Abstract
The urban heat island (UHI) effect influences the heating and cooling (H&C) energy demand of buildings and should be taken into account in H&C energy demand simulations. To provide information about this effect, the PLANHEAT integrated tool—which is a GIS-based, open-source software tool for selecting, simulating and comparing alternative low-carbon and economically sustainable H&C scenarios—includes a dataset of 1 × 1 km hourly heating and cooling degrees (HD and CD, respectively). HD and CD are energy demand proxies that are defined as the deviation of the outdoor surface air temperature from a base temperature, above or below which a building is assumed to need heating or cooling, respectively. PLANHEAT’s HD and CD are calculated from a dataset of gridded surface air temperatures that have been derived using satellite thermal data from Meteosat-10 Spinning Enhanced Visible and Near-Infrared Imager (SEVIRI). This article describes the method for producing this dataset and presents the results for Antwerp (Belgium), which is one of the three validation cities of PLANHEAT. The results demonstrate the spatial and temporal information of PLANHEAT’s HD and CD dataset, while the accuracy assessment reveals that they agree well with reference values retrieved from in situ surface air temperatures. This dataset is an example of application-oriented research that provides location-specific results with practical utility.
Highlights
By 2050 the European Union (EU) aims to achieve a prosperous, modern, competitive, and low-carbon economy [1]
The SAFNWC physical retrieval (SPhR) algorithm starts by building an initial collocated, vertical profile from the interpolated Global Forecast System (GFS) numerical weather predictions (NWPs) that is iteratively modified until its radiative transfer properties fit the Spinning Enhanced Visible and Near-Infrared Imager (SEVIRI) observations
The accuracy of the IAASARS/NOA surface air temperature data product has been assessed using in situ air temperature data for various European cities in [21], and the results revealed that the root-mean-square error (RMSE) is close to 2.3 ◦C, and the correlation coefficient is around 95%
Summary
By 2050 the European Union (EU) aims to achieve a prosperous, modern, competitive, and low-carbon economy [1] To achieve this vision, EU’s heating and cooling (H&C) sector—which is the single largest energy consumer in Europe [2]—must sharply reduce its energy consumption, cut its use of fossil fuel, and renovate the current H&C equipment stock. Because two-thirds of the EU’s buildings were built when energy efficiency requirements were limited or nonexistent, most of this energy was wasted [2] To address these issues, the European Commission (EC) proposed in February 2016 a strategy for integrating efficient and sustainable H&C into EU energy policies. Via the Covenant of Mayors for Climate & Energy (CoM) initiative [3]—which already includes more than 9000 EU signatories—a number of EU municipalities and other public bodies have already put into place integrated approaches to energy saving and energy supply in the form of sustainable energy (and climate) action plans (SEAPs and SECAPs, respectively)
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