An effective modeling technique is proposed for determining baseline energy consumption in the industry.
A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation
of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption
and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the
current consumption and production in the event that no energy-saving measures had been implemented.
Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial
neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable
accuracy levels of prediction are detected, confirming good capability of the models for predicting plant
behavior and their suitability for baseline energy consumption determining purposes. High level of robustness
is observed for ANN against uncertainty affecting measured values of variables used as input in the
models. The study demonstrates ANN great potential for assessing baseline consumption in energyintensive
industry. Application of ANN technique would also help to overcome the limited availability of
on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes
