Energy intensity is a commonly used key performance indicator (KPI) for the energy performance of production processes and often serves as an Energy Performance Indicator (EnPI). The energy performance of a process depends on a variety of factors like capacity utilization, ambient temperature and operational performance. Understanding the influence of these factors on the relevant KPI or EnPI helps to distinguish between influenceable and non-influenceable contributions and to identify the improvement potential. By describing the best historically observed performance as a function of the non-influenceable factors, valuable information on the efficiency of the current operation of a plant and the improvement potential is provided to plant managers and operators. In this contribution, a method is proposed to identify a surrogate performance model for the attainable energy performance considering the relevant factors. The modeling method is based solely on the evaluation of historical process data and employs a novel combination of known surrogate modeling techniques using clustering, model fitting and model simplification by backward elimination. The method is applied to real process data of a large industrial production plant and the use of the model for process performance monitoring and reporting in accordance with energy management system requirements is illustrated and discussed. •Influencing factors are identified for the evaluation of the energy performance.•The best observed operation is determined, which reveals energy savings potentials.•A verification of performance improvements is facilitated by comprehensive baselines.•The data-driven modeling approach is shown to be efficient in the industrial context.