Assembling Typical Meteorological Year Data Sets for Building Energy Performance Using Reanalysis and Satellite-Based Data

Abstract

We present a method to generate Typical Meteorological Year (TMY) data sets for use in calculations of the energy performance of buildings, based on satellite derived solar radiation data and other meteorological parameters obtained from reanalysis products. The great advantage of this method is the availability of data over large geographical regions, giving global coverage for the reanalysis and continental-scale coverage for the solar radiation data, making it possible to generate TMY data for nearly any location, independent of the availability of meteorological measurement stations in the area. The TMY data generated with this method have been validated against 487 meteorological stations in Europe, by calculating heating and cooling degree days, and by running building energy performance simulations using EnergyPlus. Results show that the generated data sets using a long time series perform better than the TMY data generated from station measurements for building heating calculations and nearly as well for cooling calculations, with relative standard deviations remaining below 6% for heating calculations. TMY data constructed using the proposed method yield somewhat larger deviations compared to TMY data constructed from station data. We outline a number of possibilities for further improvement using data sets that will become available in the near future.

How will climate alter efficiency objectives? Simulated impact of using recent versus historic european weather data for the cost-optimal design of nearly zero energy buildings (NZEBs)

“…The IWEC2 files, on the other hand, are a large improvement in predicting weather over the last fifteen years. The two TYP files covering ten year periods, one from White Box Technologies [15] and the other from Huld et al [7] are both reasonable. Not surprisingly, a project TMY file composed from 2003-2017, provided the lowest deviations relative to all parameters of interest: heating, cooling, total and solar electric output.…”

“…Recently, more up to date TMYs have become available, including data since 2011 with re-analysis based on satellite date [7]. Still, such data has the problem within the climate change already being experienced of looking backward for weather while projecting building energy use into the future.…”

PV‐betriebene Umkehrosmoseanlage zur Meerwasserentsalzung – Modellierung und Analyse verschiedener Energieversorgungsvarianten

“…Die Neigung und Azimutausrichtung dieses PV‐Moduls werden unter Verwendung eines iterativen Newton‐Raphson‐Algorithmus optimiert, um den maximalen Jahresenergieertrag am betrachteten Standort zu erzielen. Zusätzlich wird der Einfluss von Umgebungstemperatur ( T a ) und Windgeschwindigkeit ( W ) berücksichtigt , . Die folgenden Gleichungen beschreiben, wie das PV‐Modul in PVGIS mathematisch simuliert wird.…”

“…Die PV‐Spitzenleistung ( P STC ) wird unter Standardtestbedingungen (Einstrahlung 1000 W m −2 , Luftmasse ( air mass ) 1,5 und Lufttemperatur 25 °C) gemessen. Die Koeffizienten k 1 bis k 6 werden für jede PV‐Technologie durch Anpassung an Messdaten ermittelt , , . Die Modultemperatur wird durch Gl.…”

Estimating Hourly Beam and Diffuse Solar Radiation in an Alpine Valley: A Critical Assessment of Decomposition Models

“…The accurate knowledge of solar radiation locally available at the Earth's surface is essential for the proper sizing, design, and dynamic simulation of solar energy systems [1]. In particular, when the solar radiation incident on tilted surfaces is investigated, e.g., for photovoltaic applications, the knowledge of both beam and diffuse components is required [2,3].…”