3 years ago

RES-H statistics - European Renewable Energy Council

RES-H statistics - European Renewable Energy Council

Chapter Seven –

Chapter Seven – Conclusions and RecommendationsCHAPTER SEVENCONCLUSIONS AND RECOMMENDATIONS1 Main Conclusion on RES-H StatisticsMost EU countries collect/publish data on renewable energy sources in heating/cooling.Eurostat and the IEA have helped the development of RES-H statistics by proposingcommon definitions. The available statistics are in most cases sufficient to use them asreference for target setting and monitoring. It lies in the nature of a project like K4 RESHEAT to point out remaining weaknesses of RES-H statistics and to propose how they canbe improved in the future. Both of this will be done in the next paragraphs.A GeneralThere are weaknesses which are shared by all technology sectors, namely: Lack of attentionIn general there is a lack of attention to RES-H in the statistics. Most countries andinternational statistics don’t report RES-H data separately. The focus of statistics ismostly on renewable electricity Different definitionsMost obviously, statistics depend very much on the definitions used. Vast variences inthe statistical data are often due to different definitions used. Different concepts to collect dataIn several countries, statistics are based on surveys of companies active in that country(manufacturers or importers). In other countries statistics are based on the estimationsof one or several market experts. The accuracy of those statistics depends on the goodknowledge and honest estimation of the expert. Different concepts of cumulative installed capacity vs. capacity in operationA typical source for deviations between different statistics for the same country resultfrom a different concept of cumulative installed capacity vs. capacity in operation. Different methodology for the conversion of numbers of installations and installedcapacity to RES-HRES-H in smaller applications is not measured but calculated from statistics of theinstalled capacity. Different methodologies are used in different statistics and countries.B Solar ThermalThe biggest differences between solar thermal statistics are due to the following reasons: Different definitions Different collection of data Different aggregation and conversion of raw data Conversion from m 2 to kWth Conversion from m 2 to kWh26

Chapter Seven – Conclusions and RecommendationsC BiomassThe main deficits for biomass statistics are: The data are generally not complete for all sectors and sub-sectors. The global figuresfor all biomass for heat are vague, probably reached through broad assumptions, andcannot be considered as reliable. The data do not refer to the same type of biomass (e.g. waste included or not) MSW incineration has a large potential and is an increasing source. Main difficulty is thedetermination of the renewable fraction of the energy content of MSW. At least MSshould apply the same methodology. The data are not classified the same way. Sometimes the primary criteria is the type ofbiofuel - forest wood chips, industrial wood waste, etc. Sometimes it is the type of use -households, collective, industries, etc. The terms are not defined clearly (e.g. what does gross heat production mean in IEAstatistics?) A major source of renewable heat is combustion of wood in private households. But thestatistics for this source are poor and a lot of wood is combusted with a low efficiency.D GeothermalThe main deficits for geothermal statistics are: Different definitionssome statistics distinguish the geothermal heating and cooling productionwith heat pumps from the other direct heat use some statistics geothermal energy is limited to the deep applications onlyexcluding (all) heat pumps. Different collection of data, e.g. registers, surveys and estimations by experts Different aggregation and conversion of raw data27

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