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In Europe, more and more data on building energy use will be collected in the future as a result of the energy performance of buildings directive (EPBD), issued by the European Union. Moreover, both at European level and globally it became evident that the real energy performance of new buildings and the existing building stock needs to be documented better. Such documentation can, for example, be done with data-driven methods based on mathematical and statistical approaches. Even though the methods to extract energy performance characteristics of buildings are numerous, they are of varying reliability and often associated with a significant amount of human labour, making them hard to apply on a large scale. A classical approach to identify certain thermal performance parameters is the energy signature method. In this study, an automatised, nonlinear and smooth approach to the well-known energy signature is proposed, to quantify key thermal building performance parameters. The research specifically aims at describing the linear and nonlinear heat usage dependency on outdoor temperature, wind and solar irradiation. To make the model scalable, we realised it so that it only needs the daily average heat use of buildings, the outdoor temperature, the wind speed and the global solar irradiation. The results of applying the proposed method on heat consumption data from 16 different and randomly selected Danish occupied houses are analysed.

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The landscape of buildings is a diverse one and long-term energy system planning requires simulation tools that can capture such diversity. This work proposes a model for simulating the space-heating consumption of buildings using a linear mixed-effects model. This modelling framework captures the noise caused by the differences that are not being measured between individual buildings; e.g. the preferences of their occupants. The proposed model uses outdoor temperature and space-heating consumption measured at hourly resolution; thus, the model is able to predict the intra-day variations as well as longer effects. Given the stochastic nature of the simulation, the prediction interval of the simulation can be estimated, which defines a region where the consumption of any unobserved building will fall in. A whole year has been simulated and compared to out-of-sample measurements from the same period. The results show that the out-of-sample data is virtually always inside the estimated 90% prediction interval. This work uses data from Norwegian schools, although the model is general and can be built for other building categories. This amount of detail allows energy planners to draw a varied and realistic map of the future energy needs for a given location.