consumption prediction model, uses MAPE and RMSE indexes to evaluate the accuracy of the prediction results of the model, and uses the prediction time and input parameter dimensions to evaluate the time cost of the prediction model. Then, using the three building energy consumption prediction models based on machine learning algorithm established above, the prediction of energy consumption of four types of public buildings in different seasons is completed, and the prediction results are evaluated and analyzed. According to the prediction results and the requirements of related work on the accuracy of building energy consumption prediction model, the adaptation relationship between different types of buildings and different machine learning algorithm prediction models is summarized.
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Manapbaev, I. K., and M. D. Kutuev. "APPLICATION OF IT FOR THE DESIGN OF THERMAL PROTECTION OF BUILDINGS IN THE REGIONS OF THE COUNTRY." Herald of KSUCTA, №2, Part 1, 2022, no. 2-1-2022 (April 30, 2022): 283–88. http://dx.doi.org/10.35803/1694-5298.2022.2.283-288.
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The article deals with the problems of ensuring thermal protection of buildings in the conditions of the Kyrgyz Republic. Analyzed the main barriers to the implementation of energy conservation policy and energy security in the country. The main directions of thermal protection from the point of view of the ecological and economic essence, the conditions for ensuring the energy efficiency of the designed and reconstructed buildings in the regions of the Kyrgyz Republic have been investigated. The main factors hindering the implementation of these tasks in practice are considered. The main stages of software development for thermal calculation of building envelopes are listed. Mathematical models have been selected for the optimal design of building envelopes, taking into account the climatic features of the construction area. The choice of the C ++ programming language as a programming tool is thoroughly analyzed and substantiated. The prerequisites for creating software for determining the feasibility of using a certain set of heat-insulating materials for enclosing structures have been established.
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Xu, Zhao, Yang Zhang, Heng Li, and Qiming Li. "Study on Building Information Modeling Based Life Cycle Assessment of Environmental Impacts and Decision Making Analysis for Building Construction." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 7212–25. http://dx.doi.org/10.1166/jctn.2016.5694.
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Energy consumption by and emissions from buildings contribute greatly to environmental degradation. Currently, an important tool in the study of architectural conservation design is LCA (life-cycle assessment), with the goal of minimizing energy consumption and environmental impact. The research suggests a method to apply LCA analysis and BIM technology to design 3D BIM models and define the relationship between BIM elements and architectural materials. The obvious advantages of combining BIM with LCA have resulted in its wide use for building life cycle assessment. The study propose here quantitative analysis of environmental impact by construction and build an index database for environmental impact assessment of building projects based on analytical hierarchy process. The design plan of the Teaching and Research Building of a University in Nanjing China is taken as the example to calculate energy consumption in response models formed from construction data. From these modeled calculations, then the key environmental impact factors were analyzed. The objective is to suggest an integrated solution to BIM-based environmental impact assessment of building construction and also provide a theoretical support for optimized building design. This case study demonstrates the utility of BIM when performing LCA, providing most of the information needed to perform LCA.
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Lu, Chenyu, Xiaowan Liu, Tong Zhang, Ping Huang, Xianglong Tang, and Yueju Wang. "Comprehensive Measurement of the Coordinated Development of China’s Economic Growth, Energy Consumption, and Environmental Conservation." Energies 15, no. 17 (August 24, 2022): 6149. http://dx.doi.org/10.3390/en15176149.
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Since the Industrial Revolution, fossil fuels have become the main energy source for economic development. However, fossil fuels have also been linked to serious environmental impacts. China has recently undergone rapid economic growth, but its development model demands large amounts of energy and causes severe pollution. Therefore, there has been a recent shift toward the development of coordinated strategies to achieve economic growth while minimizing energy consumption and preserving the environment. This study sought to explore the spatiotemporal evolution of the coordination degree between economic growth, energy consumption, and environmental conservation (i.e., the “3E” system) in China, thus establishing a basis to improve coordinated development and minimize regional differences. This study evaluated 30 Chinese provinces using mathematical models. Between 2000 and 2019, the coordinated development level of the components of the 3E system in China increased steadily but remained generally low. Clear spatial agglomeration was also identified at the provincial scale, with the highest values occurring on the east coast and lower values occurring in the west and middle provinces.
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Tong, C., and A. Gluhovsky. "Gyrostatic extensions of the Howard-Krishnamurti model of thermal convection with shear." Nonlinear Processes in Geophysics 15, no. 1 (February 6, 2008): 71–79. http://dx.doi.org/10.5194/npg-15-71-2008.
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Abstract. The Howard & Krishnamurti (1986) low-order model (LOM) of Rayleigh-Bénard convection with spontaneous vertical shear can be extended to incorporate various additional physical effects, such as externally forced vertical shear and magnetic field. Designing such extended LOMs so that their mathematical structure is isomorphic to those of systems of coupled gyrostats, with damping and forcing, allows for a modular approach while respecting conservation laws. Energy conservation (in the limit of no damping and forcing) prevents solutions that diverge to infinity, which are present in the original Howard & Krishnamurti LOM. The first LOM developed here (as a candidate model of transverse rolls) involves adding a new Couette mode to represent externally forced vertical shear. The second LOM is a modification of the Lantz (1995) model for magnetoconvection with shear. The modification eliminates an invariant manifold in the original model that leads to potentially unphysical behavior, namely solutions that diverge to infinity, in violation of energy conservation. This paper reports the first extension of the coupled gyrostats modeling framework to incorporate externally forced vertical shear and magnetoconvection with shear. Its aim is to demonstrate better model building techniques that avoid pathologies present in earlier models; consequently we do not focus here on analysis of dynamics or model validation.
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Qin, Yongrui, Meng Zhao, Qingcheng Lin, Xuefeng Li, and Jing Ji. "Data-Driven Building Energy Consumption Prediction Model Based on VMD-SA-DBN." Mathematics 10, no. 17 (August 24, 2022): 3058. http://dx.doi.org/10.3390/math10173058.
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Prediction of building energy consumption using mathematical modeling is crucial for improving the efficiency of building energy utilization, assisting in building energy consumption planning and scheduling, and further achieving the goal of energy conservation and emission reduction. In consideration of the non-linear and non-smooth characteristics of building energy consumption time series data, a short-term, hybrid building energy consumption prediction model combining variational mode decomposition (VMD), a simulated annealing (SA) algorithm, and a deep belief network (DBN) is proposed in this study. In the proposed VMD-SA-DBN model, the VMD algorithm decomposes the time series into different modes to reduce the fluctuation of the data. The SA-DBN prediction model is built for each mode separately, and the DBN network structure parameters are optimized by the SA algorithm. The prediction results of each model are aggregated and reconstructed to obtain the final prediction output. The validity and prediction performance of the proposed model is evaluated on a publicly available dataset, and the results show that the proposed new model significantly improves the accuracy and stability of building energy consumption prediction compared with several typical machine learning methods. The mean absolute percent error (MAPE) of the VMD-SA-DBN model is 63.7%, 65.5%, 46.83%, 64.82%, 44.1%, 36.3%, and 28.3% lower than that of the long short-term memory (LSTM), gated recurrent unit (GRU), VMD-LSTM, VMD-GRU, DBN, SA-DBN, and VMD-DBN models, respectively. The results will help managers formulate more-favorable low-energy emission reduction plans and improve building energy efficiency.
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Korolchenko, D. A., and S. V. Puzach. "Introduction of a flame suppression pattern into integrated and zone models used to analyze the dynamics of hazardous factors of indoor fires." Pozharovzryvobezopasnost/Fire and Explosion Safety 30, no. 2 (May 15, 2021): 78–87. http://dx.doi.org/10.22227/pvb.2021.30.02.78-87.
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Introduction. Fire risk calculation is performed to substantiate the compliance of space planning solutions of buildings and structures with fire safety requirements. The calculation of time needed for hazardous factors of fire to block evacuation routes is performed with account for unlimited fire propagation. The only system, whose operation is taken into account if the fire risk value is exceeded, is the smoke ventilation system. The operation of other fire safety systems is disregarded due to the unavailability of reliable engineering methods of analysis of their influence on fire escalation. The problem of development of modified mathematical models, used to analyze the fire escalation pattern, with account to be taken of the process of fire extinguishing and its influence on fire propagation, is relevant. The purpose of this research is to develop common principles that allow to take account of the pattern of fire suppression in solid materials and combustible liquids by fire extinguishing substances having various origins and degrees of dispersion, if the fire extinguishing substances are added to integrated and zone models of development of hazardous factors of fire escalation.Calculation methodology. The calculations are based on the equation compiled in furtherance of principles of conservation of mass and energy in the flame zone above the surface of the combustible material.Research results. The co-authors suggest general principles for the introduction of a flame suppression pattern into integrated and zone models used to analyze the evolution of hazardous factors of indoor fires. The coauthors present the main provisions and parameters needed to modify integrated and zone models in case of different methods of fire extinguishing. The co-authors have analyzed hazardous factors of fire with account for the flame suppression pattern.Conclusions. The co-authors applied the research findings to develop the modified integrated and zone models that allow to analyze the dynamics of hazardous factors of fire with account for the extinguishing of solid materials and combustible fluids by fire extinguishing substances having various origins and degrees of dispersion.
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Almasarani, Ahmed, Imtiaz K. Ahmad, Mohamed F. El-Amin, and Tayeb Brahimi. "Experimental Investigations and Modeling of Atmospheric Water Generation Using a Desiccant Material." Energies 15, no. 18 (September 19, 2022): 6834. http://dx.doi.org/10.3390/en15186834.
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Harvesting atmospheric water by solar regenerated desiccants is a promising water source that is energy-efficient, environmentally clean, and viable. However, the generated amounts of water are still insignificant. Therefore, more intensive fundamental research must be undertaken involving experiments and modeling. This paper describes several experiments, which were conducted to predict and improve the behavior of water absorption/desorption by the Calcium Chloride (CaCl2) desiccant, where the uncertainty did not exceed ±3.5%. The absorption effect in a deep container was studied experimentally and then amplified by pumping air into the solution. The latter measured water absorption/desorption by a thin solution layer under variable ambient conditions. Pumping air inside deep liquid desiccant containers increased the water absorption rate to 3.75% per hour, yet when using a thin layer of the solution, it was found to have increased to 6.5% per hour under the same conditions. The maximum amount of absorbed water and water vapor partial pressure relation was investigated, and the mean absolute error between the proposed formula and measured water content was 6.9%. An empirical formula, a one-dimensional mathematical model, was then developed by coupling three differential equations and compared to experimental data. The mean absolute error of the model was found to be 3.13% and 7.32% for absorption and desorption, respectively. Governing mathematical conservation equations were subsequently formulated. The mathematical and empirical models were combined and solved numerically. Findings obtained from the simulation were compared to experimental data. Additionally, several scenarios were modeled and tested for Jeddah, Saudi Arabia, under various conditions.
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Al-Maliki, Wisam Abed Kattea, Hayder Q. A. Khafaji, Hasanain A. Abdul Wahhab, Hussein M. H. Al-Khafaji, Falah Alobaid, and Bernd Epple. "Advances in Process Modelling and Simulation of Parabolic Trough Power Plants: A Review." Energies 15, no. 15 (July 29, 2022): 5512. http://dx.doi.org/10.3390/en15155512.
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The common design of thermal power plants is fundamentally oriented towards achieving a high-process performance, with market demands necessitating enhanced operational stability as a result of ongoing global support for renewable energy sources. Indeed, dynamic simulation represents one useful and cost-effective choice for optimizing the flexibility of parabolic trough power plants (PTPP) in a range of transient operating conditions, such as weather changes, resulting again in variations of the output load as well as varying start-up times. The purpose of this review is to provide an overview of steady-state and dynamic modelling for PTPP design, development, and optimization. This gives us a greater opportunity for a broad understanding of the PTPPs subjected to a variety of irradiance solar constraints. The most important features of the steady-state and their uses are reviewed, and the most important programs used in steady-state modelling are also highlighted. In addition, the start-up process of the plant, thermal storage system capacities and response dynamics (charging and discharging modes), and yearly electricity yield can be analyzed using dynamic modelling. Depending on the dynamic simulation, specific uses can be realized, including control loop optimization, load estimation for critical in-service equipment, and emergency safety assessment of power plants in the event of an outage. Based on this review, a detailed overview of the dynamic simulation of PTPP, and its development and application in various simulation programs, is presented. Here, a survey of computational dynamic modelling software commonly applied for commercial and academic applications is performed, accompanied by various sample models of simulation programs such as APROS, DYNAMICS, DYMOLA, and ASPEN PLUS. The simulation programs generally depend on the conservation equations of mass, momentum, species, and energy. However, for the equation of equilibrium, specific mathematical expressions rely on the basic flow model. The essential flow models involved, together with the basic assumptions, are presented, and are supplemented through a general survey covering popular simulation programs. Various previous research on the dynamic simulation of the PTPP are reviewed and analyzed in this paper. Here, several studies in the literature regarding the dynamic simulation of the PTPP are addressed and analyzed. Specific consideration is given to the studies including model verification, in order to explore the effect of modelling assumptions regarding the simulation outputs.
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Oravec, Jakub, Ondřej Šikula, and Iva Nováková. "An Evaluation of the Mathematical Models of Energy Piles." Slovak Journal of Civil Engineering 28, no. 1 (March 1, 2020): 44–48. http://dx.doi.org/10.2478/sjce-2020-0006.
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AbstractA current trend is to reduce the energy performance of buildings by using alternative sources for heating and cooling. One of the most promising, and so far unprecedented sources of heating and cooling, is the use of energy from the earth using the thermally-activated foundation piles of a building, the so-called energy piles. The paper deals with an overview and comparison of computer-aided analytical models of energy piles. The individual analytical models are compared (categorized) from the point of view of their physical complexity, computational costs, and thus their usability for the purpose of optimizing energy-pile equipment or assessing the long-term energy efficiency of an energy pile field. Selected mathematical models were algorithmized, and the results obtained were compared with a more robust numerical solution performed using CalA 4 software.
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Koshevaya, Victoria, Oleksandr Koshevyi, and Oleksandr Trobiuk. "SYSTEMATIC GRAPHICALLY-INTERPRETED MODELS OF CREATION OF ENERGY EFFICIENT BUILDINGS." APPLIED GEOMETRY AND ENGINEERING GRAPHICS, no. 100 (May 24, 2021): 172–81. http://dx.doi.org/10.32347/0131-579x.2021.100.172-181.
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The article considers the creation of systematic graphically-interpreted models for matrix analysis of energy saving measures in the design of residential energy efficient and passive buildings, reconstruction of residential and historical buildings and the creation of energy-active architectural objects using alternative energy sources. Energy-efficient design of new buildings and modernization of old ones remains an urgent task. Ukraine's construction organizations are increasingly interested in reducing energy consumption and introducing energy-saving technologies. Scientists are trying to make housing more energy efficient, namely, consuming less energy compared to ordinary buildings and not polluting the environment. Increasing the level of energy supply and reducing the individual costs of servicing the house will allow solving the problem of creating graphically interpreted models of energy efficiency of the building and energy costs. The analysis is based on a combination of active and passive energy conservation measures and takes into account possible limitations based on a system approach in the construction of an integrated model of an energy active building.
The analysis is based on a combination of active and passive energy saving measures and takes into account possible limitations based on a systematic approach by creation a comprehensive model of an energy-active building.
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Ridwana, Iffat, Nabil Nassif, and Wonchang Choi. "Modeling of Building Energy Consumption by Integrating Regression Analysis and Artificial Neural Network with Data Classification." Buildings 10, no. 11 (November 2, 2020): 198. http://dx.doi.org/10.3390/buildings10110198.
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With the constant expansion of the building sector as a major energy consumer in the modern world, the significance of energy-efficient building systems cannot be more emphasized. Most of the buildings are now equipped with an electric dashboard to record consumption data which presents a significant scope of research by utilizing those data in energy modeling. This paper investigates conventional regression modeling in building energy estimation and proposes three models with data classifications to improve their performance. The proposed models are regression models and an artificial neural network model with data classification for predicting hourly or sub-hourly energy usage in four different buildings. Energy data is collected from a building energy simulation program and existing buildings to develop the models for detailed analysis. Data classification is recommended according to the system operating schedules of the buildings and models are tested for their performance in capturing the data trends resulting from those schedules. Proposed regression models and an ANN model with the recommended classification show very accurate results in estimating energy demand compared to conventional regression models. Correlation coefficient and root mean squared error values improve noticeably for the proposed models and they can potentially be utilized for energy conservation purposes and energy savings in the buildings.
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Zhao, Xing, and Zengfeng Yan. "Analysis of Energy Conservation Big Data of Embedded Large Public Buildings and Construction of the Information Model by 5G." Wireless Communications and Mobile Computing 2022 (May 9, 2022): 1–12. http://dx.doi.org/10.1155/2022/3023323.
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The problem of energy shortage has become one of the most serious problems in the process of economic development. The research is aimed at studying the energy conservation data and information model of large public buildings. Based on the theories of 5G technology, embedded system, and energy conservation of large public buildings, firstly, 5G technology is used to collect research data. Secondly, some large public buildings in Northwest China are analyzed for energy conservation by using ZigBee and other related technologies and algorithms. Finally, the office buildings in large public buildings are used as samples for the construction of the information model to be analyzed. The research results denote that large public buildings are mainly concentrated in hospitals, hotels, shopping malls, and so on. The south-facing window to wall ratio is higher than that of the north-facing window to wall ratio, and the east-west-facing window to wall ratio has the lowest probability of appearing. In addition, the thermal conductivity of the roof of most of the large buildings is less than 1.0 W, while the thermal conductivity of the outer wall of the roof is distributed around 2.5 W, and the thermal conductivity of the outer wall is around 0.6 W. Finally, commercial buildings have higher heating and cooling loads than residential buildings. In the construction of the information model for energy conservation of large public buildings, the neural networks (NNs) and clustering analysis algorithm are introduced into the prediction model of energy consumption data, and it is found that compared with the actual observed value, the overall trend shows consistency, both of which are periodic fluctuations. However, there are still some errors in some data. Therefore, an analysis of energy conservation data of embedded large public buildings and the construction of information models based on 5G has important guiding significance for the construction industry to improve business performance and market competitiveness.
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Deltour, Jade, Nicolas Heijmans, and Karel De Sloover. "Assessing the building envelope performance during occupancy." E3S Web of Conferences 172 (2020): 22004. http://dx.doi.org/10.1051/e3sconf/202017222004.
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In order to achieve carbon neutrality in the building field as expected by the Energy Performance of Buildings Directive, it is important to not only be able to calculate energy performance during design but also to be able to measure the actual energy performance of buildings during occupancy. However, there is currently no approved methodology for assessing the energy performance of the building envelope of an occupied building independently of its occupants, its systems and the climate. We applied a mathematical method to determine the heat loss coefficient (HLC) of the building envelope, from data collected in occupied buildings. The paper describes the in-situ measurement protocol and the mathematical models that contributes to address this challenge. Our methodology is demonstrated on a new semi-detached house, more insulated than the regulatory level, located in Brussels from a full year of monitoring data. For this case study, the results are promising. Indeed, some mathematical models show results of the same range value for the occupied insitu HLC and the reference HLC, obtained from in-situ measurements but without occupancy. However, more case studies should be evaluated, in order to validate the methodology.
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Li, Hui Ling, Hang Tian Liu, and Xiao Qin Li. "Research of Cost Estimation of Energy-Saving Buildings Based on the Full Lifecycle Theory." Applied Mechanics and Materials 357-360 (August 2013): 2454–59. http://dx.doi.org/10.4028/www.scientific.net/amm.357-360.2454.
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This paper first selects the theory of full lifecycle cost as the entry point and analyses the composition of full lifecycle cost of energy-saving buildings. Then it clarifies that the full lifecycle cost estimation of energy-saving buildings is an analytical tool in capital investment and a way to minimize the full lifecycle cost of energy-saving buildings. We also construct the concept and steps of cost estimation and build two mathematical models of full lifecycle cost estimation under two different application conditions; Finally, this paper illustrates the economic and social benefits brought by full life cycle cost estimation of energy-saving buildings through the case studies.
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Mohamed, Mousa, and Mohammed Almarshadi. "Energy saving in air conditioning of buildings." MATEC Web of Conferences 162 (2018): 05024. http://dx.doi.org/10.1051/matecconf/201816205024.
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The external heat load of residential buildings in summer is the dominant parameter of the required cooling load and refrigeration capacity of air conditioning systems. The consumed energy of air conditioning system is proportional to the outside conditions and intensity of solar radiation. The maximum heat load of building may occur at 3 O’clock PM, although the peak of solar radiation occurs at noon. The construction materials of building is playing an important rolls of heat transmission through buildings outside walls and glazing windows. The walls thermal insulation can be effective in energy conservation by reducing the cooling load and required electrical energy. The building is constructed from common materials with 0~12 cm thermal insulation in outside walls, ceilings, and double layers glazing windows. The building heat loads are calculated for two models of walls. The optimum thickness of thermal insulation is also determined and is found between 6~8 cm for insulation of thermal conductivity of 0.039 W/m.K the energy saving is 50.45% at 6 cm insulation thickness.
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Chidiac, S. E., E. J. C. Catania, H. L. Perry, E. Morofsky, and S. Foo. "Computational tools for selecting energy conservation measures for retrofitting existing office buildings." Canadian Journal of Civil Engineering 40, no. 5 (May 2013): 445–59. http://dx.doi.org/10.1139/l2012-037.
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Existing buildings consume most of our non-transportation related energy and are a major contributor to the emission of greenhouse gases. Improving their energy efficiency by applying energy conservation measures (ECMs) remains a major challenge. This paper presents computational tools developed to screen office buildings for retrofit and to select specific ECMs for each building. This selection process accounts for climate, occupancy, building operation, heating and cooling systems, distribution systems, envelope properties, building geometry, and cost of ECMs. The tools screen the complete building set for optimal retrofit opportunities. The methodology adopted in developing the computational tools includes characterization of the office building stock into a manageable set of archetypes; simulation of building operation using specific occupancy characteristics and local climate data; development of energy prediction models for lights, equipment, pumps, fans, domestic hot water, chillers, and boilers; and application of present value analysis to evaluate the cost effectiveness of ECMs.
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Zhang, Yajie, Behrang Vand, and Simone Baldi. "A Review of Mathematical Models of Building Physics and Energy Technologies for Environmentally Friendly Integrated Energy Management Systems." Buildings 12, no. 2 (February 18, 2022): 238. http://dx.doi.org/10.3390/buildings12020238.
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The Energy Management System (EMS) is an efficient technique to monitor, control and enhance the building performance. In the state-of-the-art, building performance analysis is separated into building simulation and control management: this may cause inaccuracies and extra operating time. Thus, a coherent framework to integrate building physics with various energy technologies and energy control management methods is highly required. This framework should be formed by simplified but accurate models of building physics and building energy technologies, and should allow for the selection of proper control strategies according to the control objectives and scenarios. Therefore, this paper reviews the fundamental mathematical modeling and control strategies to create such a framework. The mathematical models of (i) building physics and (ii) popular building energy technologies (renewable energy systems, common heating and cooling energy systems and energy distribution systems) are first presented. Then, it is shown how the collected mathematical models can be linked. Merging with two frequently used EMS strategies, namely rule-based and model predictive controls, is discussed. This work provides an extendable map to model and control buildings and intends to be a foundation for building researchers, designers and engineers.
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Gutiérrez González, Vicente, Germán Ramos Ruiz, Hu Du, Ana Sánchez-Ostiz, and Carlos Fernández Bandera. "Weather Files for the Calibration of Building Energy Models." Applied Sciences 12, no. 15 (July 22, 2022): 7361. http://dx.doi.org/10.3390/app12157361.
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In the fight against climate change, energy modeling is a key tool used to analyze the performance of proposed energy conservation measures for buildings. Studies on the integration of photovoltaic energy in buildings must use calibrated building energy models, as only with them is the demand curve real, and the savings obtained at the self-consumption level, energy storage in the building, or feed into the grid are accurate. The adjustment process of a calibrated model depends on aspects inherent to the building properties (envelope parameters, internal loads, use schedules) as well as external to them (weather, ground properties, etc.). Naturally, the uncertainty of each is essential to obtaining good results. As for the meteorological data, it is preferable to use data from a weather station located in the building or its surroundings, although this is not always possible due to the cost of the initial investment and its maintenance. As a result, weather stations with public access to their data, such as those located at airports or specific locations in cities, are largely used to perform calibrations of building energy models, making it challenging to converge the simulated model with measured data. This research sheds light on how this obstacle can be overcome by using weather data provided by a third-party company, bridging the gap between reality and energy models. For this purpose, calibrations of the two buildings proposed in Annex 58 were performed with different weather configurations, using the mean absolute error (MAE) uncertainty index and Spearman’s rank correlation coefficient (rho) as comparative measures. An optimal and cost-effective solution was found as an alternative to an on-site weather station, based on the use of a single outdoor temperature sensor in combination with third-party weather data, achieving a robust and reliable building energy model.
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Runge, Jason, and Radu Zmeureanu. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review." Energies 12, no. 17 (August 23, 2019): 3254. http://dx.doi.org/10.3390/en12173254.
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During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological and population-based. Therefore, increasing our energy efficiency is of great importance in order to achieve overall sustainability. Forecasting the building energy consumption is important for a wide variety of applications including planning, management, optimization, and conservation. Data-driven models for energy forecasting have grown significantly within the past few decades due to their increased performance, robustness and ease of deployment. Amongst the many different types of models, artificial neural networks rank among the most popular data-driven approaches applied to date. This paper offers a review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations. Based on this review, existing research gaps are identified and presented. Finally, future research directions in the area of artificial neural networks for building energy forecasting are highlighted.
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Deshko, Valeriі, Inna Bilous, Dmytro Biriukov, and Olena Yatsenko. "Transient Energy Models of Housing Facilities Operation." Rocznik Ochrona Środowiska 23 (2021): 539–51. http://dx.doi.org/10.54740/ros.2021.038.
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Buildings are the main consumer of energy resources in the total energy balance of the countries in Central and Eastern Europe, the main energy consumption is allocated for heating. Efficient use of energy resources for heating needs to a large extent depends on the efficiency of regulation of heating systems. In the article, dynamic mathematical models of a two-room typical apartment in Ukraine, built in 2016, were developed in Matlab and EnergyPlus software environment. The simulations were carried out using IWEC hourly climate data for the city of Kyiv. The results of simulations of thermal energy consumption in Matlab are characterized by a larger range of fluctuations of the heating system load, which is typical for the real operating conditions of the system with the controller of ON/OFF type. In EnergyPlus it is assumed that the gas boiler operates continuously in the ON mode. In the research, the change of load on the apartment heating system was studied at different numbers and locations of air temperature control sensors installation, according to which the controller of the autonomous gas boiler operates.
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Zhao, Jing Yuan, and Qi Bo Liu. "A Simulation Study on Energy Consumption of Cluster Distribution of Residential Buildings in Cold Zones." Advanced Materials Research 243-249 (May 2011): 5822–27. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.5822.
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The distribution of residential buildings is closely related to local climate conditions. This paper takes Xi’an region as its representative city to study the thermal conditions of various shapes of cluster distribution by utilizing energy consumption simulation. By sequentially changing the dimensions of each unit building in the cluster, it sets up models of mathematical examples which meet the requirements of different seasons. Based on the annual minimal energy consumption of buildings, the paper quantitatively expounds the influence of cluster distribution on buildings’ energy consumption. This study finally works out a recommendation for the cluster distribution in Xi’an region, i.e. the annual comprehensive energy consumption of buildings is at its minimum when buildings facing both south and north are “L”-shaped enclosures and when they have no westward extension and their length ratio between eastward extended exterior walls and southward level exterior walls is 0.5:1.
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Allen, Summer, and Sandra T. Marquart-Pyatt. "Workplace energy conservation at Michigan State University." International Journal of Sustainability in Higher Education 19, no. 1 (January 2, 2018): 114–29. http://dx.doi.org/10.1108/ijshe-07-2016-0124.
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Purpose This research contributes to the literature on workplace energy conservation by examining the predictors of individual employee behaviors and policy support in a university. The purpose of this research is to better understand what factors influence energy conservation behaviors in this setting to inform programs and interventions. Design/methodology/approach This project gathered survey data from employee-occupants of three campus buildings. Ordinary least squares regression analyses were used to investigate the theory of planned behavior (TPB) and attitudes-behavior-constraints (ABC) models examining what drives energy behaviors. Findings This study finds a large proportion of respondents would support energy use reduction policies, more technology-based interventions and increased use of renewable sources. Respondents report positive attitudes toward individual energy conservation behavior but mild agreement with the influence of social norms. Self-efficacy reports also are positive. Although the TPB model is not fully supported in this group, the ABC model appears to perform well. Originality/value In addition to adding to the relatively limited data on employee energy-saving behaviors in the university workplace, this study provides initial evidence that the ABC model is pertinent to this behavioral context. It also provides specific, usable data about policies and actions that campus community members might deem feasible and acceptable to relevant university offices.
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Sekularac, Nenad, Dragoslav Sumarac, Jasna Cikic-Tovarovic, Milos Cokic, and Jelena Ivanovic-Sekularac. "Re-use of historic buildings and energy refurbishment analysis via building performance simulation: A case study." Thermal Science 22, no. 6 Part A (2018): 2335–54. http://dx.doi.org/10.2298/tsci171124089s.
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This paper analyses the possibility to apply energy refurbishment measures in restored historic buildings in order to ensure reuse. The objective of the paper is to provide an appropriate methodology for the structural restoration of historic buildings, their re-use and sustainable energy refurbishment in accordance with current needs and conservation principles. The study provides the analysis and evaluation of the realized goals regarding energy refurbishment and energy efficiency. The presented historic buildings belong to Hilandar Monastery, Mount Athos, Greece. Mount Athos has been listed in UNESCO World Heritage Site for decades. The paper deals with the expert analysis of abandoned and devastated structures included in the Haybarn Complex. This paper provides a positive outcome of the performed restoration in terms of energy refurbishment and repurpose, since these buildings were turned into unique accommodation facilities for visitors. This accomplishment can be seen as a useful recommendation for improving energy efficiency of historic buildings during their restoration. All the undertaken methods are in accordance with the environmental protection requirements. This study is a practical observation and analysis of energy refurbishment in the field of restoration of listed buildings. This certainly is the most important contribution of this paper. All energy efficiency measures and renewable energy sources were carried out in compliance with conservation requirements and visual authenticity of historic structures. Assessment and analysis of energy efficient refurbishment via building performance simulation method and energy efficiency optimization was applied to several different models of restoration that was carried out in the Haybarn Complex.
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Bas, Erdal, Bahar Acay, and Ramazan Ozarslan. "Fractional models with singular and non-singular kernels for energy efficient buildings." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 2 (February 2019): 023110. http://dx.doi.org/10.1063/1.5082390.
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Daryanto, Eko Budihardjo, Wahyu Setyabudi, and Gagoek Hardiman. "The Role of Wind on Double Skin Facades in High Rise Office Building." Advanced Materials Research 787 (September 2013): 711–16. http://dx.doi.org/10.4028/www.scientific.net/amr.787.711.
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There was an indication that high rise buildings in Jakarta was not designed based on energy conservation principles. The most important aspects of the high-rise buildings is energy saving technology located in the building envelope design. Building envelope with a full glass design functions for widening view and enhancing natural lights, even though but it is also increasing energy consumption and thermal discomfort due to the intensity of solar radiation in hot humid climates. During the current decade, the development of double building envelope type (Double Skin Façade: DSF) seemed more just to improve the aesthetics and the use of natural light, while the wind and thermal performance aspects were still lack of serious consideration. Those aspects will be chosen as the subject matter in this research. The research was aimed to investigate and compare the value of heat transfer in the building envelope of high-rise office buildings. Samples were taken from five DSF buildings, with closed and open cavity. CFD software is used for simulation of the five different models of DSF. The research proves that the high-rise office buildings as the research object in Jakarta do not apply energy conservation principle. The utilization of wind in the DSF cavity can reduce temperature and relieve the burden of air conditioning systems that is energy save. An important finding of the research is the need for ventilation in the design of a double skin at high-rise office buildings in the humid tropics.
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Martynov, Viacheslav. "MULTIPARAMETRIC OPTIMIZATION OF THE SHAPE AND THICKNESS OF INSULATED ENERGY EFFICIENT CONSTRUCTED BUILDING WITH A SPECIFIC NUMBER OF FACES." APPLIED GEOMETRY AND ENGINEERING GRAPHICS, no. 100 (May 24, 2021): 196–204. http://dx.doi.org/10.32347/0131-579x.2021.100.196-204.
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To calculate the optimal parameters of outbuildings, a mathematical model and method for optimizing the shape and resistance of heat transfer for opaque and transparent structures with a certain constant number of faces, building volume and amount of insulation to minimize the thermal balance of enclosing structures with the environment during the heating period In the course of calculations the geometrical parameters of translucent, opaque structures in the heat-insulating shell of buildings are determined taking into account heat losses, heat influx from solar radiation by the criterion of ensuring minimum heat losses through enclosing structures, rational parameters (buildings)
The given technique and mathematical models should be used in the future in the design of energy efficient buildings in the reconstruction and thermal modernization of buildings. This will increase their energy efficiency and, accordingly, the energy efficiency class of buildings.
For the research faceted attached building in the form of a triangular pyramid, the reduction in heat loss was 14.82 percent only due to the optimization of the shape and redistribution of the insulation. Similar results were obtained for other initial forms.
For the first time, a computerized method was proposed, an algorithm and application package Optimparam for multiparameter shape optimization and insulation of translucent and opaque structures for outbuildings with a given number of arbitrarily arranged faces were developed.
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Li, Kangji, Wenping Xue, Gang Tan, and Anthony S. Denzer. "A state of the art review on the prediction of building energy consumption using data-driven technique and evolutionary algorithms." Building Services Engineering Research and Technology 41, no. 1 (May 3, 2019): 108–27. http://dx.doi.org/10.1177/0143624419843647.
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Energy consumption forecasting for buildings plays a significant role in building energy management, conservation and fault diagnosis. Owing to the ease of use and adaptability of optimal solution seeking, data-driven techniques have proved to be accurate and efficient tools in recent years. This study provides a comprehensive review on the existing data-driven approaches for building energy forecasting, such as regression models, artificial neural networks, support vector machines, fuzzy models, grey models, etc. On this basis, the paper puts emphasis to the discussion on evolutionary algorithms hybridized models that combine evolutionary algorithms with regular data-driven models to improve prediction accuracy and robustness. Various combinations of such hybrid models are classified and their characteristics are analyzed. Finally, a detailed discussion on the advantages and challenges of current predictive models is provided. Practical Application: Building energy consumption prediction is important for building energy management, efficiency and fault diagnosis. For existing buildings, multisourced, heterogeneous or inadequate data-driven models may lead to convergence problem or poor model accuracy. To this end, a state of art review on building energy forecasting technique is helpful for related professionals in the building industry.
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Răsvan, Vladimir. "Control of Conservation Laws – An Application." Tatra Mountains Mathematical Publications 71, no. 1 (December 1, 2018): 155–74. http://dx.doi.org/10.2478/tmmp-2018-0014.
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Abstract We present here three types of controlled boundary value problems for conservation laws arising from energy co-generation, hydraulic flows and water hammer for hydroelectric power plants and control of the open channel flows (shallow water). The novelty of these models, from the mathematical point of view, is that they are described by nonlinear hyperbolic partial differential equations of the conservation laws with (possibly) nonlinear boundary conditions. At their turn these boundary conditions are controlled by some systems of ordinary differential equations. The engineering requirements for such systems are asymptotic stability and disturbance rejection: these properties have to be achieved by feedback control. In our setting the main tool for tackling these problems is a suitable Lyapunov functional arising from the energy identity. The hints for “guessing” this functional are to be found in the linearized version of the aforementioned mathematical objects.
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van Odyck, Daniel E. A., John B. Bell, Franck Monmont, and Nikolaos Nikiforakis. "The mathematical structure of multiphase thermal models of flow in porous media." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 465, no. 2102 (November 4, 2008): 523–49. http://dx.doi.org/10.1098/rspa.2008.0268.
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This paper is concerned with the formulation and numerical solution of equations for modelling multicomponent, two-phase, thermal fluid flow in porous media. The fluid model consists of individual chemical component (species) conservation equations, Darcy's law for volumetric flow rates and an energy equation in terms of enthalpy. The model is closed with an equation of state and phase equilibrium conditions that determine the distribution of the chemical components into phases. It is shown that, in the absence of diffusive forces, the flow equations can be split into a system of hyperbolic conservation laws for the species and enthalpy and a parabolic equation for pressure. This decomposition forms the basis of a sequential formulation where the pressure equation is solved implicitly and then the component and enthalpy conservation laws are solved explicitly. A numerical method based on this sequential formulation is presented and used to demonstrate some typical flow behaviour that occurs during fluid injection into a reservoir.
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Zhao, Liang, Wei Zhang, and Wenshun Wang. "BIM-Based Multi-Objective Optimization of Low-Carbon and Energy-Saving Buildings." Sustainability 14, no. 20 (October 12, 2022): 13064. http://dx.doi.org/10.3390/su142013064.
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Global warming and other environmental problems are increasing the demand for green and low-carbon buildings. The development of high-performance computers and building information models has a significant impact on low-carbon buildings. Low-carbon building design needs to comprehensively consider geography, climate, material, cost and other factors, a highly complex multidisciplinary research problem. Therefore, it is urgent to use advanced modeling and simulation technology, involving BIM, parametric design, cloud platform and evolutionary algorithm. This paper proposes a BIM based low-carbon building design optimization framework, which realizes the comprehensive trade-off function of building low-carbon energy saving and daylighting performance through an improved genetic algorithm. The framework drives BIM through parameterization and integrates building environment information, geometric information and operation information, including six parts: BIM model establishment, parameter-driven development, building performance simulation, multi-objective optimization design, Pareto frontier analysis, and energy-saving decision-making and evaluation. The case study shows that the simulation results obtained through the framework can effectively achieve building energy conservation while maximizing the lighting performance of the building, providing a scientific basis and reference for construction professionals to design low-carbon buildings. Finally, the application advantages and limitations of the framework in low-carbon building design and its application prospects in low-carbon energy-saving building design are discussed. This research has made contributions to the multi-disciplinary low-carbon energy conservation research field, realized the multi-objective optimization strategy of building performance based on BIM, genetic algorithm and simulation, and is an important supplement to existing building energy conservation and emission reduction optimization design.
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Varlamov, Andrei A., Vladimir I. Rimshin, and Sergei Y. Tverskoi. "Durability of Buildings in Urban Environment." Materials Science Forum 931 (September 2018): 340–45. http://dx.doi.org/10.4028/www.scientific.net/msf.931.340.
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The longevity forecast is proposed to be implemented on the basis of models of degradation theory. The theory of degradation is developed as a general energy method for estimating the longevity of objects. It is based on the law of conservation of energy. To analyze the longevity of urban environment, it is proposed to adopt the simplest model of theory of degradation. The proposed work provides an explanation for the description of proposed model. The urban environment is divided into a number of simpler systems. The more the degree of system separation into simpler ones, the more accurately one can describe the behavior of the entire system in time. Each simple system is described by the simplest model of degradation theory. The general behavior of the system is understood as a simple sum of individual degradation models. A feature of the proposed theory is that the simplest model can describe the entire system or the sum of its individual parts at once. The model makes it possible to consider the issue of equalizing the longevity of individual systems. The analysis of durability of a multicompartment building is taken as an example. In terms of analyzing the given example, individual stages of systems operation, that are still subject to discussion, are identified.
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Masdías-Bonome, Antonio E., José A. Orosa, and Diego Vergara. "A New Methodology for Decision-Making in Buildings Energy Optimization." Applied Sciences 10, no. 13 (June 30, 2020): 4558. http://dx.doi.org/10.3390/app10134558.
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When designing or retrofitting a building, not too many tools let architects and engineers to define the optimal conditions to reduce energy consumption with the minimal economic investment. This is because different software resources must be employed and an iterative calculation must be done which, most of times, is not possible. The present study aims to define an original methodology that let researchers and architects to select the best option between different possibilities. To reach this objective, Monte Carlo method is employed on the ISO 13790 standard reaching the probability distribution of the energy consumption of each building after each possible modification. From main results, two mathematical models were obtained from a real case study showing the relation between annual energy consumption and economic investment of each different building retrofits. What is more, in disagreement with the expected result, the best retrofit option was not the one with the highest cost and qualities. In conclusion, this methodology can be a useful tool for researchers and professionals to improve their decision-making.
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Tien Bui, Dieu, Hossein Moayedi, Dounis Anastasios, and Loke Kok Foong. "Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models." Applied Sciences 9, no. 17 (August 29, 2019): 3543. http://dx.doi.org/10.3390/app9173543.
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Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing the weights and biases of the artificial neural network (ANN) in the estimation of heating load (HL) and cooling load (CL) of the energy-efficient residential buildings. To this end, a proper dataset was provided composed of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, as the HL and CL influential factors. The optimal structure of each model was achieved through a trial and error process and to evaluate the accuracy of the designed networks, we used three well-known accuracy criterions. As the result of applying GA and ICA, the performance error of ANN decreased respectively by 17.92% and 23.22% for the HL, and 21.13% and 24.53% for CL in the training phase, and 20.84% and 23.74% for HL, and 27.57% and 29.10% for CL in the testing phase. The mentioned results demonstrate the superiority of the ICA-ANN model compared to GA-ANN and ANN.
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Martínez Comesaña, Miguel, Sandra Martínez Mariño, Pablo Eguía Oller, Enrique Granada Álvarez, and Aitor Erkoreka González. "A Functional Data Analysis for Assessing the Impact of a Retrofitting in the Energy Performance of a Building." Mathematics 8, no. 4 (April 7, 2020): 547. http://dx.doi.org/10.3390/math8040547.
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There is an increasing interest in reducing the energy consumption in buildings and in improving their energy efficiency. Building retrofitting is the employed solution for enhancing the energy efficiency in existing buildings. However, the actual performance after retrofitting should be analysed to check the effectiveness of the energy conservation measures. The aim of this work was to detect and to quantify the impact that a retrofitting had in the electrical consumption, heating demands, lighting and temperatures of a building located in the north of Spain. The methodology employed is the application of Functional Data Analyses (FDA) in comparison with classic mathematical techniques such as the Analysis of Variance (ANOVA). The methods that are commonly used for assessing building refurbishment are based on vectorial approaches. The novelty of this work is the application of FDA for assessing the energy performance of renovated buildings. The study proves that more accurate and realistic results are obtained working with correlated datasets than with independently distributed observations of classical methods. Moreover, the electrical savings reached values of more than 70% and the heating demands were reduced more than 15% for all floors in the building.
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Jiao, Lichao, and Xian Rong. "Analysis of Principal Factors on Energy Consumption of Expressway Service Buildings." Energies 15, no. 12 (June 16, 2022): 4392. http://dx.doi.org/10.3390/en15124392.
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As commercial transportation complexes, expressway service buildings have large passenger flow and a poor energy-saving effect, and have become the focus of energy conservation and emissions reduction efforts in the transportation field. At the same time, the particularity of the function determines that it is within the scope of no municipal supporting facilities, which renders them typical energy island-type buildings. This paper takes the expressway service buildings in a cold area as the research object, and carries out the correlation and partial correlation analysis of the factors influencing the operating energy consumption of the air-conditioning system. For the analysis of factors affecting the energy consumption of expressway service buildings during the operation period, considering that most of the service buildings are in the form of heating and cooling air conditioners, this paper chooses to represent the “refrigeration period” with a more obvious degree of influence. At the same time, during the operation period, because the ontological characteristics have been determined according to the analysis results, the outdoor meteorological characteristics are the main factors affecting the energy consumption of expressway service buildings. These include the dry bulb temperature and horizontal plane solar irradiance index, as well as the indoor comprehensive environment parameters: temperature, CO2 concentration index, indoor personnel density index. Based on the above analysis, a low energy consumption operation strategy for the air-conditioning system is proposed. The results of this article are of great significance for the construction of energy consumption models for expressway service buildings and the adoption of low energy consumption strategies.
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Farjami, Elnaz, and Özlem Olgaç Türker. "The Extraction of Prerequisite Criteria for Environmentally Certified Adaptive Reuse of Heritage Buildings." Sustainability 13, no. 6 (March 22, 2021): 3536. http://dx.doi.org/10.3390/su13063536.
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Heritage buildings provide a remarkable value for both the culture and the region where they are located; hence, there is a necessity for them to be conserved. Sustaining heritage buildings for future generations serves cultural sustainability and can be achieved through adaptive reuse with appropriate functions as an efficient conservation approach. Moreover, harnessing the embedded energy from adaptive reuse and the improvement of environmental performance in heritage buildings plays a significant role in ecological sustainability. The aim of the study was to investigate environmental rating systems (ERS) as ecological sustainability evaluation tools and to find out mutual aspects with adaptive reuse models (ARM), thus, serving cultural sustainability.
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Adekunle, Timothy O. "Indoor Comfort, Thermal Indices, and Energy Assessment of Multi-Family Colonial Revival Style Buildings." Energies 14, no. 22 (November 9, 2021): 7468. http://dx.doi.org/10.3390/en14227468.
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Colonial Revival style residences have unique architectural features amongst others. They are common multi-family residences in the United States with no or limited information about their performance. The research purpose is to assess indoor comfort, energy performance, and thermal indices in multi-family Colonial Revival style residences. The research questions include (i) Do Colonial Revival style buildings perform better than other old buildings? (ii) Do the buildings consume additional electricity than typical and other old buildings? The research examined four case studies in Hartford County, Connecticut. The investigation explored comfort surveys, monitoring, collection of actual electricity usage, and assessed thermal indices using mathematical models. The average indoor temperature of 25.4 °C and relative humidity (RH) of 61.3% are reported. About 67% of the residents are thermally comfortable. The research noted significance between thermal sensation and other variables, excluding how occupants feel about the air movement. The average number of hours of temperature exceeds 28.0 °C and 30.0 °C marks for over 11.4% and 2.5% of the time, respectively, except in one of the buildings. The mean indoor temperatures are within the applicable bands of the adaptive comfort models. The averages of actual thermal sensation vote (TSV) ranged from 3.32 to 4.37 on a 7-point sensation scale. The mean neutral temperatures varied from 24.2–25.6 °C. The average monthly electricity bill is within the national average for residences in summer, excluding in August. The mean wet-bulb globe temperature (WBGT) of 21.1–22.3 °C and summer simmer index (SSI) of 30.1–32.4 °C are calculated as feasible bands for thermal indices in the buildings. The basements are more comfortable than other spaces within the case studies. The research outcomes can be used for future developments of Colonial Revival style and other similar buildings. The study recommends interventions such as retrofit to improve the performance of some existing Colonial Revival style buildings, especially the older ones that are less insulated with outdated equipment and appliances.
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Alvarez, Benjamin Louis, and Jérémy Faupin. "Scattering theory for mathematical models of the weak interaction." Reviews in Mathematical Physics 32, no. 01 (August 8, 2019): 2050002. http://dx.doi.org/10.1142/s0129055x20500026.
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We consider mathematical models of the weak decay of the vector bosons [Formula: see text] into leptons. The free quantum field Hamiltonian is perturbed by an interaction term from the standard model of particle physics. After the introduction of high energy and spatial cut-offs, the total quantum Hamiltonian defines a self-adjoint operator on a tensor product of Fock spaces. We study the scattering theory for such models. First, the masses of the neutrinos are supposed to be positive: for all values of the coupling constant, we prove asymptotic completeness of the wave operators. In a second model, neutrinos are treated as massless particles and we consider a simpler interaction Hamiltonian: for small enough values of the coupling constant, we prove again asymptotic completeness, using singular Mourre’s theory, suitable propagation estimates and the conservation of the difference of some number operators.
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Pilipenko, Aleksandr, and Sergei Petrov. "Computer Simulation and Modelling System of Non-Stationary Heat Exchange Processes." MATEC Web of Conferences 155 (2018): 01036. http://dx.doi.org/10.1051/matecconf/201815501036.
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This article suggests the solution to the up-to-date task of improving and increasing the energy efficiency of buildings and quality of functioning of systems, controlling boilers and heating devices, by using computer simulation and modelling of heat exchange processes of the building and operation of the heating systems. The authors present proven (checked) mathematical models, applicable for external protections, rooms, heaters, boilers, insulated and non-insulated piping. The authors offer an automated system of computer simulation and modelling, which not only allows us to solve mathematical models, but also to complement them with information from a real object and improve them by introducing corrective factors and coefficients.
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Wang, Qian-Cheng, Ke-Xin Xie, Xuan Liu, Geoffrey Qi Ping Shen, Hsi-Hsien Wei, and Tian-Yi Liu. "Psychological Drivers of Hotel Guests’ Energy-Saving Behaviours—Empirical Research Based on the Extended Theory of Planned Behaviour." Buildings 11, no. 9 (September 8, 2021): 401. http://dx.doi.org/10.3390/buildings11090401.
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The hospitality industry is an important energy consumer and carbon emitter. Behaviour-driven energy conservation is a strategy with great promise to strengthen the energy efficiency of hotel buildings. The aim of this study is to explore the driving psychological factors of hotel guests’ energy-saving intentions and behaviours. This paper constructs two extensions of the theory of planned behaviour (TPB) with personal norms, past behaviours, and self-determined motivation to explain the guests’ energy-saving behaviour in hotel buildings. This research compares the explanatory power of the original TPB and two extensions with structural equation modelling. The analysis is based on 530 valid, self-reported data from 827 surveyed hotel guests in Shanghai. The analysis suggests that the extended model gains greater explanatory power in predicting the behaviour patterns by employing the above three additional factors. In addition, self-determined motivation presents a more significant impact than other more developed TPB predictors, such as intention and perceived behaviour control. Aside from that, past behaviour replaces attitude as the most critical predictor of hotel energy-saving intention in the extended models. In addition to the existing interventions in office and residential buildings, the research highlights the role of self-determination in hotel energy conservation and further emphasises the long-term benefit of encouraging pro-environmental behaviours in hotel guests. The findings expand the existing research on pro-environmental behaviours and will contribute to energy-saving behaviour intervention in hotel buildings and policy formulations for sustainable hotel operation and maintenance.
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Kovalov, Sergey, Оlexander Mostovenko, Svitlana Botvinovska, and Anna Kolgan. "OPTIMIZATION OF PARAMETERS OF POINT SOURCES OF ENERGY AT SPECIFIC PARAMETERS OF INDIVIDUAL POINTS OF THE ENERGY FIELD." Urban development and spatial planning, no. 80 (May 30, 2022): 210–18. http://dx.doi.org/10.32347/2076-815x.2022.80.210-218.
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In most cases, when modeling energy fields, it is impossible to explicitly write the objective function of the energy field, and this greatly complicates the solution of a problem called nonlinear programming [1].
One possible way to solve nonlinear programming problems is to transform a mathematical model into a function from a single variable or to organize an iterative process by repeatedly searching for the minimum of a function of a single variable. In this case, you can use the known iterative methods for solving the problem [1]: the method of dichotomy (the method of division in half); the method of "golden section", the Fibonacci method; polynomial approximation method; Newton's method, etc.
Geometric problems of energy field optimization derive from the practical problems of energy conservation in the placement of equipment for heating or cooling of architectural structures, reducing the negative acoustic effects of sound energy in the entertainment halls of public buildings, lighting in interiors and exteriors. Most of these tasks are aimed at minimizing the total power of point energy sources while providing the specified parameters of the energy field.
The target optimization function contains both steel and variable parameters (optimization parameters). The set parameters of the energy field are constant, namely: coordinates of individual points of the field and potentials at these points; the variable parameters are the coordinates of point energy sources and their power. Restrictions are the dimensions of the room or exterior.
Solving this optimization problem involves several steps:
- formation of a mathematical model of the energy field;
- formation of the target optimization function based on a mathematical model;
- choosing a method of mathematical programming to solve the problem.
The mathematical model of the energy field in solving such an optimization problem is written in the form of a system of equations.
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Pandey, Kamal, and Bhaskar Basu. "Mathematical modeling for short term indoor room temperature forecasting using Box-Jenkins models." Journal of Modelling in Management 15, no. 3 (February 12, 2020): 1105–36. http://dx.doi.org/10.1108/jm2-08-2019-0182.
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Purpose The rapid urbanization of Indian cities and the population surge in cities has steered a massive demand for energy, thereby increasing the carbon emissions in the environment. Information and technology advancements, aided by predictive tools, can optimize this energy demand and help reduce harmful carbon emissions. Out of the multiple factors governing the energy consumption and comfort of buildings, indoor room temperature is a critical one, as it envisages the need for regulating the temperature. This paper aims to propose a mathematical model for short-term forecasting of indoor room temperature in the Indian context to optimize energy consumption and reduce carbon emissions in the environment. Design/methodology/approach A study is conducted to forecast the indoor room temperature of an Indian corporate building structure, based upon various external environmental factors: temperature and rainfall and internal factors like cooling control, occupancy behavior and building characteristics. Expert insight and principal component analysis are applied for appropriate variables selection. The machine learning approach using Box–Jenkins time series models is used for the forecasting of indoor room temperature. Findings ARIMAX model, with lagged forecasted and explanatory variables, is found to be the best-fit model. A predictive short-term hourly temperature forecasting model is developed based upon ARIMAX model, which yields fairly accurate results for data set pertaining to the building conditions and climatic parameters in the Indian context. Results also investigate the relationships between the forecasted and individual explanatory variables, which are validated using theoretical proofs. Research limitations/implications The models considered in this research are Box–Jenkins models, which are linear time series models. There are non-linear models, such as artificial neural network models and deep learning models, which can be a part of this study. The study of hybrid models including combined forecasting techniques comprising linear and non-linear methods is another important area for future scope of study. As this study is based on a single corporate entity, the models developed need to be tested further for robustness and reliability. Practical implications Forecasting of indoor room temperature provides essential practical information about meeting the in-future energy demand, that is, how much energy resources would be needed to maintain the equilibrium between energy consumption and building comfort. In addition, this forecast provides information about the prospective peak usage of air-conditioning controls within the building indoor control management system through a feedback control loop. The resultant model developed can be adopted for smart buildings within Indian context. Social implications This study has been conducted in India, which has seen a rapid surge in population growth and urbanization. Being a developing country, India needs to channelize its energy needs judiciously by minimizing the energy wastage and reducing carbon emissions. This study proposes certain pre-emptive measures that help in minimizing the consumption of available energy resources as well as reducing carbon emissions that have significant impact on the society and environment at large. Originality/value A large number of factors affecting the indoor room temperature present a research challenge for model building. The paper statistically identifies the parameters influencing the indoor room temperature forecasting and their relationship with the forecasted model. Considering Indian climatic, geographical and building structure conditions, the paper presents a systematic mathematical model to forecast hourly indoor room temperature for next 120 h with fair degree of accuracy.
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Hajtmanek, Roman, Peter Morgenstein, Tomáš Hubinský, Ján Legény, and Robert Špaček. "Determination of Solar-Surface-Area-to-Volume Ratio: Early Design Stage Solar Performance Assessment of Buildings." Buildings 13, no. 2 (January 19, 2023): 296. http://dx.doi.org/10.3390/buildings13020296.
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One of the main targets of globally aimed strategies such as the UN-supported Race to Zero campaign or the European Green Deal is the decarbonisation of the building sector. The implementation of renewable energy sources in new urban structures, as well as the complex reconstruction of existing buildings, represents a key area of sustainable urban development. Supporting this approach, this paper introduces the solar-surface-area-to-volume ratio (Rsol) and the solar performance indicator (Psol), applicable for evaluation of the energy performance of basic building shapes at early design stages. The indicators are based on the preprocessors calculated using two different mathematical models—Robinson and Stone’s cumulative sky algorithm and Kittler and Mikler’s model—which are then compared and evaluated. Contrary to the commonly used surface-area-to-volume ratio, the proposed indicators estimate the potential for energy generation by active solar appliances integrated in the building envelope and allow optimisation of building shape in relation to potential energy losses and potential solar gains simultaneously. On the basis of the mathematical models, an online application optimising building shape to maximise sun-exposed surfaces has been developed. In connection with the solar-surface-area-to-volume ratio, it facilitates the quantitative evaluation of energy efficiency of various shapes by the wider professional public. The proposed indicators, verified in a case study presented, shall result in the increased sustainability of building sector by improving the utilisation of solar energy and overall energy performance of buildings.
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García-Cuadrado, Juan, Andrea Conserva, Juan Aranda, David Zambrana-Vasquez, Tatiana García-Armingol, and Gema Millán. "Response Surface Method to Calculate Energy Savings Associated with Thermal Comfort Improvement in Buildings." Sustainability 14, no. 5 (March 2, 2022): 2933. http://dx.doi.org/10.3390/su14052933.
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In developed countries, a large part of the building stock in 2050 will consist of currently existing buildings. Consequently, in order to achieve the objectives in terms of energy efficiency in the building sector we must consider not only new infrastructures but also the old ones. A reduction in energy consumption for climate control of between 50 and 90% can be achieved by rehabilitation and the implementation of different energy efficiency measures. Currently, these measures to reduce energy consumption and associated CO2 emissions can be modelled using computer tools. However, high precision and detail of thermal behaviour models through simulations can mean a great computational cost for companies, which results in a blockage of servers and workers. In this paper, the Response Surface Methodology (RSM) is presented as an innovative methodology for the simplification of models for calculation of the energy savings associated with thermal comfort improvement in buildings. A single-family house model, located in three different climates, is presented as a case study in order to validate the proposed methodology. Different scenarios were simulated, addressing heating and cooling temperature set points and external wall insulation represented by the transmittance (U-value). Results obtained from energy simulation using Design Builder were contrasted against those estimated from the simplified model extracted from the RSM analysis. The results revealed a deviation lower than 3% when comparing both methods. Therefore, the simplified mathematical prediction models are demonstrated to be suitable for the study of the energy performance of buildings, saving computational time, costs and associated human resources.
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Majid, Muhammad Imran, and Muhammad Ibrahim Khan. "Techno-Economic Analysis of Green Construction Regulations Plus Survey for Prototype Implementation in Karachi." Pakistan Journal of Scientific & Industrial Research Series A: Physical Sciences 64, no. 2 (July 6, 2021): 161–72. http://dx.doi.org/10.52763/pjsir.phys.sci.64.2.2021.161.172.
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Conventional buildings consume large amounts of energy and are the source of greenhouse gas emissions contributing to climate change. The shortage of green buildings in Pakistan have been said to be due to shortage of knowledge of the benefits of essential green building materials and the absence of a regulatory framework. The study is qualitative and quantitative and is divided into two parts. This review paper focuses on awareness in the Pakistan's local construction market concerning green buildings through a survey questionnaire. Data collection procedure consists of 60 questionnaires related to green building construction awareness and priorities within Karachi's construction sector. Also, a focus group and personal interviews conducted with at least 5 professionals working in local construction projects are analyzed. The research questions formed from detailed literature review were analyzed and found that energy conservation, environmental/resource conservation and improving indoor environmental quality are major driving factors for green building development. Similarly, there is a lack of knowledge of green construction methods and regulations and lack of support from the government. This has been found as the major barrier in going towards green construction. We recommend policy directions towards this end and elaborate points of concern to the authorities.
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Huang, Junhui, Mohammed Algahtani, and Sakdirat Kaewunruen. "Energy Forecasting in a Public Building: A Benchmarking Analysis on Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) Networks." Applied Sciences 12, no. 19 (September 28, 2022): 9788. http://dx.doi.org/10.3390/app12199788.
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A primary energy consumption and CO2 emission source stems from buildings and infrastructures due to rapid urbanisation and social development. An accurate method to forecast energy consumption in a building is thus critically needed to enable successful management of adaptive energy consumption and ease the level of CO2 emission. However, energy forecasting for buildings, especially residential buildings, has several challenges, such as significant variations in energy usage patterns due to unpredicted demands of the residences and some intricate factors, which can randomly affect the patterns. Traditional forecasting approaches require a tremendous number of inputs needed for building physic models and variations often exist between as-built and as-designed buildings in reality. Most recent studies have adopted only ambient weather conditions, building components, and the occupant’s behaviours. As a result, in order to take into account the complexity of factors that can affect the building energy model development and its computation, we develop advanced machine learning models driven by the inherent electricity consumption pattern associated with the day and time. In this study, we demonstrate benchmarking results derived from three different machine learning algorithms, namely SVR, XGBoost, and LSTM, trained by using 1-year datasets with sub-hourly (30 min) temporal granularity to determine the outperformed predictor. Ultimately, the machine learning model robustness and performance on a basis of the coefficient of variation (CV) obtained by the SVR is benchmarked across XGBoost and LSTM trained by the same datasets containing attributes related to the building type, data size, and temporal granularity. The insight stemming from this study indicates that the suitable choice of the machine learning models for building energy forecasts largely depends on the natural characteristics of building energy data. Hyperparameter tuning or mathematical modification within an algorithm may not be sufficient to attain the most accurate machine learning model for building energy forecast.
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Zhang, Yu Fan, Zhi Hao Ji, Jin Yan Liu, Shu Sheng Xiong, Xiao Bo Huang, Bin Tao Mao, Bao Sheng Guo, Zhi Chao Ying, and Ya Bo Sun. "Study on Mathematical Model in Working Process of Automotive Air Conditioning Scroll Compressor." Applied Mechanics and Materials 741 (March 2015): 572–76. http://dx.doi.org/10.4028/www.scientific.net/amm.741.572.
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In order to study the working features of automotive air conditioning scroll compressor, this paper uses mathematical model of working process to analyze it. After studying working process, a equation of suction volume is given. The thermodynamic model of compression chamber and back pressure chamber includes mass conservation and energy conservation. Based on Hydromechanics, leakage models of radial leakage and tangential leakage are discussed. The model lays a foundation of automotive air conditioning scroll compressor performance simulation.
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El-Agami, Mohanad, Gehad Hanafy, and Medhat Osman. "Investigating the Effect of High-Rise Buildings’ Mass Geometry on Energy Efficiency within the Climatic Variation of Egypt." Sustainability 13, no. 19 (September 23, 2021): 10529. http://dx.doi.org/10.3390/su131910529.
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Energy conservation is recently the most important issue all over the world, including in Egypt. Recently, the built environment of Egypt has experienced a dramatic change in its buildings’ typology, with more interest in constructing high-rise buildings. This in turn creates high demand for energy, as high-rise buildings are considered to be one of the most energy-consuming types of buildings. Egypt has a wide variety in its climatic conditions, with seven different inhabited climatic regions, and a further one which is uninhabited. Therefore, integrating the energy efficiency of a building as a major design factor in the early design stages of such a type of buildings is important. This article is concerned with investigating the effect of high-rise buildings’ geometrical shape on the building’s energy consumption within the different climatic regions of Egypt. Four building shapes (square, circular, rectangular, and ellipse) are examined. The long axe of the models is oriented to the north (“the optimum orientation within all regions”), with a window-to-wall ratio (WWR) of 30%. The performance of these models is studied in seven cities representing the inhabited Egyptian climatic regions using simulation software, DesignBuilder, with the EnergyPlus simulation tool. Study findings revealed that adjusting the geometric form of the building significantly affects energy consumption and thermal comfort with climatic variation. The most compact shape, circular, was the most suitable geometrical shape in four regions out of seven. The ellipse shape was found to be the most suitable mass geometry within two other regions, while the square shape was found to be effective in only one region. The results of this research indicate that designers should not use the rectangular shape anywhere across Egypt.
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Rastbod, Samira, Farnaz Rahimi, Yara Dehghan, Saeed Kamranfar, Omrane Benjeddou, and Moncef L. Nehdi. "An Optimized Machine Learning Approach for Forecasting Thermal Energy Demand of Buildings." Sustainability 15, no. 1 (December 23, 2022): 231. http://dx.doi.org/10.3390/su15010231.
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Recent developments in indirect predictive methods have yielded promising solutions for energy consumption modeling. The present study proposes and evaluates a novel integrated methodology for estimating the annual thermal energy demand (DAN), which is considered as an indicator of the heating and cooling loads of buildings. A multilayer perceptron (MLP) neural network is optimally trained by symbiotic organism search (SOS), which is among the strongest metaheuristic algorithms. Three benchmark algorithms, namely, political optimizer (PO), harmony search algorithm (HSA), and backtracking search algorithm (BSA) are likewise applied and compared with the SOS. The results indicate that (i) utilizing the properties of the building within an artificial intelligence framework gives a suitable prediction for the DAN indicator, (ii) with nearly 1% error and 99% correlation, the suggested MLP-SOS is capable of accurately learning and reproducing the nonlinear DAN pattern, and (iii) this model outperforms other models such as MLP-PO, MLP-HSA and MLP-BSA. The discovered solution is finally expressed in an explicit mathematical format for practical uses in the future.