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Gillich, A, Sunikka-Blank, M and Ford, A (2017) Lessons for the UK green deal from the US BBNP. Building Research & Information, 45(04), 384-95.
Goulden, S, Erell, E, Garb, Y and Pearlmutter, D (2017) Green building standards as socio-technical actors in municipal environmental policy. Building Research & Information, 45(04), 414-25.
Gram-Hanssen, K, Heidenstrøm, N, Vittersø, G, Madsen, L V and Jacobsen, M H (2017) Selling and installing heat pumps: Influencing household practices. Building Research & Information, 45(04), 359-70.
Guerra-Santin, O and Silvester, S (2017) Development of Dutch occupancy and heating profiles for building simulation. Building Research & Information, 45(04), 396-413.
- Type: Journal Article
- Keywords: occupancy profiles; occupant behaviour; retrofit; simulation tools; energy demand; heating; personas; performance simulation; behavior; patterns; energy-consumption; model; prediction; performance analysis; demand; construction & building technology;
- ISBN/ISSN: 0961-3218
- URL: https://doi.org/10.1080/09613218.2016.1160563
- Abstract:
Building simulations are often used to predict energy demand and to determine the financial feasibility of the low-carbon projects. However, recent research has documented large differences between actual and predicted energy consumption. In retrofit projects, this difference creates uncertainty about the payback periods and, as a consequence, owners are reluctant to invest in energy-efficient technologies. The differences between the actual and the expected energy consumption are caused by inexact input data on the thermal properties of the building envelope and by the use of standard occupancy data. Integrating occupancy patterns of diversity and variability in behaviour into building simulation can potentially foresee and account for the impact of behaviour in building performance. The presented research develops and applies occupancy heating profiles for building simulation tools in order create more accurate predictions of energy demand and energy performance. Statistical analyses were used to define the relationship between seven most common household types and occupancy patterns in the Netherlands. The developed household profiles aim at providing energy modellers with reliable, detailed and ready-to-use occupancy data for building simulation. This household-specific occupancy information can be used in projects that are highly sensitive to the uncertainty related to return of investments.;Building simulations are often used to predict energy demand and to determine the financial feasibility of the low-carbon projects. However, recent research has documented large differences between actual and predicted energy consumption. In retrofit projects, this difference creates uncertainty about the payback periods and, as a consequence, owners are reluctant to invest in energy-efficient technologies. The differences between the actual and the expected energy consumption are caused by inexact input data on the thermal properties of the building envelope and by the use of standard occupancy data. Integrating occupancy patterns of diversity and variability in behaviour into building simulation can potentially foresee and account for the impact of behaviour in building performance. The presented research develops and applies occupancy heating profiles for building simulation tools in order create more accurate predictions of energy demand and energy performance. Statistical analyses were used to define the relationship between seven most common household types and occupancy patterns in the Netherlands. The developed household profiles aim at providing energy modellers with reliable, detailed and ready-to-use occupancy data for building simulation. This household-specific occupancy information can be used in projects that are highly sensitive to the uncertainty related to return of investments.;Building simulations are often used to predict energy demand and to determine the financial feasibility of the low-carbon projects. However, recent research has documented large differences between actual and predicted energy consumption. In retrofit projects, this difference creates uncertainty about the payback periods and, as a consequence, owners are reluctant to invest in energy-efficient technologies. The differences between the actual and the expected energy consumption are caused by inexact input data on the thermal properties of the building envelope and by the use of standard occupancy data. Integrating occupancy patterns of diversity and variability in behaviour into building simulation can potentially foresee and account for the impact of behaviour in building performance. The presented research develops and applies occupancy heating profiles for building simulation tools in order create more accurate predictions of energy demand and energy performance. Statistical analyses were used to define the relationship between seven most common household types and occupancy patterns in the Netherlands. The developed household profiles aim at providing energy modellers with reliable, detailed and ready-to-use occupancy data for uilding simulation. This household-specific occupancy information can be used in projects that are highly sensitive to the uncertainty related to return of investments.;Building simulations are often used to predict energy demand and to determine the financial feasibility of the low-carbon projects. However, recent research has documented large differences between actual and predicted energy consumption. In retrofit projects, this difference creates uncertainty about the payback periods and, as a consequence, owners are reluctant to invest in energy-efficient technologies. The differences between the actual and the expected energy consumption are caused by inexact input data on the thermal properties of the building envelope and by the use of standard occupancy data. Integrating occupancy patterns of diversity and variability in behaviour into building simulation can potentially foresee and account for the impact of behaviour in building performance. The presented research develops and applies occupancy heating profiles for building simulation tools in order create more accurate predictions of energy demand and energy performance. Statistical analyses were used to define the relationship between seven most common household types and occupancy patterns in the Netherlands. The developed household profiles aim at providing energy modellers with reliable, detailed and ready-to-use occupancy data for building simulation. This household-specific occupancy information can be used in projects that are highly sensitive to the uncertainty related to return of investments.;
Haddad, S, Osmond, P and King, S (2017) Revisiting thermal comfort models in Iranian classrooms during the warm season. Building Research & Information, 45(04), 457-73.
Moore, T, Ridley, I, Strengers, Y, Maller, C and Horne, R (2017) Dwelling performance and adaptive summer comfort in low-income Australian households. Building Research & Information, 45(04), 443-56.
Wade, F, Shipworth, M and Hitchings, R (2017) How installers select and explain domestic heating controls. Building Research & Information, 45(04), 371-83.
Wallhagen, M, Malmqvist, T and Eriksson, O (2017) Professionals' knowledge and use of environmental assessment in an architectural competition. Building Research & Information, 45(04), 426-42.