Construction of a typical meteorological year for air temperature

Abstract

The availability of accurate and reliable data models is of utmost importance especially when it comes to the development of energy efficient buildings through specialised software, and the process adopted is commonly known as Building Information Modelling (BIM). These modelling software require reliable weather data representative of the site or the region, which are represented by a Typical Meteorological Year (TMY) covering a minimum of 11 years, being a full one cycle of the sun’s magnetic activity cycle. In Malta, a TMY does not exist as yet, and software models use either average data or extrapolate from nearby weather stations that have produced their own hourly-data TMY. Unfortunately, such meteorological data does not give an accurate representation of Malta due to the microclimate effects. An ideal way forward is to develop a TMY for Maltese weather, thus providing effective solutions to the current needs for such a file among designers and building planners, especially because all new and renovated buildings need to attain nearly zero-energy status, as required by the EU Energy Performance of Buildings Directive. The development of such data started in 2019 when an M.Sc. student produced a TMY for solar radiation. In this project a TMY for air temperature will be developed. Solar radiation and air temperature are regarded as being the two most important parameters when it comes to creating a TMY. Literature review has showed that there are three TMY methods. These are the Sandia National Laboratory method, the Danish method and the Festa-Ratto method. Following a literary analysis, it was decided that the Danish method was the most adaptable statistical approach, since it produced a TMY whose global values were very close to long term averages and also link very well and build on the TMY for solar radiation that was developed in the previous MSc dissertation. The hourly-data TMY for air temperature was then plotted together with the long-term average (LTA) temperatures. This process showed how the TMY fully displays any spikes or dips in temperature along the representative months, while the LTA smoothens them out, thus making TMY data more representative of the site than LTA data.