Abstract
ABSTRACTBuilding 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.
Highlights
The building stock in the Netherlands consists of 7.5 million dwellings (CBS, 2014)
The goal of this research is the development of occupancy and heating profiles that can be applied to building simulation tools to predict more accurately and to fine-tune the energy performance of the building
It is important to add that the development of occupancy and heating profiles in this paper aimed at determining household-specific profiles, and not with the intention of predicting occupancy patterns or energy consumption
Summary
The building stock in the Netherlands consists of 7.5 million dwellings (CBS, 2014). Dwellings of the postwar period account for approximately one-third of the residential stock (Itard & Meijer, 2008); a large number of these properties are in need of renovation. There are approximately 400 housing associations in the Netherlands that manage 2.4 million residential properties, constituting 34% of the total housing stock (Aedes, 2013). Dutch housing associations have the ambition of achieving an energy rating of C for 80% of their properties and an average rating B by 2020 (Aedes, 2013), while currently the average rating for the post-war building according to AgentschapNL (2011) is D–E (approximately 350–400 kWh/m2/year primary energy), resulting in an expected energy consumption of approximately 20 000 kWh/dwelling/year. There is a lack of fast, affordable and robust processes for largescale building renovation This problem is magnified in multi-family rented buildings in which the incentives for saving energy and increasing indoor comfort are split between owners and tenants, increasing the risk of a large gap between the predicted and actual energy consumption
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
