HTB2: A flexible model for dynamic building simulation

Lewis, Peter; Alexander, Donald Kneale

doi:10.1016/0360-1323(90)90035-p

Cited by 43 publications

(27 citation statements)

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“…This 'typical' building was then modelled in 3D using the Ecotect® software (Marsh, 1996), before being exported to the Heat Transfer in Buildings 2 (HTB2) software (Lewis & Alexander, 1990) for thermal performance simulation and analysis. Exporting to HTB2 was necessary as this software allows for more in depth analysis and also simulates the building dynamically as opposed to the admittance or steady state method used in Ecotect®.…”

Section: Building Performance Simulationmentioning

confidence: 99%

The impact of regulations on overheating risk in dwellings

Mulville

Stravoravdis

2016

Building Research & Information

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Section: Building Performance Simulationmentioning

confidence: 99%

The impact of regulations on overheating risk in dwellings

Mulville

Stravoravdis

2016

Building Research & Information

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“…To generate the learning datasets for the FM the simulation software tool HTB2 [17] has been used. The FC is designed ad hoc.…”

Section: A Mas Solution Using Fm and Fcmentioning

confidence: 99%

“…The dataset must be generated from simulations. The HTB2 simulation software [17], in following HTB2, is a well known tool suitable to analyze the dynamics of heating systems with concentrated parameter problems as the one that concerns us [3,16].…”

Section: Creating the Dataset From Realistic Simulationsmentioning

confidence: 99%

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Energy Saving By Means Of Multiagent Systems And Fuzzy Systems

Villar

Cal

Sedano

2008

IEEE Latin Am. Trans.

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Abstract. The reduction of the consumed energy in heating systems is one of the most important actions to be taken in order to achieve a global sustainability. To save energy is urgent, which means that the energy losses must be reduced. In Spain it is especially important as the construction rate is almost the half of that in Europe. It's designed a distributed solution by means of a multi-agent system, and a central control unit responsible for minimising the energy consumption. The central control unit (CCU) includes a Fuzzy Model -for estimating the energy needs-and a Fuzzy controller -to set the power rate of each heater-. The CCU is responsible for distributing the available power over the set of heaters. This solution is the preliminary study to be included in a heating system product of a local company.

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“…A three-step procedure for testing and validating the model is proposed: firstly, the dynamic thermal behaviour of a specific configuration is calculated using HTB2 software [6]. The outcome of the HTB2 should then be post-processed to obtain a suitable dataset.…”

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confidence: 99%

Improving Energy Efficiency in Buildings Using Machine Intelligence

Sedano

Villar

Curiel

et al. 2009

Intelligent Data Engineering and Automated Learning - IDEAL 2009

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Abstract. Improving the detection of thermal insulation in buildings -which includes the development of models for heating and ventilation processes and fabric gain -could significantly increase building energy efficiency and substantially contribute to reductions in energy consumption and in the carbon footprints of domestic heating systems. Thermal insulation standards are now contractual obligations in new buildings, although poor energy efficiency is often a defining characteristic of buildings built before the introduction of those standards. Lighting, occupancy, set point temperature profiles, air conditioning and ventilation services all increase the complexity of measuring insulation efficiency. The identification of thermal insulation failure can help to reduce energy consumption in heating systems. Conventional methods can be greatly improved through the application of hybridized machine learning techniques to detect thermal insulation failures when a building is in operation. A three-step procedure is proposed in this paper that begins by considering the local building and heating system regulations as well as the specific features of the climate zone. Firstly, the dynamic thermal performance of different variables is specifically modelled, for each building type and climate zone. Secondly, Cooperative Maximum-Likelihood Hebbian Learning is used to extract the relevant features. Finally, neural projections and identification techniques are applied, in order to detect fluctuations in room temperatures and, in consequence, thermal insulation failures. The reliability of the proposed method is validated in three winter zone C cities in Spain. Although a great deal of further research remains to be done in this field, the proposed system is expected to outperform conventional methods described in Spanish building codes that are used to calculate energetic profiles in domestic and residential buildings.

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HTB2: A flexible model for dynamic building simulation

Cited by 43 publications

References 0 publications

The impact of regulations on overheating risk in dwellings

The impact of regulations on overheating risk in dwellings

Energy Saving By Means Of Multiagent Systems And Fuzzy Systems

Improving Energy Efficiency in Buildings Using Machine Intelligence

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