Use of different methodologies for thermal load and energy estimations in buildings including meteorological and sociological input parameters

This review paper provides first an overview of the background for meteorological and sociological influences on thermal load and energy estimations. The different yearly representations of weather parameters (test reference year (TRY), design reference year (DRY), typical meteorological year (TMY) and weather year for energy calculations (WYEC)) are discussed, and compared to simplified representations of weather characteristics. Sociological influences on energy demand are discussed in relation to attitude and culture. Many methods exist for estimating thermal load and energy consumption in buildings, and they are primarily based on three different methodologies; regression analyses, energy simulation programs and intelligent computer systems. Regression analyses are mainly based on large amounts of metered load data, long-term weather characteristics and some information about the buildings. Energy simulation programs require detailed information about the buildings and sociological parameters, as well as thorough representation of weather data. Intelligent computer systems require metered load data, weather parameters and building information. The advantages and disadvantages of the alternative methodologies are discussed, as well as when and where to use them. Finally, the more specific usages of the methodologies are exemplified through three specific methods: conditional demand analysis (CDA), engineering method (EM) and neural networks (NN).