A comparative analysis of regression algorithms and a real world application of multivariable energy signatures

An ecosystem of energy models of buildings is needed to boost the retrofitting process to improve energy efficiency and meet sustainability goals. Such models should enhance the understanding of the energy behavior of a building, the impact of the external variables, and the causes of inefficiencies. Energy Signatures can fill this role, with particular regard to the consumption due to air conditioning. Univariate models, neglecting the impact of solar radiation, have been widely adopted for Energy Signature analysis. This paper presents Multivariable Energy Signatures considering outdoor temperature and solar radiation. The application on a real-world dataset of multivariable non-parametric approaches stands out from previous works in the ES sector. This led to a mean improvement of 0.768 to 0.804 of the coefficients of determination calculated over 103 real-world case studies. Moreover, Neural Networks outperformed several literature algorithms regarding accuracy, robustness, and scalability. The paper also discusses issues regarding the time resolution of input data and introduces appropriate visualization tools to employ Multivariable Energy Signatures as diagnostic tools.