A new hybrid model to foretell thermal power efficiency from energy performance certificates at residential dwellings applying a Gaussian process regression

An energy performance certificate (EPC) provides information on the energy performance of an energy system. The objective of this research aimed at obtaining a predictive model for early detection of thermal power efficiency (TPE) for energy conversion and preservation in buildings. This article expounds a sound and solid nonparametric Bayesian technique known as Gaussian process regression (GPR) approach, based on a set of data collected from different dwellings in an oceanic climate. Firstly, this model introduces the relevance of each predictive variable on energy performance in residential buildings. The second result refers to the statement that we can predict successfully the TPE by using this model. A coefficient of determination equal to 0.9687 was thus established in order to predict the TPE from the observed data, using the GPR approach in combination with the differential evolution (DE) optimiser. The concordance between experimental observed data and the predicted data from the best-proposed novel hybrid DE/GPR-relied model demonstrated here the adequate efficiency of this innovative approach.

Automated summary

The research used the Gaussian process regression method to establish a predictive model for early detection of thermal power efficiency in buildings, based on data collected from different dwellings. The model successfully predicted the thermal power efficiency and demonstrated the effectiveness of the innovative approach.