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Energy performance indices are widely used methods for determining and assessing the efficiency of buildings in operation. However, methodologies for indices that compare heat consumption among buildings and at higher temporal granularity than yearly are needed. This makes it possible for consumers to compare themselves to others to encourage them to improve their energy efficiency. For a district heating system, it also provides the district heating company with an overview of consumers' energy efficiency (and consumers' efficiency must be comparable). This is critical due to the interconnectedness of district heating systems, as consumers can indirectly have negative impacts on each other's operations. Having an overview of the efficiency of consumers can provide useful information for the district heating companies to detect unusual consumption and possible faults. This paper therefore has created a framework for the comparison of heat consumption among buildings, by taking into account various contextualities to make comparisons possible. The framework has been tested on a case study, and its capabilities are showcased. • Leveraging building metadata to allow energy consumption comparison among buildings. • Creation of heating consumption performance indexes for use by end-consumers. • Enabling of higher temporal resolution performance indexes increasing usability. • Fault detection based on performance indexes. • Showcasing of methodology on real consumption data from a suburb in Denmark. [ABSTRACT FROM AUTHOR]

Highlights • Net Present Value (NPV) investment analysis is simulated using significant factors. • District heating (DH) price is simulated using a dynamic pricing model. • Waste heat to DH from medium and large DCs provide positive NPV with high probability. • Small DCs are sensitive to input factors and may require alternative customers to DH. • The most influential factors are heat and electricity price and operative labor costs. Abstract The growth of computing and Internet use have attracted the attention of the general public concerning the carbon footprint of data centers (DCs). Previous research has focused on the implementations of energy efficiency activities. However, little research has been published on the economic evaluation of waste heat utilization in the DC industry. This paper aims to provide an economic investment assessment of DC waste heat utilization. The contribution of this paper is the assessment of three different sized cases with realistic input factors affecting the net present value (NPV) model. We contribute to the ongoing discussion on the energy efficiency of DCs and provide a transparent assessment model for DC and district heating operators. We identified the positive NPV cases with high probability. The medium case has an NPV of 1.04 M€ (with uncertainty, the results range from −0.332 M€ to 2.57 M€). The large case has an NPV of 16.3 M€ (with uncertainty, the results range from 4.1 M€ to 30.2 M€). Both of these are clear-cut waste heat utilization investment proposals. The small case NPV is −48.5 k€ (with uncertainty, the results range from −264 k€ to 143 k€). The small case is sensitive to input factor values. [ABSTRACT FROM AUTHOR]

This paper presents the editorial for the Renewable and Sustainable Energy Reviews joint special issue devoted to the research work discussed and presented at the 11th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES), held from the 4th September to the 9th September 2016 in Lisbon, Portugal and the 2nd South East European (SEE) SDEWES Conference held from June 15th to June 18th, 2016 in Piran, Slovenia. This special issue is in line with the journal's aim of publishing research from across the ever-broadening field of renewable and sustainable energy with a strong review element. Previous SDEWES conference special issues have gathered a significant knowledge base in the field of sustainable development that reflects the continuous research efforts of the SDEWES research community. Therefore, this editorial provides not only an overview of the papers published in this particular special issue, but also a wider overview of the current trends in the domain of sustainable and renewable energy. This year's special issue focuses particularly on the benefits of the bio-based economy, energy security issues, fossil fuel thermal plant alternatives and environmental constraints, district heating and cooling together with cross sector energy efficiency and energy conservation issues. Sustainable transport systems, the issue of fuel poverty in urban neighbourhoods and renewable energy to support development of peripheral rural areas, optimising passive building design for hot climates and solar-powered heating and cooling are further topics featured in this special issue. In the process of selecting papers for this special issue, the guest editors invited in total 23 extended manuscripts for consideration for publication. After a rigorous review process by expert reviewers overseen by the guest editors a total of 16 articles were accepted for publication. [ABSTRACT FROM AUTHOR]

The paper analyses possibilities for CO 2 emission reduction into the centralized district heating system by using the index decomposition analysis: Kaya identity equation. The classical Kaya equation is complemented with an energy efficiency indicator of a district heating system. The paper focuses on the impact of the Kaya equation components on the CO 2 emission reduction. The elaborated methodology is tested in order to analyze possibilities for CO 2 emission reduction at district heating systems in Latvia. 3 scenarios were formed for the forecast of the CO 2 emission reduction. In case the current model for the state development is continued (scenario1) CO 2 emissions will be reduced for 13%; however, in the scenario of orderly and balanced DH systems they will reduce for 29% by 2020 as compared to 2012. [ABSTRACT FROM AUTHOR]

Thermal grids will play a key role in the development of local energy communities and the achievement of 100% renewable societies. Such systems allow excess heat produced by distributed producers through renewable energy sources (also referred to as "thermal prosumers") to be shared among other consumers characterized by high heat demand or who still depend on fossil fuels. However, to achieve more reliable results when performing energy analyses, it is of utmost importance to develop models of prosumers' substations, where technical details (e.g., type of connections, heat exchangers, valves, etc.) and controllers are accounted for. Starting from the layout of a bidirectional substation for a thermal energy network proposed in the literature, this paper proposes a dynamic model that replicates the experimental setup in the TRNSYS environment. Validation results show a good matching between simulation and experiments in terms of dynamic behavior and energy balance. To show the capabilities of the proposed model, a prosumer with heat available from 205 m2 solar thermal collectors is considered as a case study. The analysis is performed by assuming two locations characterized by different irradiation values, i.e., Palermo (Italy) and Berlin (Germany). The results show that exchanging the excess heat produced on-site with a heating network allows the solar collectors to reach peak heat production, which is 130 kW and 110 kW for Palermo and Berlin, respectively. The surplus heat sold to the network is equal to 66% and 29% of the total energy exchange within the substation for Palermo and Berlin, respectively. Conversely, the self-consumption of the produced heat accounts for 21.2% and 30.6%, respectively. The model prospectively represents a valuable tool to develop feasibility studies in Thermal Energy Communities and assess the potential of innovative energy- and cost-effective operation strategies. • A model of a thermal prosumer substation exchanging with a District Heating Network (DHN) was created in TRNSYS • The virtual model was validated through experimental data by showing a good matching between simulation and experiments • To show the capabilities of the model, a prosumer equipped with 205 m2 solar thermal collectors is considered as a case study • Results showed that the prosumer substation allowed for optimal exploitation of the heat produced by the solar collectors • The proposed model represents a viable tool to perform analyses applied to renewable energy systems in prosumers networks [ABSTRACT FROM AUTHOR]

Abstract: This paper discusses the opportunities and challenges of implementing District Heating (DH) in Canada, with focus on the network design and operation. We selected for case study an urban area in Ottawa. First, we proved that the medium-temperature district heating (MTDH) (70°C≤ T supply ≤90°C) had better energy delivery performance than high-temperature district heating (HTDH) (T supply >100°C), decreasing the heat loss by approximately 40%. The low-temperature networks (T supply <60°C) achieved even lower heat losses, but they required additional capital investment. The implementation of low-temperature district heating (LTDH) should be considered, thanks to the capability of including more renewable energy and excess industrial waste heat. Next, the simulations show that DH can be implemented to supply present heating loads with medium temperature DH, and operate in the future at low temperature, after energy saving measures have been implemented in the buildings. Areas having linear heat densities greater than 3MWh/(myr) could economically be supplied by DH. Areas with linear heat density below 1.5MWh/(myr) are considered not practically feasible with the current energy market situation in Canada. The paper discusses critical issues and quantifies the performance of design concepts for DH supply to low heat density areas. DH is a fundamental energy infrastructure and is part of the solution for sustainable energy planning in Canadian communities. [Copyright &y& Elsevier]

Projects like the 4DH research platform and Heat Roadmap Europe have successfully demonstrated that district heating is the only viable solution to efficiently utilize both low-grade renewable energy, excess heat from other sources as well as waste heat for providing heat for space heating and domestic hot water purposes. Nevertheless, there are open questions regarding cost competitiveness of district heating in combination with low energy buildings of the future. In fact, one of the challenges district heating is facing, is the general perception that district heating is too investment intensive compared to individual solutions. In many cases that perception is also used to imply that district heating has no future with the introduction of strict building energy codes that require new buildings to fulfill low energy buildings standards. In this paper, the levelized cost of heating using district heating and individual heating solutions are compared by looking at a concrete area where both the heat demand per square meter as well as the distance between buildings are varied. This study thus analyses when a 4th generation district heating system is competitive, under different linear heat densities. [ABSTRACT FROM AUTHOR]

Abstract For the last decades energy efficiency initiatives have avoided enormous amounts of energy consumption, to the favor of the environment and consumer expenditures. Although there is still a big potential for further energy efficiency improvements it is time to move further and start preparing the whole energy system for the challenge of oversupply from intermittent renewable energy sources, particularly in the power sector. In 2015 the maximum one-hour power oversupply from wind turbines alone in Denmark happened the 26th of June, peaking at around 900 MW and the oversupply over a 15-hour period was 10 GWh. Known solutions to make use of oversupply in the power sector are power export, energy storage and halting the power generation. The power export needs to rely on sufficient capacity at local interconnectors, power demand in the importing country and there is an economic gain in the export. Energy storages can range from storing of power in batteries, pumped hydro, synthetic fuels to fuel displacing at other energy sectors. The last option is stopping the turbines, which should be avoided. The optimum energy storage would have large capacity, fast charging, high recovery efficiency and low cost. Scoring high on all criteria’s can be difficult when focusing on a single energy sector. By widening the perspective and start taking advantages of synergies between the energy sectors there is a possibility to score high on all three criteria’s. In this paper the potential of utilizing synergies between the power and heat sectors will be explored by considering the projections for the Danish energy system in 2025. The result of the analysis shows the optimum energy storage of renewable power is achieved through fuel displacement in the heating sector in combination with utility sized heat pumps, electric boilers and large thermal storages. [ABSTRACT FROM AUTHOR]

Data centers seek solutions to increase energy efficiency and lower costs by novel methods. Waste heat utilization is considered to be one of the major trends in the near future, especially in the Nordic countries, where heat demand is high. In this paper, waste heat utilization was analyzed from the perspectives of both the data center and district heating network operators. Timing of the data center waste heat production was considered based on an existing data center load profile. For the district heating network operator, the system level effects of increased waste heat utilization were quantified by simulating district heating production in the city of Espoo, Finland, with actual plant and heat demand data for 2013 and 2015. Results showed that with high shares of waste heat in the district heating system, i.e. 20–60 MW, the system level operational cost savings were 0.6–7.3% in the case study. Utilizing waste heat decreased utilization hours of both combined heat and power plants and heat-only boilers. The analysis showed that pricing of the procured waste heat affects the utilization level of waste heat, but operational hours of waste heat utilization were over 95% in all scenarios. [ABSTRACT FROM AUTHOR]

Modern ways of exploring the diagnostic knowledge provided by data mining and machine learning raise some concern about the ways of evaluating the quality of output knowledge, usually represented by information systems. Especially in district heating, the stationarity of efficiency models, and thus the relevance of diagnostic classification system, cannot be ensured due to the impact of social, economic or technological changes, which are hard to identify or predict. Therefore, data mining and machine learning have become an attractive strategy for automatically and continuously absorbing such dynamics. This paper presents a new method of evaluation and comparison of diagnostic information systems gathered algorithmically in district heating efficiency supervision based on exploring the evolution of information system and analyzing its dynamic features. The process of data mining and knowledge discovery was applied to the data acquired from district heating substations’ energy meters to provide the automated discovery of diagnostic knowledge base necessary for the efficiency supervision of district heating-supplied buildings. The implemented algorithm consists of several steps of processing the billing data, including preparation, segmentation, aggregation and knowledge discovery stage, where classes of abstract models representing energy efficiency constitute an information system representing diagnostic knowledge about the energy efficiency of buildings favorably operating under similar climate conditions and supplied from the same district heating network. The authors analyzed the evolution of a series of information systems originating from the same knowledge discovery algorithm applied to a sequence of energy consumption-related data. Specifically, the rough sets theory was applied to describe the knowledge base and measure the uncertainty of machine learning predictions of current classification based on a past knowledge base. Fluctuations of diagnostic class membership were identified and provided for the differentiation between returning and novel fault detections, thus introducing the qualities of information system uncertainty and its sustainability. The usability of the new method was demonstrated in the comparison of results for exemplary data mining algorithms implemented on real data from over one thousand buildings. [ABSTRACT FROM AUTHOR]

District heating is widely used in heating buildings in Finland. Due to new European legislation, increasing adaption of renewables, and new stakeholders in the heating business, Finnish district heating needs to evolve. This paper analyzes energy and emissions impacts of different heating energy scenarios in a typical Finnish district heated area. A period of 20 years (2015–2035) was studied assuming the future development of the building stock. The conservative, extensive and extreme scenarios assumed different amounts of solar energy and ground source heat pumps to be implemented as decentralized systems. In addition, two heat prosumer scenarios were analyzed, namely an industrial waste heat scenario and a modest solar heat prosumer scenario. Despite the increase of the buildings’ total floor area by about a third, the combined heating and domestic hot water consumption per floor area was estimated to decrease by about 36%. The extreme scenario with ground source heat pumps and a solar thermal system decreased the annual centralized heat production by 34% and the industrial waste heat scenario by 32%. The waste heat scenario decreased the emissions the most. [ABSTRACT FROM AUTHOR]

The paper includes description of an experiment where the insulation of 532 m long 1200 mm pipe section was replaced with prefabricated polyurethane (PUR) foam shells. As a result reduction of heat losses in comparison with old insulation by 3.35 times was calculated. After successful experiment full renovation of DH main pipe (total pipe length is 10730 m) insulation was performed. Renovation of main pipeline was finished in summer 2015. The first results show that relative heat losses in Eastern part of Tallinn supplied by renovated DH main pipe were decreased from 20.2% to 16.0%. [ABSTRACT FROM AUTHOR]

Identifying and harnessing renewable clean energy sources in crowded and polluted urban areas for heat production, in existing heating systems, represent an opportunity insufficiently exploited at present. Creating the optimum frame, in order to increase the participation share of renewable energies, represents nowadays a constant preoccupation and an important object in the field of energy. In this regard, the purposes of this paper are: identifying, analyzing and validating the specific performance indicators applicable in the comparative thermal energy analysis of the urban heating systems which quantifies the impact of integrating renewable energy sources, particularly thermal-solar energy, in the classical heating systems and the hybrid systems’ efficiency thus obtained, highlighting the importance of harnessing clean energies in crowded urban areas. The following concepts were approached simultaneously and synergistic: the energy efficiency of the thermal consumers and the harnessing efficiency of the renewable energy sources identified in urban areas in the buildings connected to the district heating system. Thoroughly, the performance indicators defined for the district heating systems were centralized and analyzed, achieving a synthesis of the specific indicators meant to analyze the hybrid heating systems which integrate technologies that harness renewable energy sources and a comparison of different suggested systems. [ABSTRACT FROM AUTHOR]

This paper investigates the integration of a district heating heat pump for the production of about 4.65 MW th with the drinking water network–playing the role of low temperature heat source -as an alternative to conventional fossil fuel heating. The heat recovery reduces water temperature from 15 °C to 12 °C, thus requiring partial reheating by the drinking water end-user that needs to be estimated to evaluate the energetic convenience of this solution. Heat transfer between water mains and surrounding soil is considered by a proper thermal model computing the temperature vs. time profile at nodes. The developed model, which exploits Epanet to simulate the water network, compares the primary energy consumption and CO 2 emissions of the studied system with a conventional district heating solution. Each component, which constitute the overall system, (i.e. heat pump, water network, heating by water end-user etc.) is analyzed and modelled. Assuming a fossil fuel based scenario, the investigated heat pump system reduces the overall primary energy consumption and CO 2 emission by about 3%. This value boosts to 41% in case all the electricity generation relies on renewables, thus proving this solution is a promising alternative to conventional district heating in future energy scenarios dominated by renewables. [ABSTRACT FROM AUTHOR]

Forecasting energy consumption in buildings is a key step towards the realization of optimized energy production, distribution and consumption. This paper presents a data driven approach for analysis and forecast of aggregate space and water thermal load in buildings. The analysis and the forecast models are built using district heating data unobtrusively collected from 10 residential and commercial buildings located in Skellefteå, Sweden. The load forecast models are generated using supervised machine learning techniques, namely, support vector machine, regression tree, feed forward neural network, and multiple linear regression. The model takes the outdoor temperature, historical values of heat load, time factor variables and physical parameters of district heating substations as its input. A performance comparison among the machine learning methods and identification of the importance of models input variables is carried out. The models are evaluated with varying forecast horizons of every hour from 1 up to 48 h. Our results show that support vector machine, feed forward neural network and multiple linear regression are more suitable machine learning methods with lower performance errors than the regression tree. Support vector machine has the least normalized root mean square error of 0.07 for a forecast horizon of 24 h. [ABSTRACT FROM AUTHOR]

Even though industry consumes nearly half of total energy production, the relative share of total energy consumption related to heating and operating buildings is growing constantly. The motivation for this study was to reveal the differences in electricity use and district heating consumption in school buildings of various ages during the working day and also during the night when human-based consumption is low. The overall aim of this study is to compare the energy (electricity and heating) consumption of six school buildings in Kuopio, Eastern Finland. The selected school buildings were built in different decades, and their ventilation and building automation systems are also inconsistent. The hourly energy consumption data was received from Kuopion Energia, the local energy supply company. In this paper, the results of data analysis on the energy consumption in these school buildings are presented. Preliminary results show that, generally speaking, new school buildings are more energy-efficient than older ones. However, concerning energy efficiency, two very new schools were exceptional because ventilation was on day and night in order to dry the building materials in the constructions. The novelty of this study is that it makes use of hourly smart metering consumption data on electricity and district heating, using modern computational methods to analyse complex multivariate data in order to increase knowledge of the buildings’ consumption profiles and energy efficiency. [ABSTRACT FROM AUTHOR]

This paper is devoted to the development of the non-Emissions Trading Scheme for the district heating systems. To select indicators characterizing overall operation of the non-Emissions Trading Scheme district heating system, the article analyses various aspects (type of fuel, temperature regime, level of building energy efficiency) that impact the development of a district heating system model, which strives to reach 4th generation district heating system and to reduces greenhouse emission gases. To determine efficiency rating of the district heating system, a multi-criteria model is developed using selected indicators. The most suitable temperature regime of district heating systems is analyzed for a new buildings and exiting dwelling area depending on end-user heat energy consumption. Research methodology includes algorithm with several modules. The multi-criteria analysis evaluates different district heating systems and identifies optimal solution of 12 scenarios taking into account different criteria. The TOPSIS tool is integrated to find the optimal solutions. Results of the multi-criteria analysis confirm that scenario that is the closest to the ideal solution from the point of view of district heating company, sharply differs from the best scenario of the developers. From the point of view of district heating company the optimal solution is the lowest temperature regime 60/30 and wood chips as energy source. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an innovative low-energy district heating (DH) concept based on low-temperature operation. The decreased heating demand from low-energy buildings affects the cost-effectiveness of traditionally-designed DH systems, so we carried out a case study of the annual energy performance of a low-energy network for low-energy houses in Denmark. We took into account the effect of human behaviour on energy demand, the effect of the number of buildings connected to the network, a socio-economic comparison with ground source heat pumps, and opportunities for the optimization of the network design, and operational temperature and pressure. In the north-European climate, we found that human behaviour can lead to 50% higher heating demand and 60% higher heating power than those anticipated in the reference values in the standard calculations for energy demand patterns in energy-efficient buildings. This considerable impact of human behaviour should clearly be included in energy simulations. We also showed that low-energy DH systems are robust systems that ensure security of supply for each customer in a cost-effective and environmentally friendly way in areas with linear heat density down to 0.20 MWh/(m year), and that the levelized cost of energy in low-energy DH supply is competitive with a scenario based on ground source heat pumps. The investment costs represent up to three quarters of the overall expenditure, over a time horizon of 30 years; so, the implementation of an energy system that fully relies on renewable energy needs substantial capital investment, but in the long term this is sustainable from the environmental and socio-economic points of view. Having demonstrated the value of the low-energy DH concept, we evaluated various possible designs with the aim of finding the optimal solution with regard to economic and energy efficiency issues. Here we showed the advantage of low supply and return temperatures, their effect on energy efficiency and that a DH design that relies on low-temperature operation is superior to a design based on low-flow operation. The total primary energy use in the best design was 14.3% lower than the primary energy use for standard, recently designed networks, and distribution heat losses were halved. Moreover, the exploitation of the entire available pressure by means of careful network design decreased the average pipe size required, which slightly lowers the investment costs for purchasing and laying the pipelines in the ground. This low-temperature DH concept fits the vision of the future energy-sustainable society. [Copyright &y& Elsevier]

Abstract: An efficient method for numerical simulation and analyses of the steady state hydraulics of complex pipeline networks is presented. It is based on the loop model of the network and the method of square roots for solving the system of linear equations. The procedure is presented in the comprehensive mathematical form that could be straightforwardly programmed into a computer code. An application of the method to energy efficiency analyses of a real complex district heating system is demonstrated. The obtained results show a potential for electricity savings in pumps operation. It is shown that the method is considerably more effective than the standard Hardy Cross method still widely used in engineering practice. Because of the ease of implementation and high efficiency, the method presented in this paper is recommended for hydraulic steady state calculations of complex networks. [Copyright &y& Elsevier]

District heating systems are an important part of the future smart energy systems and are seen in the European Union as a vehicle for reaching energy efficiency targets. Integrating different energy systems requires high prediction accuracy for all energy sub-systems. Within this paper a data analysis was made with the goal of identifying a high accuracy prediction model and ranking the most influential parameters on heat consumption of final consumers in district heating systems. The data set consisted of the actual billing data comprising of 260 buildings and it was additionally supplemented by the behavioural data obtained from interviews and questionnaires conducted on the demonstration building in Zagreb, Croatia. The authors choose regression trees, random forest and regression support vector machines as algorithms for testing prediction accuracy and evaluating the variable importance ranking on the data set. The best performing algorithm was random forest, resulting with high prediction accuracy and the root mean squared error of prediction of specific annual heat consumption below 1 kWh/m2. Furthermore, all analysed machine learning algorithms ranked importance variables for both technical and behavioural parameters, giving the indication what parameters should be influenced in order to reach specific targets, such as energy savings. [ABSTRACT FROM AUTHOR]

This paper explores the potential for using large-scale heat pumps (HPs) to extract energy from Copenhagen's drinking water network and deliver it to its district heating system. The system involves certain losses in terms of additional heat and power consumption for end-use water heating. The net potential for energy extraction was analysed by means of an EPANET model to simulate system-wide temperatures in a piped distribution network. The model was validated against measured data from the network. Heat transfer in service lines was computed analytically and included in the net potential for energy extraction, which was determined to be 21 MW in Copenhagen. Around 38% of the HP source demand was harnessed from the ground. With HP COPs between 2.8 and 3.2, the System COP was only 1.7, thus suggesting that the choice of drinking water as a low-temperature heat source should depend on the available alternatives. Drinking water HPs have the side-benefit of preventing high drinking water temperatures; if operated in the summer they increased the share of supplied water complying with a recommended upper temperature limit of 12 °C from 42% to 81%. • A heat transfer model for water supply networks was validated against measured data. • The undisturbed soil temperature is key to predicting heat transfer correctly. • The potential for drinking water heat pumps in Copenhagen was around 36 MW. • Around 38% of the heat pump source demand is reaped from the ground. • Due to increased end-use energy demand, the system COP was reduced by around 42%. [ABSTRACT FROM AUTHOR]