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Passive solar building design

About: Passive solar building design is a(n) research topic. Over the lifetime, 5117 publication(s) have been published within this topic receiving 84349 citation(s).

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Papers
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Journal ArticleDOI: 10.1119/1.14178
Abstract: FUNDAMENTALS. Solar Radiation. Available Solar Radiation. Selected Heat Transfer Topics. Radiation Characteristics of Opaque Materials. Radiation Transmission Through Glazing: Absorbed Radiation. Flat--Plate Collectors. Concentrating Collectors. Energy Storage. Solar Process Loads. System Thermal Calculations. Solar Process Economics. APPLICATIONS. Solar Water Heating----Active and Passive. Building Heating----Active. Building Heating: Passive and Hybrid Methods. Cooling. Industrial Process Heat. Solar Thermal Power Systems. Solar Ponds: Evaporative Processes. THERMAL DESIGN METHODS. Simulations in Solar Process Design. Design of Active Systems by f--Chart. Design of Active Systems by Utilizability Methods. Design of Passive and Hybrid Heating Systems. Design of Photovoltaic Systems. Appendices. Author Index. Subject Index.

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7,783 Citations


Journal ArticleDOI: 10.1016/S1364-0321(01)00006-5
Soteris A. Kalogirou1Institutions (1)
Abstract: Artificial neural networks are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems and, once trained, can perform prediction and generalisation at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimisation, signal processing and social/psychological sciences. They are particularly useful in system modelling such as in implementing complex mappings and system identification. This paper presents various applications of neural networks mainly in renewable energy problems in a thematic rather than a chronological or any other order. Artificial neural networks have been used by the author in the field of solar energy; for modelling and design of a solar steam generating plant, for the estimation of a parabolic trough collector intercept factor and local concentration ratio and for the modelling and performance prediction of solar water heating systems. They have also been used for the estimation of heating loads of buildings, for the prediction of air flow in a naturally ventilated test room and for the prediction of the energy consumption of a passive solar building. In all those models a multiple hidden layer architecture has been used. Errors reported in these models are well within acceptable limits, which clearly suggest that artificial neural networks can be used for modelling in other fields of renewable energy production and use. The work of other researchers in the field of renewable energy and other energy systems is also reported. This includes the use of artificial neural networks in solar radiation and wind speed prediction, photovoltaic systems, building services systems and load forecasting and prediction.

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Topics: Photovoltaic system (56%), Renewable energy (55%), Solar energy (54%) ...read more

893 Citations


Open accessBook
01 Aug 1997-
Abstract: BUILDING CLIMATOLOGY. Comfort Issues and Climate Analysis for Building Design. Architectural Features Affecting the Indoor Climate. Materials Properties and Thermal Performance of Buildings. Passive Solar Heating Systems. Passive Cooling of Buildings. Climatic Characteristics of Housing Types. URBAN CLIMATOLOGY. General Characteristics of the Urban Climate. Urban Design Effects on the Urban Climate. Impact of Green Areas on Site and Urban Climates. BUILDING AND URBAN DESIGN GUIDELINES. Building and Urban Design for Hot-Dry Regions. Building and Urban Design for Hot-Humid Regions. Building and Urban Design in Cold Climates. Regions with Cold Winters and Hot-Humid Summers. Index.

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Topics: Urban climate (70%), Urban climatology (66%), Building design (59%) ...read more

771 Citations


Journal ArticleDOI: 10.1016/S0306-2619(00)00005-2
Soteris A. Kalogirou1Institutions (1)
01 Sep 2000-Applied Energy
Abstract: Artificial neural networks offer an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems, and once trained can perform predictions and generalisations at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimisation, signal processing, and social/psychological sciences. They are particularly useful in system modelling, such as in implementing complex mapping and system identification. This paper presents various applications of neural networks in energy problems in a thematic rather than a chronological or any other way. Artificial neural networks have been used by the author in the field of solar energy; for modelling and design of a solar steam generating plant, for the estimation of a parabolic-trough collector's intercept factor and local concentration ratio and for the modelling and performance prediction of solar water-heating systems. They have also been used for the estimation of heating-loads of buildings, for the prediction of air flows in a naturally ventilated test room and for the prediction of the energy consumption of a passive solar building. In all such models, a multiple hidden-layer architecture has been used. Errors reported when using these models are well within acceptable limits, which clearly suggests that artificial neural-networks can be used for modelling in other fields of energy production and use. The work of other researchers in the field of energy is also reported. This includes the use of artificial neural-networks in heating, ventilating and air-conditioning systems, solar radiation, modelling and control of power-generation systems, load-forecasting and refrigeration.

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Topics: Artificial neural network (53%), Solar energy (53%), Energy consumption (53%) ...read more

766 Citations


Journal ArticleDOI: 10.1016/J.BUILDENV.2009.08.016
Laurent Magnier1, Fariborz Haghighat1Institutions (1)
Abstract: Building optimization involving multiple objectives is generally an extremely time-consuming process. The GAINN approach presented in this study first uses a simulation-based Artificial Neural Network (ANN) to characterize building behaviour, and then combines this ANN with a multiobjective Genetic Algorithm (NSGA-II) for optimization. The methodology has been used in the current study for the optimization of thermal comfort and energy consumption in a residential house. Results of ANN training and validation are first discussed. Two optimizations were then conducted taking variables from HVAC system settings, thermostat programming, and passive solar design. By integrating ANN into optimization the total simulation time was considerably reduced compared to classical optimization methodology. Results of the optimizations showed significant reduction in terms of energy consumption as well as improvement in thermal comfort. Finally, thanks to the multiobjective approach, dozens of potential designs were revealed, with a wide range of trade-offs between thermal comfort and energy consumption.

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Topics: Multi-objective optimization (60%), TRNSYS (55%), Energy consumption (54%) ...read more

520 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20228
2021151
2020148
2019148
2018121
2017282

Top Attributes

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Topic's top 5 most impactful authors

G.N. Tiwari

18 papers, 711 citations

Andreas K. Athienitis

14 papers, 890 citations

Ruzhu Wang

8 papers, 481 citations

Hongxing Yang

8 papers, 348 citations

Xi Chen

6 papers, 309 citations

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