Abstract
This chapter provides methodology for setting energy performance of residential buildings in European region and estimation of difference in energy performance as a tool for managers in public sector. The study deals with how to estimate energy performance for areas, where, due to its size, it is not possible to get technical parameters of particular buildings when data for setting energy performance are not or partially available and often inconsistent. Practical use of methodology is in providing essential data for subsequent determination of investment intensity of retrofit of buildings for the needs of public sector. It researches potential effects of retrofitting of all existing houses in the passive standard in region on overall improving of its energy performance. Final value represents maximum possible performance assuming all existing buildings corresponded to passive standard. This methodology can be generalized and used as tool for decision-making on energy performance at regional level in environment of insufficient data.
TopBackground
The first one involves publications dealing with a similar issue. That is, determining energy performance, but in case of available and valid data. Buratti et al. (2014) are dealing with the method on how to quickly verify the accuracy of the energy certificates, which is, in principle, a very similar topic. To solve this problem, they use the neuron network. Within regions, however, the lack of available data is a significant problem, which makes it impossible to use neural networks because there is not enough data available for its learning. Cajias and Piazolo (2013) solve a similar problem, but again they have enough data available, which allows the application of statistical methods. The significant difference here lies in the fact that authors are not bound in case of open data by the characteristics of a specific, precisely defined region and, as they state in the publication, they use data “from the German Investment Property Databank (IPD) from 2008 until 2010. We merged geographically referenced demographic and economic information, using German statistical office (DESTATIS) and identified residential buildings distributed mainly in southern Germany.” These are excellent and comprehensive information sources, but its analogy is not for use in other regions. Dascalaki et al. (2016) used data from the Episcope (n.d.) for their research, and, similarly to this paper, they created different scenarios of further development based on the summary of the data. This approach might be appropriate to solve our problem. However, as in previous studies, national data is being used, which allows for more extensive analyzes. Engvall et al. (2014) also use a wide database of data in their publication. For analysis purposes, authors also divide houses into groups according to the time of construction, as it is in this paper. There is enough data available for the researched area, so if the same data availability is possible for the territory researched by us, this methodology could be used. Kontokosta (2015) based the research on the use of robust and available data in sufficient numbers for the US, New York. Besides, the proposed methodology works with data in time series. The author states: “First, sufficient data must be available to perform regression analyses and to achieve reasonable model significance.” This statement demonstrates the necessity of making methodology in terms of lack of data, we suggest. Orosa (2012) worked with real sampled data and dealt with the issue of the relationship between energy performance and indoor temperature. Zhang et al. (2015) solved a similar problem but tried to figure out what energy performance of buildings after Retrofit will be. This means that authors already had data on existing buildings and researched how it would look when they are all renovated (retrofit).
Key Terms in this Chapter
EnerPhit Concept: Because it is not always possible to achieve the Passive House Standard for refurbishments of existing buildings, the EnerPhit concept is a certificate declaring Quality-Approved Energy Retrofit with Passive House Components.
Energy Efficient Economy: It is an approach proposed by the European Council for economy which is energy efficient.
Tabula Episcope: It is a project consisting of two particular parts TABULA and EPISCOPE. TABULA is a Typology Approach for Building Stock Energy Assessment and EPISCOPE is Energy Performance Indicator Tracking Schemes for the Continuous Optimisation of Refurbishment Processes in European Housing Stocks.
Energy Performance: It is indicator describing the energy consumption of an building. It should be calculated on the basis of a methodology, which may be differentiated at national and regional level. That includes, in addition to thermal characteristics, other factors that play an increasingly important role such as heating and air-conditioning installations, application of energy from renewable sources, passive heating and cooling elements, shading, indoor air-quality, adequate natural light and design of the building.
Directive of the European Parliament and Council 2010/31/EU on the Energy Performance of Buildings: The directive determining the energy performance of buildings on the EU territory. It requests increasing of energy performance of buildings.
Energy Certificate: Energy certificate is a record or guarantee, in relation to the amount of a specific type of energy or material goods consumed by an energy conversion device in the production of a quantity of energy or material goods and/or the attributes of the method and quality of its production.
Passive House: Standard for energy efficiency in a building, which reduces the building's ecological footprint. It results in ultra-low energy buildings that require little energy for space heating or cooling.