Harnessing machine learning algorithms to unveil energy efficiency investment archetypes

Abstract

Increasing transparency about the performance of different projects is crucial to reducing the heterogeneity in the energy efficiency services market, thereby upscaling investments. In this context, machine learning algorithms could assist in identifying and analyzing energy efficiency project archetypes, although this field has so far been explored with a limited view in the literature. This paper aims to address this gap by identifying energy efficiency investment families and the determinant factors of the classification scheme, using machine learning. In this effort, it hinges on a wide range of indicators from implemented projects around Europe and the USA, including investment profitability, initial investment, risk of failure, intervention type, life measure, region of implementation and building type. The analysis employs two clustering approaches, namely Partitioning Around Medoids (PAM) and K-means, determining the number of clusters based on the Silhouette index and total within–cluster sum of squares. The results indicate that energy efficiency investments can be classified into three categories: (i) “junk investments”, characterized by low–profitability (IRR∼10%), moderate risk, and extended horizons; (ii) “safe profitability”, distinguished by high profitability (IRR∼30%) and minimal risk; and (iii) “high stakes”, described by exceptionally high profitability (IRR∼40%), coupled with a substantial risk. Next to profitability and risk of failure, also energy efficiency intervention and building type (sector) emerge among the most influential factors in the classification scheme. Feature importance shows a significant sensitivity to the chosen classification model.