The EERAdata project is developing a decision-support tool (DST) enabling regional and municipal policymakers to plan and prioritise energy efficiency (EE) investments in buildings based on a holistic assessment of their building data.
In this article, our partner the Technical University of Denmark (DTU) explores different methodologies to assess resource efficiency of buildings that will be used to inform the development of the EERAdata decision-support methodology.
Methodologies used to estimate building performance and evaluate impacts of energy efficiency upgrades and supply-side investments are core elements of EERAdata.
Incentives to make such upgrades generally rely on the ratio between the costs and the benefits of the actions. Benefits can be evaluated with a rather high certainty when the investment is only weighted against the profitability of higher energy efficiency.
It is far more complex to estimate the value of an improved indoor environment that follows from upgrading building envelopes, ventilation systems or other building features that may affect the indoor environment.
Effects of insufficient indoor environment quality on people are multi-faceted and can result in building-related symptoms, discomfort, reduced work performance, absenteeism due to own or children’s disease, hospitalization, and in severe cases, even death. The socio-economic effects of indoor environment exposures are wide-ranging and not at all straightforward.
As a first step in establishing this component of the EERAdata methodology, we carried out a detailed review of existing related methodologies to assess resource efficiency of buildings. The review comprised more than 20 methodologies of highly varying relevance for EERAdata. Here we describe examples of methodologies and procedures to implement in the EERAdata decision support tool with a focus on the wider benefits.
Indoor environment quality
Algorithms to evaluate the health, comfort and performance impacts that result from energy efficiency improvements are treated differently in different studies. A recent Danish study used newly developed associations between the indoor environment in classrooms and pupil performance and absenteeism to estimate the socio-economic value of upgrading the indoor environment. Children spend more time indoors at home rather than in school, where a poor classroom environment may cause reduced concentration, discomfort or absenteeism. This may affect their learning efficiency and their grades, their choice of education and their salaries during their working life.
Indoor environment is often classified according to its quality. For example, the temperature or the air quality may be tightly controlled in a comfortable range throughout the year, in which case the quality category will be high. Temperature or air quality may also drift in wider ranges, in which case the distribution of the quality category will be more uneven.
In a school, the latter situation may result in periods during the year with deteriorated pupil performance. Based on the Danish study, the economic benefits that resulted from even a modest indoor environment upgrade were estimated to a net present value of €1.9M over a 30-year period due to reduced illness absence and €1.2M due to improved learning. This example applied to a school with 650 pupils and 50 teachers in which it was assumed that 80% of the lessons were affected by the improved conditions. Similar estimations can be made also for other building types and uses, but generally, this requires that associations between indoor environment exposures and relevant outcome metrics exist. The task now is to extract functional information on such associations from the vast body of literature in the field.
The indoor environment comprises a multitude of parameters associated with the thermal, atmospheric, acoustic and visual exposures. Naturally, the thermal conditions indoors and the heating and cooling energy use are closely related with retrofit of the building envelope or of heating, ventilation or cooling systems. The thermal conditions alone include a range of parameters, such as air and surface temperatures, air humidity, and air velocity, all of which vary both spatially and temporally. Retrofits may affect also daylight distribution, sound pressure levels, concentrations of airborne particular matter, and concentrations of volatile organic compounds and other indoor air pollutants. Quantification of indoor environment exposures in buildings is highly complex and for many of these parameters not reliable or even possible with our current knowledge. Development of this component of the EERAdata methodology is expected to require a number of compromises between desired tool functionality and the complexity of the included algorithms.
As in the reviewed existing methodologies, the development of the EERAdata methodology faces a range of challenges, including an almost unlimited number of potential model inputs and outputs. Variation in building type and use implies that different outcome metrics are relevant for schools, administrative, residential or other building types. Alignment of inputs between the different components in the tool are currently discussed at frequent online meetings between the project partners and considerable progress is foreseen in the coming months.