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Messung von Energiearmut trotz Datenmangels.
Abstract
In vielen Ländern Europas werden heute Debatten bezüglich der Umgestaltung des Energiesektors geführt, mit dem Ziel, die Abhängigkeit von fossilen Brennstoffen zu reduzieren und dem Klimawandel entgegenzuwirken. Zwar gewinnt das Thema Energiearmut in diesem Kontext vermehrt an Bedeutung (Schaffrin, 2013), aber es bestehen weiterhin große Unterschiede hinsichtlich der Stellung, die Energiearmut in der politischen Wahrnehmung in unterschiedlichen Ländern einnimmt. In Deutschland beispielsweise hat das Thema erst im Rahmen der Debatte um die Kosten der Energiewende die öffentliche und politische Aufmerksamkeitsschwelle überschritten.
... The method was applied in Karlsruhe, Germany where information on the heating sources was made available at the level of a town (Stadtteil) with the help of a commercial database solution called INFAS, [108]. This solution has been used previously with other scientific publications, see [107]. Statistical information with regards to the average GHG emissions (CO 2 equivalent was obtained from the IWU institute, [88]. ...
With energy modeling at different complexity levels for smart cities and the concurrent data availability revolution from connected devices, a steady surge in demand for spatial knowledge has been observed in the energy sector. This transformation occurs in population centers focused on efficient energy use and quality of life. Energy-related services play an essential role in this mix, as they facilitate or interact with all other city services. This trend is primarily driven by the current age of the Ger.: Energiewende or energy transition, a worldwide push towards renewable energy sources, increased energy use efficiency, and local energy production that requires precise estimates of local energy demand and production. This shift in the energy market occurs as the world becomes aware of human-induced climate change, to which the building stock has a significant contribution (40% in the European Union). At the current rate of refurbishment and building replacement, of the buildings existing in 2050 in the European Union, 75% would not be classified as energy-efficient. That means that substantial structural change in the built environment and the energy chain is required to achieve EU-wide goals concerning environmental and energy policy. These objectives provide strong motivation for this thesis work and are generally made possible by energy monitoring and modeling activities that estimate the urban energy needs and quantify the impact of refurbishment measures.
To this end, a modeling library called aEneAs was developed in the scope of this thesis that can perform city-wide building energy modeling. The library performs its tasks at the level of a single building and was a first in its field, using standardized spatial energy data structures that allow for portability from one city to another. For data input, extensive use was made of digital twins provided from CAD, BIM, GIS, architectural models, and a plethora of energy data sources. The library first quantifies primary thermal energy demand and then the impact of refurbishment measures. Lastly, it estimates the potential of renewable energy production from solar radiation. aEneAs also includes network modeling components that consider energy distribution in the given context, showing a path toward data modeling and simulation required for distributed energy production at the neighborhood and district level.
In order to validate modeling activities in solar radiation and green façade and roof installations, six spatial models were coupled with sensor installations. These digital twins are included in three experiments that highlight this monitoring side of the energy chain and portray energy-related use cases that utilize the spatially enabled web services SOS-SES-WNS, SensorThingsAPI, and FIWARE. To this author's knowledge, this is the first work that surveys the capabilities of these three solutions in a unifying context, each having its specific design mindset.
The modeling and monitoring activity and their corresponding literature review indicated gaps in scientific knowledge concerning data science in urban energy modeling. First, a lack of standardization regarding the spatial scales at which data is stored and used in urban energy modeling was observed. In order to identify the appropriate spatial levels for modeling and data aggregation, scale is explored in-depth in the given context and defined as a byproduct of resolution and extent, with ranges provided for both parameters. To that end, a survey of the encountered spatial scales and actors in six different geographical and cultural settings was performed. The information from this survey was used to put forth a standardized spatial scales definition and create a scale-dependent ontology for use in urban energy modeling. The ontology also provides spatially enabled persistent identifiers that resolve issues encountered with object relationships in modeling for inheritance, dependency, and association. The same survey also reveals two significant issues with data in urban energy modeling. These are data consistency across spatial scales and urban fabric contiguity. The impact of these issues and different solutions such as data generalization are explored in the thesis.
Further advancement of scientific knowledge is provided specifically with spatial standards and spatial data infrastructure in urban energy modeling. A review of use cases in the urban energy chain and a taxonomy of the standards were carried out. These provide fundamental input for another piece of this thesis: inclusive software architecture methods that promote data integration and allow for external connectivity to modern and legacy systems. In order to reduce time-costly extraction, transformation, and load processes, databases and web services to ferry data to and from separate data sources were used. As a result, the spatial models become central linking elements of the different types of energy-related data in a novel perspective that differs from the traditional one, where spatial data tends to be non-interoperable / not linked with other data types. These distinct data fusion approaches provide flexibility in an energy chain environment with inconsistent data structures and software. Furthermore, the knowledge gathered from the experiments presented in this thesis is provided as a synopsis of good practices.
Typology of residential buildings in Germany. Exemplary measures to improve the energy performance, demonstrated by use of showcase buildings
Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies conceptually and practically integrate urban spatial and energy planning approaches. The combined modelling approach that will be developed based on the described sectorial models holds the potential to represent hybrid energy systems coupling distributed generation of electricity with thermal conversion systems.
We present ongoing work on the construction of a spatial microsimulation model to assess the influence of demographics on residential heat consumption for Hamburg, Germany. Demographics are important for urban energy planning as: (1) Buildings are becoming more energy-efficient and building occupant behaviour accounts for a growing share in the variation of consumption; (2) building occupant needs are changing along with demographic change; and (3) the share of small decentralized district heating grids, in which fewer customers mean less averaging out of heterogeneous occupant profiles, is set to play a bigger role in the country’s heat supply. We construct a spatial microdata set for the city of Hamburg (of roughly 1.8 million inhabitants and 370 000 buildings), with households populating geo-referenced buildings, in three steps: (a) Synthesizing the population of small scale “statistical areas”, comprising up to around 2 000 people (we do this by selecting households recorded in the German microcensus and fitting them into the statistical areas); (b) assigning energy relevant properties to the geo-referenced buildings from the Hamburg digital cadaster (we do this by making use of a well-established building typology developed for energy assessment) and constructing dwelling units in these buildings; and (c) matching households to the dwelling units in these buildings (which we do again by using household data from the microcensus). This last step – allocating households to buildings – may be the most interesting and challenging task. As of to date, we use a combinatorial optimization algorithm to achieve this. Once we have a microsimulation model of buildings and households living in them, including their demographic composition, the range of questions that can be explored is immense. The illustration presented here is a simple heat balance computation of individual buildings, using the constructed socio-demographic data and the digital cadaster data as input parameters.
Our aim is better understanding of the theoretical heat-energy demand of different types of urban form at a scale of 500 m x 500 m. The empirical basis of this study includes samples of dominant residential building typologies identified for Paris, London, Berlin, and Istanbul. In addition, archetypal idealised samples were created for each type through an analysis of their built form parameters and the removal of unwanted 'invasive' morphologies. The digital elevation models of these real and idealised samples were run through a simulation that modelled solar gains and building surface energy losses to estimate heat-energy demand. In addition to investigating the effect of macroscale morphological parameters, microscale design parameters, such as U-values and glazing ratios, as well as climatic effects were analysed. The theoretical results of this study suggest that urban-morphology-induced heat-energy efficiency is significant and can lead to a difference in heat-energy demand of up to a factor of six. Compact and tall building types were found to have the greatest heat-energy efficiency at the neighbourhood scale while detached housing was found to have the lowest.
Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing
demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a
considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system
models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a
spatial representation in order to reflect the local context and the boundary conditions.
This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy
demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart
grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to
target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual
components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where
heterogeneous models are spatially represented.
Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system
models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for
spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers
(e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit
from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for
energy use at component level. The proposed modelling strategies conceptually and practically integrate urban spatial and energy
planning approaches. The combined modelling approach that will be developed based on the described sectorial models holds the
potential to represent hybrid energy systems coupling distributed generation of electricity with thermal conversion systems.
In 2012, the Low Income High Costs (LIHC) indicator was proposed as an alternative way of measuring fuel poverty (Hills, 2012) [27]. Since its publication, the indicator has received considerable attention, not only in Great Britain, but also in other European countries. The applicability of the indicator is, however, highly contingent on detailed household and building data. This leads to the question of whether it is feasible to use the indicator in countries with less extensive available data. In this study, we test the applicability of the LIHC indicator in France, using an innovative approach to estimate energy requirements in locations with limited availability of physical building data. We show how this enables us to conveniently adapt the two most frequently used indicators to the French context (and possibly to other countries) and how to compare their results.
Since its publication in the early 90s, Brenda Boardman's Fuel Poverty has been the reference text for those wishing to learn about this complex subject. In this, its successor, she turns a critical eye to the new millennium and finds that the situation, while now more widely recognised, is far from having improved. The book begins by discussing the political awakening to the issue and exploring just who constitutes the fuel poor. It examines the factors that contribute to fuel poverty - low incomes, high fuel prices and poor quality housing - and looks at and evaluates the policies that have been employed to help reduce the problem. The latter part presents a detailed set of proposals based around long-term improvements in the housing stock that must be employed if we are to avoid a dire situation continuing to get worse. Based on detailed analysis of the situation in the UK, the growth of fuel poverty (sometimes called energy poverty) in other countries and the new focus in European policy makes the book timely and provides important lessons for those who now have to produce policies to tackle the issues.
Measures of affordability and of fuel poverty are applied in practice to assess the affordability of energy services, for example, or of water or housing. The extensive body of literature on affordability measures has little overlap with the existing literature on poverty measurement. A comprehensive assessment of the response of affordability measures as a result of changes in the distribution of income or expenditure (the dynamic properties) is missing. This paper aims to fill this gap by providing a conceptual discussion on the ‘dynamics’ of both energy affordability measures and fuel poverty measures. Several types of measures are examined in a microsimulation framework. Our results indicate that some measures exhibit odd dynamic behavior. This includes measures used in practice, such as the low income/high cost measure and the double median of expenditure share indicator. Odd dynamic behavior causes the risk of drawing false policy recommendations from the measures. Thus, an appropriate response of affordability measures to changes in relevant variables is a prerequisite for defining meaningful measures that inform about affordability or deprivation in certain domains of consumption.
Indicators of energy affordability and fuel poverty are a powerful tool to identify the most vulnerable households and to avoid imposing excessive burdens by climate policy. Fuel-poverty measurement consists of two independent parts: first, the definition of an appropriate fuel-poverty
line, and secondly, techniques to measure fuel poverty. This paper reviews options for the definition of fuel-poverty lines and measurement techniques. Based on household data from Germany, figures that would result from different fuel-poverty lines are derived. The choice of the fuel-poverty
line matters decisively for the resulting assessment. Existing fuel-poverty lines should undergo empirical reexamination and then be adapted to the case of Germany.
International agreements have led to an increase of national climate policies. The selection and analysis of policy instruments are mainly oriented at their technical effectiveness and financial feasibility while social sustainability plays a minor role. As a consequence, the impact of climate policies on individuals’ life situation is largely unknown even though the design of these policies suggests effects on national and international patterns of income inequality. Focusing on the residential sector, this paper investigates how national climate policy strategies affect the relationship between income and utility costs of households within nation states. It is argued that the effect of climate policy on income inequality depends on the specific design of these policies. Market-based solutions of energy efficient housing insulation, for example, focus more on effectiveness and less on social justice. In contrast, financial benefits from regulatory climate policies like eco-taxes on fuel can be configured as an instrument for income-redistribution. This paper sets a link between environmental politics and social justice research in a multi-level approach focusing on inequalities in housing utility costs among socio-economic groups. The analysis is based on EU SILC data which is combined with climate policy instruments in the field of housing.
This paper outlines why the definition of fuel poverty is important in policy formulation and describes how the Government's current definitions evolved from the original concept. It discusses the determination of income and fuel costs and the possibilities for a relative and common European measure. It examines problems inherent in assessing fuel costs as a percentage of income and puts forward the arguments for a ‘budget standard’ approach. The paper illustrates how the size of the problem depends on the definition and chosen threshold and suggests advantages for a rating scale. It illustrates how the income composition and thresholds also govern the distribution of the target populations and the relative importance of the main causal factors, and examines the consequent policy implications. It explores the definition of vulnerable households and the importance of severity and questions whether the UK fuel poverty strategy is targeted at households least able to afford their fuel costs (as the name implies) or primarily those at risk from excess winter and summer mortality and morbidity. Finally, after examining the role of supplementary indicators, it looks at the opportunities for changing the definition and comments on the Government review of the definition and targets.
Throughout the industrialised world, fuel poverty is the most commonly accepted term with which to describe a household's inability to afford basic standards of heat, power and light. Whilst the term gained widespread acceptance with the publication of the UK's Fuel Poverty Strategy in 2001, little is known about the origins of the term itself. This paper traces the earliest formulations of the concept, focusing particularly on the 10% needs to spend threshold which was adopted in 1991 and remains in place some 20 years later. This paper argues that understanding more about the origins of this threshold yields a more critical understanding of why fuel poverty targets in the UK have not been reached, and enables a more informed approach to setting realistic targets for the future. It also provides an opportunity to explore regional disparities in UK fuel poverty prevalence, highlighting the extent to which rigid adherence to a 10% threshold has created an unstable regional mosaic of over-estimation and under-estimation.
Energy efficiency and social programmes have failed to stem the dramatic increase in the number of fuel poor households in recent years. As the 2016 deadline for eradicating fuel poverty nears, energy efficiency and fuel poverty programmes are undergoing significant changes. The ambitions for Britain's Green Deal, the overhaul of supplier obligations alongside the winding down of Warm Front, and the introduction of an incentive for renewable heat combine to form a sea change in how energy efficiency and fuel poverty objectives are financed and delivered. Green Deal Finance (GDF) eliminates the up-front capital cost of energy efficiency measures to the household by linking repayments to energy savings and spreading them over many years. This paper asks whether and how GDF could be beneficial to fuel poor households. Using scenarios modelled on the English House Condition Survey, it explores the extent to which fuel poverty could be reduced, allowing for repayments incurred by GDF. It examines how much further fuel poverty could be alleviated were the capital cost subsidised or repayments supported, and concludes that a flexible design for GDF is necessary if it is to contribute to alleviating fuel poverty.
There is a growing interest in reducing energy consumption and the associated greenhouse gas emissions in every sector of the economy. The residential sector is a substantial consumer of energy in every country, and therefore a focus for energy consumption efforts. Since the energy consumption characteristics of the residential sector are complex and inter-related, comprehensive models are needed to assess the technoeconomic impacts of adopting energy efficiency and renewable energy technologies suitable for residential applications.The aim of this paper is to provide an up-to-date review of the various modeling techniques used for modeling residential sector energy consumption. Two distinct approaches are identified: top-down and bottom-up. The top-down approach treats the residential sector as an energy sink and is not concerned with individual end-uses. It utilizes historic aggregate energy values and regresses the energy consumption of the housing stock as a function of top-level variables such as macroeconomic indicators (e.g. gross domestic product, unemployment, and inflation), energy price, and general climate. The bottom-up approach extrapolates the estimated energy consumption of a representative set of individual houses to regional and national levels, and consists of two distinct methodologies: the statistical method and the engineering method.Each technique relies on different levels of input information, different calculation or simulation techniques, and provides results with different applicability. A critical review of each technique, focusing on the strengths, shortcomings and purposes, is provided along with a review of models reported in the literature.
This paper explores the effects of urban texture on building energy consumption. It is based on the analysis of digital elevation models (DEMs)—raster models of cities which have proven to be very effective in the urban context. Different algorithms are proposed and discussed, including the calculation of the urban surface-to-volume ratio and the identification of all building areas that are within 6 m from a façade (passive areas). An established computer model to calculate energy consumption in buildings, the LT model, is coupled with the analysis of DEMs, providing energy simulations over extensive urban areas. Results for the three case study cities of London, Toulouse and Berlin are presented and discussed.
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