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1
Kuprys, Algirdas, and Ramūnas Gatautis. "COMPARISON REFURBISHMENT MODELS OF DISTRICT HEATING NETWORKS." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 20, no. 1 (October 24, 2013): 11–20. http://dx.doi.org/10.3846/13923730.2013.812576.
Повний текст джерелаАнотація:
The renovation of multi-storey residential buildings reduces heat consumption intensity and decreases heat demand, which may have a harmful effect on a district heating supply system. The paper analyses the heat loss change in four district heating distribution networks (DHNs) of Kaunas at the various scenarios of buildings and DHN renovation stages. A bundle of genetic algorithm software package was used to carry out the districts’ distribution network hydraulic calculations in the case of building renovation without changing the hydrodynamic and network routes. The experimental data were used to calculate heat loss for old and new pipes. The computer data of networks used to summarise the cost of DHN then for the renovation of buildings as well as for renovation progress will go evenly with DHN refurbishment. Network optimization results were summarised by functional dependence. The comparison of the projects’ efficiency was analysed in the following cases: the diameters of pipes of DHN were not changed, new diameters of pipes were integrated partly after partial residential buildings renovation and after a complete renovation of residential building and optimisation of pipeline diameters. The efficiency of separate guidelines of the DHN refurbishment project was summarised by performing sensitivity analysis.
2
Wang, Qiu Xia, and Chen Lin. "Energy Consumption Prediction and Monitoring System for Steel Structure Residential Buildings." Applied Mechanics and Materials 409-410 (September 2013): 553–56. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.553.
Повний текст джерелаАнотація:
Building energy consumption is a large proportion in energy consumption. In order to improve the building energy saving behavior, the building energy consumption prediction should be adopted in practice. Using the expert system to forecast and analyze energy consumption of a steel residential building in the north region, in which the factors: the power saving for buildings and the park electric equipment, heating system control, reclaimed water and solar energy are considered. The network monitoring system is established to realize monitoring energy consumption of buildings and parks. In this case, expert system network monitoring platform can provide managers with energy saving decision-making and environmental parameters and personnel information. Finally, the optimal control for equipments is realized by use of monitoring data.
3
Wang, Endong, and Zhigang Shen. "LIFECYCLE ENERGY CONSUMPTION PREDICTION OF RESIDENTIAL BUILDINGS BY INCORPORATING LONGITUDINAL UNCERTAINTIES." Journal of Civil Engineering and Management 19, Supplement_1 (January 9, 2014): S161—S171. http://dx.doi.org/10.3846/13923730.2013.802744.
Повний текст джерелаАнотація:
Accurate prediction of buildings’ lifecycle energy consumption is a critical part in lifecycle assessment of residential buildings. Longitudinal variations in building conditions, weather conditions and building's service life can cause significant deviation of the prediction from the real lifecycle energy consumption. The objective is to improve the accuracy of lifecycle energy consumption prediction by properly modelling the longitudinal variations in residential energy consumption model using Markov chain based stochastic approach. A stochastic Markov model considering longitudinal uncertainties in building condition, degree days, and service life is developed: 1) Building's service life is estimated through Markov deterioration curve derived from actual building condition data; 2) Neural Network is used to project periodic energy consumption distribution for each joint energy state of building condition and temperature state; 3) Lifecycle energy consumption is aggregated based on Markov process and the state probability. A case study on predicting lifecycle energy consumption of a residential building is presented using the proposed model and the result is compared to that of a traditional deterministic model and three years’ measured annual energy consumptions. It shows that the former model generates much narrower distribution than the latter model when compared to the measured data, which indicates improved result.
4
Xiao, Ziwei, Jiaqi Yuan, Wenjie Gang, Chong Zhang, and Xinhua Xu. "A NILM method for cooling load disaggregation based on artificial neural network." E3S Web of Conferences 111 (2019): 05020. http://dx.doi.org/10.1051/e3sconf/201911105020.
Повний текст джерелаАнотація:
The demand of building energy management has increased due to high energy saving potentials. Load monitor and disaggregation can provide useful information for building energy management systems with detailed and individual loads of the building, so corresponding energy efficient measures can be taken to reduce the energy consumption of buildings. The technique is investigated widely in residential buildings known as Non-Intrusive Load Monitoring (NILM). However, relevant studies are not sufficient for non-residential buildings, especially for the cooling loads. This paper proposes a NILM method for cooling load disaggregation using artificial neural network. The cooling load is disaggregated into four categories: building envelope load, occupant load, equipment load and fresh air load. Two approaches are used to realize the load disaggregation: one is based on the Fourier transfer of the cooling loads, the other takes the cooling load, dry-bulb temperature and humidity of outdoor air, and time as inputs. By implementing the methods in a metro station, the performance of the proposed method can be obtained. Results show that both approaches can realize the load disaggregation accurately, with a RMSE less than 11.2. The second approach is recommended with a higher accuracy.
5
Malinowski, Pawel, Iwona Polarczyk, and Jerzy Piotrowski. "NEURAL MODEL OF RESIDENTIAL BUILDING AIR INFILTRATION PROCESS." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 12, no. 1 (March 31, 2006): 83–88. http://dx.doi.org/10.3846/13923730.2006.9636377.
Повний текст джерелаАнотація:
For the needs of this paper, certain area of issues associated with the infiltration process and neural networks were selected. In the scope of infiltration process, the measurements and analysis of air infiltration through the buildings have shown how difficult this process is in terms of strict mathematical models formulation. In the scope of neural networks, attention was given to multi‐layer perceptrons and systems composed of them. It must be noted that, due to the novelty of this method, a detailed description of the neuron network theory was presented in this paper as well as a thorough discussion of the neuron modelling process. The work was carried out in two stages, with the first stage covering the development of detailed methodology for the construction of neural model and the second stage in which a developed methodology was applied to modelling real objects.
6
Rong, Yuan Xiao. "Information System Design of High Residential Wall Advertising." Advanced Materials Research 1065-1069 (December 2014): 2693–96. http://dx.doi.org/10.4028/www.scientific.net/amr.1065-1069.2693.
Повний текст джерелаАнотація:
The emergence of the network as the carrier with new media technology, network media to become a major new media in modern society, the network media encompasses all manifestations of traditional media and the characteristics of the aid of computers and networks to provide users with text, graphics, sound, and impact of integrated services such as data, this paper mainly from the perspective of digital information transmission methods discussed high-rise building wall advertising design.
7
Yang, Xiaodong, Jiayu Zhang, and Xianbo Zhao. "Factors Affecting Green Residential Building Development: Social Network Analysis." Sustainability 10, no. 5 (May 1, 2018): 1389. http://dx.doi.org/10.3390/su10051389.
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8
Ozola, Silvija. "LOW-RISE RESIDENTIAL BUILDING AND PLANNING DEVELOPMENT OF LIEPAJA “NEW WORLD” AND THE LAKE TOSMARE SHORE TILL WORLD WAR II." SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference 4 (May 25, 2018): 484. http://dx.doi.org/10.17770/sie2018vol4.3422.
Повний текст джерелаАнотація:
Russian Army Headquarters and Maritime Fleet planned to build a sea and land fortress, and Major General Ivan Alfred McDonald developed a project on August 30, 1892. Near Naval Port and the Lake Tosmare Apparent Heir’s Grove and residential buildings with streets were built. Residential buildings were built in “New World” – land between Romny Railway and Grobin Highway. In Liepaja 1922 administrative border plan development was started to include the Lake Liepaja’s northern part into the urban territory. Low-rise residential buildings of Aspazija’s (former Apparent Heir’s) Grove were supplemented. Research issue – building structure and development of residential buildings of Libava Maritime fortress territory has been studied insufficiently. Novelty characteristics – low-rise residential buildings’ construction and street network of Apparent Heir’s Grove has been analysed. Research goal – analyse “New World” and low-rise residential building and planning of the Lake Tosmare surroundings till World War II. Principal research methods – planning and construction observation in nature, archive and cartographic material analysis. Brief description of research outcomes: fortress built on the Baltic Seacoast affected further development of the territory. Nowadays development of qualitative architectonic space without historical development analysis is impossible.
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Generalov, V. P., and E. M. Generalova. "Potential of Buildings Creating High-Quality Urban Environment." IOP Conference Series: Earth and Environmental Science 988, no. 4 (February 1, 2022): 042086. http://dx.doi.org/10.1088/1755-1315/988/4/042086.
Повний текст джерелаАнотація:
Abstract The study deals with the problem of creating high-quality comfortable residential urban environment with the inclusion of residential buildings that have different space-planning structure. The analysis of objects built both in the middle of the last century and in the last 20-30 years is carried out. The research provides comparative assessment of these buildings and complexes that have a developed network of service functions in their structure. As a result, these buildings and complexes have a more significant impact on the creation of high-quality comfortable living environment. Due to the different impact of a building on the living environment, the authors propose to introduce such a concept as «typological potential of a building». Depending upon the impact on comfort and quality of the environment there are residential buildings with «negative», «zero», «small», «medium», «above-average» and «high potential».
10
Lee, Kisu, Goopyo Hong, Lee Sael, Sanghyo Lee, and Ha Young Kim. "MultiDefectNet: Multi-Class Defect Detection of Building Façade Based on Deep Convolutional Neural Network." Sustainability 12, no. 22 (November 23, 2020): 9785. http://dx.doi.org/10.3390/su12229785.
Повний текст джерелаАнотація:
Defects in residential building façades affect the structural integrity of buildings and degrade external appearances. Defects in a building façade are typically managed using manpower during maintenance. This approach is time-consuming, yields subjective results, and can lead to accidents or casualties. To address this, we propose a building façade monitoring system that utilizes an object detection method based on deep learning to efficiently manage defects by minimizing the involvement of manpower. The dataset used for training a deep-learning-based network contains actual residential building façade images. Various building designs in these raw images make it difficult to detect defects because of their various types and complex backgrounds. We employed the faster regions with convolutional neural network (Faster R-CNN) structure for more accurate defect detection in such environments, achieving an average precision (intersection over union (IoU) = 0.5) of 62.7% for all types of trained defects. As it is difficult to detect defects in a training environment, it is necessary to improve the performance of the network. However, the object detection network employed in this study yields an excellent performance in complex real-world images, indicating the possibility of developing a system that would detect defects in more types of building façades.
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Sholahudin, Azimil Gani Alam, Chang In Baek, and Hwataik Han. "Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments." Applied Mechanics and Materials 819 (January 2016): 541–45. http://dx.doi.org/10.4028/www.scientific.net/amm.819.541.
Повний текст джерелаАнотація:
Energy consumption of buildings is increasing steadily and occupying approximately 30-40% of total energy use. It is important to predict heating and cooling loads of a building in the initial stage of design to find out optimal solutions among various design options, as well as in the operating stage after the building has been completed for energy efficient operation. In this paper, an artificial neural network model has been developed to predict heating and cooling loads of a building based on simulation data for building energy performance. The input variables include relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a building, and the output variables include heating load (HL) and cooling load (CL) of the building. The simulation data used for training are the data published in the literature for various 768 residential buildings. ANNs have a merit in estimating output values for given input values satisfactorily, but it has a limitation in acquiring the effects of input variables individually. In order to analyze the effects of the variables, we used a method for design of experiment and conducted ANOVA analysis. The sensitivities of individual variables have been investigated and the most energy efficient solution has been estimated under given conditions. Discussions are included in the paper regarding the variables affecting heating load and cooling load significantly and the effects on heating and cooling loads of residential buildings.
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Jakubczyk-Gałczyńska, Anna. "Predicting the impact of traffic–induced vibrations on buildings using artificial neural networks." MATEC Web of Conferences 219 (2018): 04004. http://dx.doi.org/10.1051/matecconf/201821904004.
Повний текст джерелаАнотація:
Traffic–induced vibrations may constitute a considerable load to a building, cause cracking of plaster, cracks in load–bearing elements or even a global structural collapse of the whole structure [1-4]. Vibrations measurements of real structures are costly and laborious, not justified in all cases. The aim of the paper is to create an original algorithm, to predict the negative dynamic impact on the examined residential building with a high probability. The model to forecast the impact of vibrations on buildings is based on artificial neural networks [5]. The author’s own field studies carried out according to the Polish standard [6] and literature examples [7-10] have been used to create the algorithms. The results of the conducted analysis show that an artificial neural network can be considered a good tool to predict the impact of traffic–induced vibrations on residential buildings, with a sufficiently high reliability.
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Álvarez, José Antonio, Juan Ramón Rabuñal, Dolores García-Vidaurrázaga, Alberto Alvarellos, and Alejandro Pazos. "Modeling of Energy Efficiency for Residential Buildings Using Artificial Neuronal Networks." Advances in Civil Engineering 2018 (November 28, 2018): 1–10. http://dx.doi.org/10.1155/2018/7612623.
Повний текст джерелаАнотація:
Increasing the energy efficiency of buildings is a strategic objective in the European Union, and it is the main reason why numerous studies have been carried out to evaluate and reduce energy consumption in the residential sector. The process of evaluation and qualification of the energy efficiency in existing buildings should contain an analysis of the thermal behavior of the building envelope. To determine this thermal behavior and its representative parameters, we usually have to use destructive auscultation techniques in order to determine the composition of the different layers of the envelope. In this work, we present a nondestructive, fast, and cheap technique based on artificial neural network (ANN) models that predict the energy performance of a house, given some of its characteristics. The models were created using a dataset of buildings of different typologies and uses, located in the northern area of Spain. In this dataset, the models are able to predict the U-opaque value of a building with a correlation coefficient of 0.967 with the real U-opaque measured value for the same building.
14
Biswas, M. A. Rafe, Melvin D. Robinson, and Nelson Fumo. "Prediction of residential building energy consumption: A neural network approach." Energy 117 (December 2016): 84–92. http://dx.doi.org/10.1016/j.energy.2016.10.066.
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Michalak, Piotr. "Thermal Network Model for an Assessment of Summer Indoor Comfort in a Naturally Ventilated Residential Building." Energies 15, no. 10 (May 18, 2022): 3709. http://dx.doi.org/10.3390/en15103709.
Повний текст джерелаАнотація:
Costs of cooling installations cause them to be very rarely used in residential buildings in countries located in heating-dominated climates, like Poland. Hence, there arises the need to assess indoor thermal comfort during summer and to indicate ways to reduce possible overheating. This paper presents an attempt to use the thermal network model of the building zone of EN ISO 13790 to assess indoor operative temperature during four warm months from June to September. The model of the naturally ventilated single-family residential building located in central Poland was used. Performed calculations for the base case resulted in 38 and 63 days within the comfort zone at 80% acceptance level in a total of 122 days in the analyzed period for EN 15251 and ASHRAE standards, respectively. Use of external shading on windows and the roof with lower solar absorptance resulted in 46 and 70 days with acceptable conditions, respectively. Further application of night ventilation resulted in the 38 and 63 days, respectively. From the considered solutions in Polish climate conditions, windows shading seems to be the most efficient solution when controlling indoor comfort in residential buildings with no cooling system. A comparison of hourly operative temperature from that model with the detailed simulation in EnergyPlus showed a strong correlation with R2 = 0.934.
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Razali, Che Munira Che, Shamsul Faisal Mohd Hussein, Nolia Harudin, and Shahrum Shah Abdullah. "Estimation of Building Energy Efficiency Performance Using Radial Basis Function Neural Network." International Journal of Engineering & Technology 7, no. 4.35 (November 30, 2018): 755. http://dx.doi.org/10.14419/ijet.v7i4.35.23102.
Повний текст джерелаАнотація:
Since a pass few decades up to recent, building energy efficiency performance is the top priority due to the sustainability of energy and quality of life. According to recent study related to computer experiment, there are various types of the model has been proposed by the researcher to improve the performance of building energy efficiency. However, there is no empirical evidence to prove the best method in prediction and estimation of energy efficiency that ensure adequate energy to meet todays and future needs. The objective of this paper is to propose Radial Basis Function Neural Network (RBFNN) for estimating the heating load and cooling load of a residential building. This study set out to evaluate different estimation methods of residential building energy efficiency using RBFNN. The data of residential building are obtained from UCI Machine Learning Repository. The dataset of simulation using Ecotect consists of 768 samples with 8 input features and 2 output variables were used to train and test the algorithm of RBFNN. The input variables involved in this experiment are relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a building, while the output variables are heating and cooling loads of the building. The analytical result of the proposed method shows that RBFNN produces better result and performance compared with the previous researches.
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Zhang, Xuenan, Jinxin Zhang, Jinhua Zhang, and YuChuan Zhang. "Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural Network." Geofluids 2021 (September 25, 2021): 1–12. http://dx.doi.org/10.1155/2021/7271383.
Повний текст джерелаАнотація:
As the energy consumption of residential building takes a large part in the building energy consumption, it is important to promote energy efficiency in residential building for green development. In order to evaluate the energy consumption of residential building more effectively, this paper proposes a combined prediction model based on random forest and BP neural network (RF-BPNN). To verify the prediction effect of the RF-BPNN combined model, experiments were performed by using the energy efficiency data set in the UCI database, and the model was evaluated with five indicators: mean absolute error, root mean square deviation, mean absolute percentage error, correlation coefficient, and coincidence index. Compared with the random forest, BP neural network model, and other existing models, respectively, it is proven by the experimental results that the RF-BPNN model possesses higher prediction accuracy and better stability.
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Chen, Qi, Yuanyi Zhang, Xinyuan Li, and Pengjie Tao. "Extracting Rectified Building Footprints from Traditional Orthophotos: A New Workflow." Sensors 22, no. 1 (December 29, 2021): 207. http://dx.doi.org/10.3390/s22010207.
Повний текст джерелаАнотація:
Deep learning techniques such as convolutional neural networks have largely improved the performance of building segmentation from remote sensing images. However, the images for building segmentation are often in the form of traditional orthophotos, where the relief displacement would cause non-negligible misalignment between the roof outline and the footprint of a building; such misalignment poses considerable challenges for extracting accurate building footprints, especially for high-rise buildings. Aiming at alleviating this problem, a new workflow is proposed for generating rectified building footprints from traditional orthophotos. We first use the facade labels, which are prepared efficiently at low cost, along with the roof labels to train a semantic segmentation network. Then, the well-trained network, which employs the state-of-the-art version of EfficientNet as backbone, extracts the roof segments and the facade segments of buildings from the input image. Finally, after clustering the classified pixels into instance-level building objects and tracing out the roof outlines, an energy function is proposed to drive the roof outline to maximally align with the building footprint; thus, the rectified footprints can be generated. The experiments on the aerial orthophotos covering a high-density residential area in Shanghai demonstrate that the proposed workflow can generate obviously more accurate building footprints than the baseline methods, especially for high-rise buildings.
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Li, Miao, Yiran Feng, Maojun Zhou, Hailin Mu, Longxi Li, and Yajun Wang. "Economic and Environmental Optimization for Distributed Energy System Integrated with District Energy Network." Energies 12, no. 10 (May 15, 2019): 1844. http://dx.doi.org/10.3390/en12101844.
Повний текст джерелаАнотація:
The purpose of this research is to develop a mixed integer linear programming model for optimization of a distributed energy system integrated with electricity network. In this model, the optimal configuration of the selected equipment and dynamic intelligent control of the hourly electricity interchange among end-users were determined. The multi-objective function was to maximize the total cost saving and pollutant emission reduction. As an illustrative example, the model was applied to a neighborhood level containing hotel, office and residential buildings in Dalian, China. According to the results, with the installation of the electricity network, the load rate of the power generating unit in the hotel and office were improved and the power generate unit (PGU) often operated at full loads during the daytime with the surplus electricity distributed to the residential buildings. Furthermore, the overall performance was enhanced leading to a more than 30–40% reduction compared to the only distributed energy system (20–30%). In addition, the advancement of the electricity network was reflected in its application in office, where most of the excess electricity is transferred from office to residential building during the daytime in winter, while during the night time, the phenomenon was reversed, so that the office received electricity from residential buildings and no generated unit was at work in office.
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Lee, Myung-Won, and Keun-Chang Kwak. "An Incremental Radial Basis Function Network Based on Information Granules and Its Application." Computational Intelligence and Neuroscience 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/3207627.
Повний текст джерелаАнотація:
This paper is concerned with the design of an Incremental Radial Basis Function Network (IRBFN) by combining Linear Regression (LR) and local RBFN for the prediction of heating load and cooling load in residential buildings. Here the proposed IRBFN is designed by building a collection of information granules through Context-based Fuzzy C-Means (CFCM) clustering algorithm that is guided by the distribution of error of the linear part of the LR model. After adopting a construct of a LR as global model, refine it through local RBFN that captures remaining and more localized nonlinearities of the system to be considered. The experiments are performed on the estimation of energy performance of 768 diverse residential buildings. The experimental results revealed that the proposed IRBFN showed good performance in comparison to LR, the standard RBFN, RBFN with information granules, and Linguistic Model (LM).
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Chang, Dong Eil, Do Guen Yoo, and Joong Hoon Kim. "Practical Head-Outflow Relationship Definition Methodology That Accounts for Varied Water-Supply Methods." Sustainability 12, no. 11 (June 10, 2020): 4755. http://dx.doi.org/10.3390/su12114755.
Повний текст джерелаАнотація:
Achieving reliable pressure-driven analysis (PDA) results that account for anomalies within water-pipe networks requires a head-outflow relationship (HOR) model that can calculate supply flow rate according to the supply pressure at each node. Many studies have suggested HOR models, but a methodology to define HORs that considers the actual residential environment of users and differing water-supply methods for residential buildings has not yet been proposed. This study considered water-use data from buildings and actual differences in residential environments in a surveyed area (including building heights, topography, and water systems within buildings) and water-supply methods (indirect/direct water supply, existence of a pump within buildings) to develop a methodology and derive a representative HOR for the target area for PDA. Further, a representative HOR was determined for each block by applying the developed methodology for two blocks with similar residential environments but different water-use patterns. It confirmed that the HOR induced through this process could provide high water-supply performance despite a low supply head and needed to reflect the diversity of the water-supply method. The proposed HOR-definition methodology can be easily applied in water-pipe network design and operation processes and ensure objectivity and rationality of HOR selection to yield reliable PDA results.
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Zygmunt, Marcin, and Dariusz Gawin. "Application of ANN for analysing a neighbourhood of single-family houses constituting an Energy Cluster." MATEC Web of Conferences 282 (2019): 02072. http://dx.doi.org/10.1051/matecconf/201928202072.
Повний текст джерелаАнотація:
In this article a simple computer tool that can predict energy demand of residential buildings area in Poland by means of artificial neural network is developed and its application is demonstrated. Authors focused on energy demand analyses for single-family houses, representative for the Polish household sector. Advanced computer simulations were performed by means of the Energy Plus software, with hourly calculation step. Then, the obtained results and simulation parameters were used as input data for artificial neural network analysis. As a result, authors developed a simple, user friendly computer tool that can predict, with relatively good accuracy in comparison to the Energy Plus results, energy demand for residential buildings located in Poland. The software might be used for local (a single building) or regional (whole areas, neighbourhoods) analyses of single-family houses in Polish household sector. Additionally, for the analysed area, Renewable Energy potential can be checked – the developed software allows for analyses of solar energy application in the building/neighbour-hood design. Some examples of the energy analyses performed by means of the developed software have been presented.
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Huuskonen, Arto Pekka Juhani. "Supply network design in the residential-FM sector." Facilities 32, no. 11/12 (August 4, 2014): 723–38. http://dx.doi.org/10.1108/f-11-2012-0092.
Повний текст джерелаАнотація:
Purpose – The purpose of this study is to examine supply network designs that large service organisations use in the residential-FM sector to respond to the organising requirements of their operating environments. Design/methodology/approach – The study is theoretically grounded in the well-established literature on the organisational design and structural contingency theory. Utilising a framework of generic organising problems proposed by Miles and Snow (1978) and a multiple-case study design, the study elaborates how large service organisations organise and manage their supply networks in the Finnish residential-FM sector. Findings – The study identifies four supply network designs that organisations use for responding to the organising problems inherent in the property and resident services domains in the residential-FM sector. These include regional production organisation, horizontal decentralisation, environment stabilisation and demand – supply pooling, reflecting the type of the service-market domain and the organisation’s service strategy. Originality/value – The study contributes to the facilities management literature by expanding the field of inquiry from the commercial real estate sector into the emerging field of professional residential facility services. In particular, the study adds to the discussion on supply strategies and design, offering a service provider perspective to the organisation of service supply in housing.
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Sutanto, Sutanto. "MENGGAGAS SUATU KAWASAN INDUSTRI." Gema Teknologi 16, no. 4 (April 15, 2012): 186. http://dx.doi.org/10.14710/gt.v16i4.4788.
Повний текст джерелаАнотація:
Sutanto, in this paper explain that industrial area is the area where the concentration of industrial activities whose existence the ideal should be integrated with other areas such as residential, office, education, economic activity and other agricultural green belt - the other. This industrial area should be equipped with infrastructure and other support facilities so as to serve the upstream to downstream processes. The necessary infrastructure and facilities such as office managers, environmental facilities, the network - a network of roads, electricity, telecommunications, sewage, water, fire and reforestation. For industrial buildings must meet the requirements of building height, building area coefficient referring to the City Master Plan. Keywords: city master plan
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McLeod, Robert S., Michael Swainson, Christina J. Hopfe, Kostas Mourkos, and Chris Goodier. "The importance of infiltration pathways in assessing and modelling overheating risks in multi-residential buildings." Building Services Engineering Research and Technology 41, no. 3 (February 25, 2020): 261–79. http://dx.doi.org/10.1177/0143624420906765.
Повний текст джерелаАнотація:
With the help of building diagnostics, the causes and solutions to complex problems in buildings can be determined. In central and greater London, an increasing number of cases of chronic, year-round, overheating in buildings have been reported. We present three cases of unexpected temperatures in multi-storey residential buildings. Detailed analysis and modelling of these scenarios have led to an investigation of whether the way in which infiltration is currently modelled in building performance simulation may be exerting a pronounced effect on the results of overheating studies. An EnergyPlus model, of one of the dwellings in a multi-residential building in London, was created to investigate the influence of infiltration and exfiltration pathway assumptions on the prediction of overheating. The simulation results were compared to empirical data and show that the predicted indoor temperatures are highly sensitive to how the infiltration airflow network is modelled. The findings of this study have been used to provide practical guidance for modellers and building designers on critical aspects to consider when creating building performance simulation models to ensure more reliable outcomes. Overheating in buildings is an emerging topic of critical importance to the future of the built environment. The importance of understanding infiltration pathways in assessing and modelling overheating risks in flats and multi-residential buildings has been hitherto underestimated or simply ignored. In this paper, examples are given which highlight the need for a fuller understanding of internal air movement where accurate predictions of internal temperatures are required. At present, common building simulation practices and existing technical memorandum (TM) standards are masking the problem and do not provide a basis from which typical or worst-case scenarios can be adequately considered.
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Ocheretiana, H. V., V. M. Makhniuk, V. V. Chorna, A. K. Horval, and H. V. Klymenko. "Legal regulation of the hygienic issues on the interaction of built-in healthcare institutions and residential part of the building." Environment & Health, no. 4 (97) (December 2020): 35–45. http://dx.doi.org/10.32402/dovkil2020.04.035.
Повний текст джерелаАнотація:
Objective: We analysed the regulatory framework on the design of healthcare facilities built in the residential buildings, studied the conditions of their location and developed sanitary-and-antiepidemic requirements for them. Materials and methods: In the work, we used bibliosemantic (for the analysis of the use of legal regulation), theoretical (retrospective use of research data) and analytical methods. Results: According to the reform of the healthcare sector of Ukraine, at the primary level of medical care, the number of healthcare institutions is growing, in particular outpatient ones (outpatient clinics, family doctors’ offices, etc.), which are located in built-in (attached) non-residential premises of ground, first, and basement floors of multistory residential buildings. The authors of the article propose sanitary-and-hygienic and antiepidemic requirements for the location of built-in healthcare institutions at the hybrid type of the construction of residential and public part of the building, using parametric and target (object-by-object) methods. Conclusions: Taking into account that the outpatient healthcare institutions are the primary link in healthcare and make up the bulk of the entire healthcare institutions network, as well as the shortage of land for the construction of outpatient clinics in a separate building, the built-in healthcare institutions have become widespread and are located mainly in non-residential premises and residential buildings (ground, first, and basement floors), which is the element of hybrid construction. Developed sanitary-and-hygienic and antiepidemic measures for built-in healthcare institutionswill help to increase their epidemic safety, to prevent occupational diseases of medical staff.
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Yuan, Jingyi, Miao Li, Xiaoyu Liu, and kun Xu. "Optimization and evaluation of operation strategies for regional distributed energy system." E3S Web of Conferences 194 (2020): 01015. http://dx.doi.org/10.1051/e3sconf/202019401015.
Повний текст джерелаАнотація:
With the development of energy technologies, the electricity, cooling and heating are tightly connected to improve the overall efficiency of distributed energy systems. These systems satisfy simultaneously different energy demands at building level (hotels, offices and residential buildings). The optimal capacity of technologies from numerous alternatives and optimal operating strategy as well as electricity network connection was investigated in this paper in order to achieve economic and environmental gains. A mixed integer linear programming model was also used for multi-objective optimization to minimize total cost and pollutant emissions. The results show that distributed energy system with electricity network as well as real time control can realize the better performance through multi-optimization from regional viewpoint. However, the results are on the contrary aiming at individual hotels and offices. The performance advancement of this system can only be reflected in residential buildings.
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Barberán, Ramón, Diego Colás, and Pilar Egea. "Water Supply and Energy in Residential Buildings: Potential Savings and Financial Profitability." Sustainability 11, no. 1 (January 8, 2019): 295. http://dx.doi.org/10.3390/su11010295.
Повний текст джерелаАнотація:
This article examines the suitability of water supply installations in residential buildings for the pressure conditions of the main water network, and evaluates the energy saving possibilities associated with pumping water into homes. It assesses the situation and the options for renovation in a sample of 151 buildings in the city of Zaragoza (Spain), estimating the savings in electric power and the possible financial returns that could be obtained. The results show that in half the buildings, the installations are inadequate and lead to inefficient energy use, which could be avoided by renovation. However, they also show that in many cases, this type of retrofitting would not be profitable for the building owners, meaning that technically viable solutions may not necessarily be financially viable. To mitigate or avoid the energy inefficiency in question, the public sector could step in by informing and financing support for building owners and regulating in the areas of town planning and construction.
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Popova, Olga, Julia Glebova, and Andrey Pustovgar. "Quality assessment of urban areas based on neural network modeling and GIS." E3S Web of Conferences 33 (2018): 02032. http://dx.doi.org/10.1051/e3sconf/20183302032.
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In this article the authors carry out the research of the urban development areas structure and propose the system of its characteristics on the basis of sector affiliation of the municipal economy. The authors have developed an algorithm for quality assessment of urban development areas. The results of the research are presented on the example of several central quarters of Arkhangelsk city. The city’s residential development was formed in the periods from 1900-1950, 1950-1980 and from 2002 to date. It is currently presented by low-rise wooden, homestead type residential houses and barracks-type houses; mid-rise and high-rise brick and panel buildings of typical development, buildings of large-panel housing construction. Structural SOM-analysis compiled separate quarters of Arkhangelsk into 5 groups with a high level of characteristic similarity: "Commercial", "Prospective complex development", "Sustainable development", "Perspective renovation of residential development", "Investment-unattractive". Typical development strategies for each group of quarters are determined. Most developed areas characterized by upward height. The development strategies for depressed areas is in a high-rise building, which show the economic, social and environmental benefits of upward growth of the city. Using GIS allows to visually reflect the state and assess the quality of the urban development area by the aggregate of all parameters, and also to assess the quality of the quarters for each sector.
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Troshkin, Arsenii. "CURRENT STATUS OF THE NETWORK OF PUBLIC CATERING FACILITIES IN KYIV." Research and methodological works of the National Academy of Visual Arts and Architecture, no. 28 (December 15, 2019): 76–82. http://dx.doi.org/10.33838/naoma.28.2019.76-82.
Повний текст джерелаАнотація:
According to the results of the substantial on-site inspection in 2019, this article shows the analysis of the current status of the network of food facilities in the uptown of Kyiv established in the latter half of the 20th century. Residential areas of Obolon, Pozniaky and Rusanivka have been developed in accordance with almost the same building regulations and have the highest density of population in Kyiv. Considering this fact, exactly these residential areas have been chosen for study.The author of this article were established the establishment of new (considering the time when residential areas were established) types of food facilities, their number, accommodation ratio and tendencies, has defined commons and differences in the accommodation of food facilities in different studied residential areas of Kyiv.In order to study the existing network of public catering facilities, we have considered food facilities forming it at the uptown level in the microdistrict as the nearest food facilities to buildings. The active types of facilities have been studied in each district. All facilities can be divided into two functional groups — by use of food facilities and by time of the food consumption: “fast food” and “leisure food”.During our study we have counted up the number of food sales facilities (food stores, supermarkets, kiosks etc.) which enabled us to specify the modern ratio between food sales facilities and out-of-door food facilities in studied areas. The analysis of the existing network of food facilities in residential areas built up in the latter half of the 20th century has shown dramatic changes in the development of this network in all studied residential areas of Kyiv.This article confirms that in residential areas habitants prefer to eat more at home than in public catering facilities. However, the ratio shows us the tendency that corresponds to the world practice of the food consumption outdoors, reduction in time required for home-based food preparation, although not in such a broad volume.
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Paprotny, Dominik, Heidi Kreibich, Oswaldo Morales-Nápoles, Paweł Terefenko, and Kai Schröter. "Estimating exposure of residential assets to natural hazards in Europe using open data." Natural Hazards and Earth System Sciences 20, no. 1 (January 28, 2020): 323–43. http://dx.doi.org/10.5194/nhess-20-323-2020.
Повний текст джерелаАнотація:
Abstract. Natural hazards affect many types of tangible assets, the most valuable of which are often residential assets, comprising buildings and household contents. Yet, information necessary to derive exposure in terms of monetary value at the level of individual houses is often not available. This includes building type, size, quality, or age. In this study, we provide a universal method for estimating exposure of residential assets using only publicly available or open data. Using building footprints (polygons) from OpenStreetMap as a starting point, we utilized high-resolution elevation models of 30 European capitals and pan-European raster datasets to construct a Bayesian-network-based model that is able to predict building height. The model was then validated with a dataset of (1) buildings in Poland endangered by sea level rise, for which the number of floors is known, and (2) a sample of Dutch and German houses affected in the past by fluvial and pluvial floods, for which usable floor space area is known. Floor space of buildings is an important basis for approximating their economic value, including household contents. Here, we provide average national-level gross replacement costs of the stock of residential assets in 30 European countries, in nominal and real prices, covering the years 2000–2017. We either relied on existing estimates of the total stock of assets or made new calculations using the perpetual inventory method, which were then translated into exposure per square metre of floor space using data on countries' dwelling stocks. The study shows that the resulting standardized residential exposure values provide much better coverage and consistency compared to previous studies.
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Hang, L., and G. Y. Cai. "CNN BASED DETECTION OF BUILDING ROOFS FROM HIGH RESOLUTION SATELLITE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 187–92. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-187-2020.
Повний текст джерелаАнотація:
Abstract. The detection and reconstruction of building have attracted more attention in the community of remote sensing and computer vision. Light detection and ranging (LiDAR) has been proved to be a good way to extract building roofs, while we have to face the problem of data shortage for most of the time. In this paper, we tried to extract the building roofs from very high resolution (VHR) images of Chinese satellite Gaofen-2 by employing convolutional neural network (CNN). It has been proved that the CNN is of a higher capability of recognizing detailed features which may not be classified out by object-based classification approach. Several major steps are concerned in this study, such as generation of training dataset, model training, image segmentation and building roofs recognition. First, urban objects such as trees, roads, squares and buildings were classified based on random forest algorithm by an object-oriented classification approach, the building regions were separated from other classes at the aid of visually interpretation and correction; Next, different types of building roofs mainly categorized by color and size information were trained using the trained CNN. Finally, the industrial and residential building roofs have been recognized individually and the results have been validated individually. The assessment results prove effectiveness of the proposed method with approximately 91% and 88% of quality rates in detection industrial and residential building roofs, respectively. Which means that the CNN approach is prospecting in detecting buildings with a very higher accuracy.
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Otmani, Amira, Mohamed Bouabaz, and Assem Al-Hajj. "Predicting Maintenance and Rehabilitation Cost for Buildings Based on Artificial Neural Network and Fuzzy Logic." International Journal of Computational Intelligence and Applications 19, no. 01 (March 2020): 2050001. http://dx.doi.org/10.1142/s1469026820500017.
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In this paper, the study aims to develop a model for predicting and budgeting maintenance and rehabilitation projects costs for residential buildings throughout their life cycle based on artificial neural network, fuzzy logic and statistical techniques (multi-layer regression). Data consisting of bills of quantities were collected from local Algerian building construction agencies. The principle of costing significant items and work packages has been applied to optimize accurate and efficient document contract model. The results of the research show that neural network has more accuracy with 97% than the multi-layer regression analysis model.
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Mao, Xiao Ling. "Research on Design of SOHO Residential Unit." Advanced Materials Research 228-229 (April 2011): 894–98. http://dx.doi.org/10.4028/www.scientific.net/amr.228-229.894.
Повний текст джерелаАнотація:
Along with the arriving of the information era, more and more people begin to utilize network and computers in their job, making it a new popularized living and fashionable trend. As for this, tradition residential buildings are not able to satisfy people’s needs of work, while tradition office buildings can not meet people’s needs of living as well. Therefore, a new type of building, which integrates its residential function with working function altogether, is urgently demanded to fulfill the objective requirements of the society. The SOHO people work at home, living in groups, and their jib requirements may change, or bring about new situations along with the development of the era. Correspondingly, the design of SOHO residential unit shall also develop and update with new contents in practice. The paper has made deep analysis and discuss on design of functional spaces in SOHO residential unit. After giving consideration to the living features of SOHO groups, as well as based on the concept of economical usage of buildings, the paper further proposed that, in the design of SOHO residential unit, the interaction and interdependency among the three elements of human beings, facilities and environment shall be systematically studied to play the entire unit to its highest performance.
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Aleksahin, A., A. Boblovskii, K. Miahkokhlib, and Y. Schactnyi. "INDICATORS THERMAL AND HYDRAULIC MODES OF DISTRIBUTION HEAT NETWORKS." Municipal economy of cities 3, no. 163 (June 29, 2021): 7–11. http://dx.doi.org/10.33042/2522-1809-2021-3-163-7-11.
Повний текст джерелаАнотація:
The task of heat supply systems is to ensure the quality of services at a minimum cost in the production and transportation of thermal energy. Because the distribution heat networks of district and district systems are characterized by significant branching and significantly longer lengths of heat pipelines compared to the main sections of heating networks, reducing heat loss in these elements of the heating system significantly affects the overall efficiency of district heating. The amount of heat loss depends on the method of laying networks and the diameter of heat pipes, thermal insulation parameters and temperature of the coolant and the environment. Based on the formulas for determining the specific pressure losses to overcome the friction forces obtained from the generalization of these projects of central heating systems of a number of residential districts of Kharkiv, calculated dependences for determining heat losses by supply and return pipelines of the main branches of the heating network. Estimates of accuracy of use of the offered formulas are carried out. The aim of the work is to determine the specific pressure losses for the main branches of heating distribution networks on the basis of generalization of design data for heat supply systems of building groups and clarification of formulas for calculating heat losses by pipelines of central heating distribution system. Based on the generalization of projects for the heating network of residential neighborhoods in Kharkiv, a formula for calculating the specific pressure loss during water movement in the pipelines of the main branches of the heating network depending on the heat load of buildings connected to the branch. Formulas for calculation of heat losses by supply and return pipelines of the main branches of a heating network of the residential district are offered. A comparison of the accuracy of calculations using the proposed formulas with existing methods for determining heat loss in branched heat supply networks, which showed the possibility of using formulas in preliminary assessments of the thermal state of networks.
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Ding, Zhikun, Rongsheng Liu, Zongjie Li, and Cheng Fan. "A Thematic Network-Based Methodology for the Research Trend Identification in Building Energy Management." Energies 13, no. 18 (September 5, 2020): 4621. http://dx.doi.org/10.3390/en13184621.
Повний текст джерелаАнотація:
The rapid increase in the number of online resources and academic articles has created great challenges for researchers and practitioners to efficiently grasp the status quo of building energy-related research. Rather than relying on manual inspections, advanced data analytics (such as text mining) can be used to enhance the efficiency and effectiveness in literature reviews. This article proposes a text mining-based approach for the automatic identification of major research trends in the field of building energy management. In total, 5712 articles (from 1972 to 2019) are analyzed. The word2vec model is used to optimize the latent Dirichlet allocation (LDA) results, and social networks are adopted to visualize the inter-topic relationships. The results are presented using the Gephi visualization platform. Based on inter-topic relevance and topic evolutions, in-depth analysis has been conducted to reveal research trends and hot topics in the field of building energy management. The research results indicate that heating, ventilation, and air conditioning (HVAC) is one of the most essential topics. The thermal environment, indoor illumination, and residential building occupant behaviors are important factors affecting building energy consumption. In addition, building energy-saving renovations, green buildings, and intelligent buildings are research hotspots, and potential future directions. The method developed in this article serves as an effective alternative for researchers and practitioners to extract useful insights from massive text data. It provides a prototype for the automatic identification of research trends based on text mining techniques.
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Li, Xu Jia. "A Study of Beijing’s Suburban Residential Areas." Advanced Materials Research 250-253 (May 2011): 2418–23. http://dx.doi.org/10.4028/www.scientific.net/amr.250-253.2418.
Повний текст джерелаАнотація:
This paper is an analytical study of the planning techniques of the new residential areas in Beijing’s suburbs at the turn of the 21st century, including the scale, structure, road network, green system and public building arrangements of a housing program. Its purpose is to reexamine the assumptions for the planning techniques of modern residential areas, recheck the legitimacy of these techniques, and reconsider residential areas as a proper residential model for modern Beijing. The paper evaluates three issues related to the modern residential areas: the controversies associated with super residential areas, the relationship between pedestrians and automobiles, and urbanization and mix-use communities.
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Jiang, Bo. "Application of Internet of Things Technology in the Property Management of Intelligent Residential District." Applied Mechanics and Materials 357-360 (August 2013): 2242–45. http://dx.doi.org/10.4028/www.scientific.net/amm.357-360.2242.
Повний текст джерелаАнотація:
This article is in view of the development of residential communities tends to be intelligent, digital, network-based, take the property management of intelligent residential district as the object of study and analyze the application of Internet of Things technology in the property management system of intelligent residential district. And a property management platform of intelligent residential district was built based on the IOT technology and the implement steps were set forth in this article, all of which is order to lay the foundation for building the intelligent community and developing intelligence and perception city.
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Brennenstuhl, Marcus, Robin Zeh, Robert Otto, Ruben Pesch, Volker Stockinger, and Dirk Pietruschka. "Report on a Plus-Energy District with Low-Temperature DHC Network, Novel Agrothermal Heat Source, and Applied Demand Response." Applied Sciences 9, no. 23 (November 23, 2019): 5059. http://dx.doi.org/10.3390/app9235059.
Повний текст джерелаАнотація:
District heating and cooling networks can pose the possibility of including a variety of renewable energy sources as well as waste heat into a district’s heat supply concept. Unfortunately, low demand densities as they increasingly occur through higher building energy standards and in rural areas render conventional heating and cooling networks inefficient. At the same time, power-to-heat is becoming more and more important to make use of a larger amount of renewable energy sources on the electrical side by providing more flexibility by means of demand response and demand-side management. Within this work, a rural Plus-Energy settlement is presented addressing those topics by a low-temperature district heating and cooling network connected to a novel agrothermal collector supplying 23 residential buildings with decentralized heat pumps and PV systems. The collector, the network, and six of the buildings are equipped with comprehensive monitoring equipment. Within those buildings, forecast and optimization algorithms are implemented to adapt their heat pump operation to enable an increase of self-consumption, to include flexible electricity tariffs, and also to participate in power markets. Thereby, for the low-temperature district heating and cooling network, it has been shown that the concept can operate in the future at competitive heat costs. On the building level, up to 50% of cost savings could be achieved under ideal conditions with the optimization of the self-consumption of PV electricity. However, to ensure optimal results, the individual system components have to be dimensioned for this task.
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Kim, Mansu, Sungwon Jung, and Joo-won Kang. "Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea." Sustainability 12, no. 1 (December 22, 2019): 109. http://dx.doi.org/10.3390/su12010109.
Повний текст джерелаАнотація:
When researching the energy consumption of residential buildings, it is becoming increasingly important to consider how residents use energy. With the advancement of computing power and data analysis techniques, it is now possible to analyze user information using big data techniques. Here, we endeavored to integrate user information with the physical characteristics of residential buildings to analyze how these elements impact energy consumption. Regression analysis was conducted to accurately identify the impact of each element on energy consumption. It was found that six elements were influential in all seasons: the number of exterior walls, housing direction, housing area, number of years occupied, number of household members, and the occupation of the household head. The elements that had an impact in each period were then derived. Based on the results of the regression analysis, input variables for the training of an artificial neural network (ANN) model were selected for each period, and residential energy consumption prediction models were implemented based on actual consumption. The elements identified as those affecting energy consumption, through regression analysis, can be used for implementing prediction models with advanced forms. This study is significant in that we derived influential elements from an integrative perspective.
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Orlov, Evgeny, Mariia Lavrova, and Alexandra Mishueva. "Water losses in internal water supply services of residential buildings." E3S Web of Conferences 97 (2019): 05015. http://dx.doi.org/10.1051/e3sconf/20199705015.
Повний текст джерелаАнотація:
Internal water supply is intended to provide a consumer with potable quality water for various needs. Leakages appearing in the course of internal water supply operation have a negative effect on the operation of a whole system. All mains reasons of water losses in internal water supply services have been studied. It was found that losses can be caused by a human factor and caused by technical errors in a system, for example, network faults. Based on the study of scientific works on water losses of internal water supply systems, as well as on analysis of various water loss options there is a new well grounded method presented aimed to achieve sustainable water saving directly in a building in the process of water consumptions. There were conclusions drawn saying that: design of an internal water supply system of a residential building should follow the scheme of reducing water consumption on the lower floors of buildings by zoning the entire system; it is advisable to use a system with water tanks; to achieve indirect saving of electric energy, it is necessary to use pumping units with an adjustable drive.
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Nedelcu, Otilia, Ioan Corneliu Salisteanu, Traian Ivanovici, and Valentin Dogaru. "Energy passivation of a building. The ICSTM building case study." E3S Web of Conferences 112 (2019): 02003. http://dx.doi.org/10.1051/e3sconf/201911202003.
Повний текст джерелаАнотація:
The renewable energy sources are becoming more and more current in the energy supply of buildings, from residential buildings to institutional buildings. The renewable energy sources have proved to be a solution and an important element of the Romanian Energy System. We are starting from the idea of using the renewable sources to passivize a building, and we are relying on existing facilities in the Multidisciplinary Scientific and Technological Research Institute (ICSTM) from Valahia University of Targoviste (UVT) to produce the energy from its own sources. We are also considering the consumption data from previous year as well as the electric link to external power grid. This paper aims to propose new renewable energy units so that ICSTM become energetical independent. Currently, the ICSTM building consumes an amount of energy produced from renewable sources about three times less than that consumed from the external network. In order to determine the number of equipment, specifically CPV (concentrating photovoltaic) panels placed on trackers, it is desirable to create a 40% reserve above the installed power, taking in account the possible future development of the research laboratories.
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Yan, Xiongfeng, Huan Chen, Haoran Huang, Qian Liu, and Min Yang. "Building Typification in Map Generalization Using Affinity Propagation Clustering." ISPRS International Journal of Geo-Information 10, no. 11 (October 28, 2021): 732. http://dx.doi.org/10.3390/ijgi10110732.
Повний текст джерелаАнотація:
Building typification is of theoretical interest and practical significance in map generalization. It aims to transform an initial set of buildings to a subset, while maintaining the essential distribution characteristics and important individual buildings. This study focuses on buildings located in residential suburban or rural areas and generalizes them to medium or small scale, for which the typification process can be viewed as point-similar object selection that generates exemplars in local building clusters. From this view, we propose a novel building typification approach using affinity propagation exemplar-based clustering. Based on a sparse graph constructed on the input building set, the proposed approach considers all buildings as potential cluster exemplars and keeps passing messages between those objects; thus, high-quality representative objects (i.e., exemplars) of the initial building set can be obtained and further outputted as the typified result. Experiments with real-life building data show that the proposed method is superior to the two existing representative methods in maintaining the overall distribution characteristics. Meanwhile, the importance of each individual building and the constraints of the road network can be embedded flexibly in this method, which gives some advantages in terms of preserving important buildings and the local structural distribution along the road, etc.
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Li, Chunyu, and Lei Wang. "Research on the Generation and Design Method of Residential Community Scheme Based on Improving Design." Scientific Programming 2021 (November 12, 2021): 1–9. http://dx.doi.org/10.1155/2021/5053494.
Повний текст джерелаАнотація:
Along with the urban renewal and development, the urban living environment has given rise to various problems that need to be solved. With an eye on the future development model of residential communities, an experimental preliminary design for the construction of architectural space, public space, and landscape space based on people’s actual needs is carried out in an attempt to alleviate the more urgent symbiotic relationship between people and urban environment. To this end, this paper proposes a planning and design generation framework for the constructed external spatial environment of building groups based on a recursive double-adversarial network model. Firstly, we extract the features of the constructed external spatial environment of the building group in depth and generate the expression feature map, which is used as a supervisory signal to generate an expression seed image of the constructed external spatial environment of the building group; then we use the generated seed image together with the constructed external spatial environment of the original target building group as the input to generate a feature-holding image as the output of the current frame, and the feature-holding image is also used as the input for the next. Finally, the seed image generation network and the feature-holding image generation network are recursively used to generate the next frame, and the video sequence of the expressions of the constructed external spatial environment of the building group with the same feature-holding expressions as the original input is recursively obtained several times. The experimental results on the building group database show that the proposed method can generate clear and natural video frames of the constructed external spatial environment of the building group, which can be gradually derived from the design of building units to the construction of the building group and penetrate into the planning and design of the external spatial environment in order to comprehensively improve the living environment of urban population and provide a design method and theoretical support for the design of future urban residential communities.
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Kania, Gabriela, Klaudia Kwiecień, Mateusz Malinowski, and Maciej Gliniak. "Analyses of the Life Cycles and Social Costs of CO2 Emissions of Single-Family Residential Buildings: A Case Study in Poland." Sustainability 13, no. 11 (May 30, 2021): 6164. http://dx.doi.org/10.3390/su13116164.
Повний текст джерелаАнотація:
Comprehensive environmental impact assessments of buildings and construction as a whole consider the preparation of construction and finishing materials, their transportation, the process of erecting buildings, long-term operations—including the consumption of electricity, water, and fuels—and the management of the waste generated during the demolition of facilities. In terms of the above-mentioned elements, the most negative environmental impact on a building’s life cycle is in its exploitation stage. In order to reduce this impact, modern sustainable construction uses renewable energy sources. In the area of the Polish building market, analyses of CO2 emissions, the application of LCAs for building materials, and assessments of the social impacts of modern buildings are still very limited. The aim of this study is to evaluate the environmental life cycles and social costs of the CO2 emissions of single-family residential buildings, in which four different systems providing energy (heat and electricity) from renewable and nonrenewable sources are used. In this research, it was found that the annual CO2 emissions per square meter of building surface area in the analyzed objects were in the range of 30 to 176 kg CO2. The greatest contributor to the environmental effects was energy consumption (58% to 90%). The CO2 analysis conducted showed that facilities that use a heat pump are characterized by an environmental effect that is six times lower than that of facilities that are powered by coal combustion and electricity from the network. Similarly, the social costs associated with CO2 emissions were significantly lower in the case of the use of renewable energy sources.
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Moradzadeh, Arash, Amin Mansour-Saatloo, Behnam Mohammadi-Ivatloo, and Amjad Anvari-Moghaddam. "Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings." Applied Sciences 10, no. 11 (May 31, 2020): 3829. http://dx.doi.org/10.3390/app10113829.
Повний текст джерелаАнотація:
Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.
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Camarasa, Clara, Raphael Heiberger, Lena Hennes, Martin Jakob, York Ostermeyer, and Leonardo Rosado. "Key Decision-Makers and Persuaders in the Selection of Energy-Efficient Technologies in EU Residential Buildings." Buildings 10, no. 4 (April 2, 2020): 70. http://dx.doi.org/10.3390/buildings10040070.
Повний текст джерелаАнотація:
With regard to residential energy use in the European Union (EU), most studies consider potential adopters of the technology (e.g., private owners) as being the sole decision-makers in the technology selection. However, during an integrated decision-making process (e.g., a construction project) multiple stakeholders will interact, influencing each other’s judgement, thereby making it difficult to discern who is affecting the final decision, and to what extent. The goal of this study is to outline the full network of stakeholders involved in the decision-making process, along with their degree of power and interaction in the technology choice. For this purpose, empirical evidence from a multi-country survey is examined using social network analysis (SNA). The information is compared across building typologies, project types and countries (i.e., Italy, Spain, Germany, Poland, the United Kingdom, France, Belgium and the Netherlands). The results demonstrate that, in EU residential buildings, potential adopters of the technology are not the only stakeholders involved in the technology selection. They are in all instances in communication with multiple stakeholders, some of whom also hold a high level of power in the decision (i.e., key persuaders). Furthermore, their level of power and communication varies substantially across building typologies, project types and countries.
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Chandrasekar, Aruna, and Eoin Syron. "Evaluation of Heat Decarbonization Strategies and Their Impact on the Irish Gas Network." Gases 1, no. 4 (December 17, 2021): 180–98. http://dx.doi.org/10.3390/gases1040014.
Повний текст джерелаАнотація:
Decarbonization of the heating sector is essential to meet the ambitious goals of the Paris Climate Agreement for 2050. However, poorly insulated buildings and industrial processes with high and intermittent heating demand will still require traditional boilers that burn fuel to avoid excessive burden on electrical networks. Therefore, it is important to assess the impact of residential, commercial, and industrial heat decarbonization strategies on the distribution and transmission gas networks. Using building energy models in EnergyPlus, the progressive decarbonization of gas-fueled heating was investigated by increasing insulation in buildings and increasing the efficiency of gas boilers. Industrial heat decarbonization was evaluated through a progressive move to lower-carbon fuel sources using MATLAB. The results indicated a maximum decrease of 19.9% in natural gas utilization due to the buildings’ thermal retrofits. This, coupled with a move toward the electrification of heat, will reduce volumes of gas being transported through the distribution gas network. However, the decarbonization of the industrial heat demand with hydrogen could result in up to a 380% increase in volumetric flow rate through the transmission network. A comparison between the decarbonization of domestic heating through gas and electrical heating is also carried out. The results indicated that gas networks can continue to play an essential role in the decarbonized energy systems of the future.
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Khan, Zulfiqar, Tanveer Hussain, Amin Ullah, Seungmin Rho, Miyoung Lee, and Sung Baik. "Towards Efficient Electricity Forecasting in Residential and Commercial Buildings: A Novel Hybrid CNN with a LSTM-AE based Framework." Sensors 20, no. 5 (March 4, 2020): 1399. http://dx.doi.org/10.3390/s20051399.
Повний текст джерелаАнотація:
Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in European Union countries up to 40% of the total energy is consumed by households. Most residential buildings and industrial zones are equipped with smart sensors such as metering electric sensors, that are inadequately utilized for better energy management. In this paper, we develop a hybrid convolutional neural network (CNN) with an long short-term memory autoencoder (LSTM-AE) model for future energy prediction in residential and commercial buildings. The central focus of this research work is to utilize the smart meters’ data for energy forecasting in order to enable appropriate energy management in buildings. We performed extensive research using several deep learning-based forecasting models and proposed an optimal hybrid CNN with the LSTM-AE model. To the best of our knowledge, we are the first to incorporate the aforementioned models under the umbrella of a unified framework with some utility preprocessing. Initially, the CNN model extracts features from the input data, which are then fed to the LSTM-encoder to generate encoded sequences. The encoded sequences are decoded by another following LSTM-decoder to advance it to the final dense layer for energy prediction. The experimental results using different evaluation metrics show that the proposed hybrid model works well. Also, it records the smallest value for mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared to other state-of-the-art forecasting methods over the UCI residential building dataset. Furthermore, we conducted experiments on Korean commercial building data and the results indicate that our proposed hybrid model is a worthy contribution to energy forecasting.
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Lee, Chanuk, Dong Eun Jung, Donghoon Lee, Kee Han Kim, and Sung Lok Do. "Prediction Performance Analysis of Artificial Neural Network Model by Input Variable Combination for Residential Heating Loads." Energies 14, no. 3 (February 1, 2021): 756. http://dx.doi.org/10.3390/en14030756.
Повний текст джерелаАнотація:
In Korea apartment buildings, most energy is consumed as heating energy. In order to reduce heating energy in apartment buildings, it is required to reduce the amount of energy used in heating systems. Energy saving in heating systems can be achieved through operation and control based on efficient operation plans. The efficient operation plan of the heating system should be based on the predicted heating load. Thus, various methods have been developed for predicting heating loads. Recently, artificial intelligence techniques (e.g., ANN: artificial neural network) have been used to predict heating loads. The process for determination of input data variables is necessary to obtain the accuracy of predicted results using an ANN model. However, there is a lack of studies to evaluate the accuracy level of the predicted results caused by the selection and combination of input variables. There is a need to evaluate the performance of an ANN model for prediction of residential heating loads. Therefore, the purpose of this study is, for a residential building, to evaluate the accuracy levels of predicted heating loads using an ANN model with various combinations of input variables. To achieve the study purpose, each case was classified according to the combination of the input variables and the prediction results were analyzed. Through this, the worst, mean, and best were selected according to the predicted performance. In addition, an actual case was selected consisting of variables that can be measured in an actual building. The derived cv(RMSE) of each case resulted in a percentage value of 38.2% for the worst, 7.3% for the mean, 3.0% for the best, and 5.4% for the actual. The largest difference between the best and worst resulted in 33.2%, and thus the precision of the predicted heating loads was highly affected by the selection and combination of the input variables used for the ANN model.