Abstract
Slowing down the energy efficiency (EE) improvement rate over the past half a decade and relatively low EE-related investments highlight a need for a more targeted approach to encourage EE improvement investment in the Swiss manufacturing sector. The current work is based on an analysis of already implemented energy efficiency measures (EEMs) via target agreements. Four segments of the Swiss manufacturing sector are identified using the K-means clustering based on the energy consumption profiles of the establishments. The EEMs are categorized through text analysis with the help of a machine learning algorithm. The EEMs are ranked using two indicators that can inform different stakeholders: i) EEMs with Internal rates of return well above the reported risk-adjusted hurdle rates are identified for each segment to help industrial decision-makers take ad-hoc, profit-based investment decisions on EE improvement. ii) Levelized costs of saved energy are presented as value ranges for the EEMs that have the largest impact on annual total final energy (TFE) reduction for each segment to help policymakers design a more targeted approach for the promotion of high-impact EEMs. EE system improvements such as insulation of steam systems and process optimization, as well as EE equipment improvements like process equipment insulation, installation of variable frequency drives (VFDs), smart controls, and IE3 motors, were found to be the most profitable options across all clusters. For the electricity cluster, CHP was found to be the most impactful way of EE improvement (high energy savings) but not cost-effective. For fuel-consuming clusters, waste heat recovery measures were found to be the most cost-effective and among the most impactful options for EE improvement, along with fuel substitution measures with varying degrees of cost-effectiveness.
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