Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/106929
Title: Determining the typical meteorological year for the Maltese Islands
Authors: Spiteri, Daniel (2022)
Keywords: Meteorology -- Malta
Meteorology -- Observations
Buildings -- Energy conservation -- Malta
Buildings -- Energy conservation -- Computer simulation
Issue Date: 2022
Citation: Spiteri, D. (2022). Determining the typical meteorological year for the Maltese Islands (Master's dissertation).
Abstract: Typical meteorological year (TMY) data files are becoming increasingly in demand especially to serve as input to building energy modelling software, which requires representative hourly dataset of one year. The dataset should contain all relevant meteorological parameters, such as dry bulb temperature, dew point temperature, wind speed and wind direction, global and diffuse solar radiation, relative humidity, and atmospheric pressure. Several methods by which such an hourly TMY can be derived from a long-term dataset exist. These methods apply various statistical tools and selection criteria to select the most representative months from the available set of weather data files. The selected months are then concatenated to form what is known as the TMY. The main aim of this dissertation was to build the TMY for the Maltese Islands for its implementation in building energy performance software. Different methodologies were applied to a 13-year dataset of meteorological measurements from an onshore site in the Maltese Islands and five TMYs were generated. The TMYs were statistically compared to the long-term weather behaviour and the most representative TMY was determined. From the analyses carried out it was found that overall, the most representative TMY was the one yielded by a variant of the Festa-Ratto method developed by Festa and Ratto. The other methods produced TMYs with varying degrees of representation.
Description: M.Sc.(Melit.) Sust.Energy
URI: https://www.um.edu.mt/library/oar/handle/123456789/106929
Appears in Collections:Dissertations - InsSE - 2022

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