Feature Selection Based Root Cause Analysis for Energy Monitoring and Targeting

Abstract

Energy Monitoring (EM) systems are based on monitoring the difference between targeted and measured energy consumption. Data-driven dynamic targeting models can be used to estimate values of key energy indicators (KEI). In some cases it is difficult to determine which process variables influence the KEIs. We developed an automated root cause analysis (RCA) technique to find the most important driving factors of energy efficiency. The proposed concept is based on the application of feature selection algorithms. We applied Orthogonal Least Squares (OLS) and Random Forest Regression (RFR) to find the proper set of input variables of the targeting models. The concept of the resulted energy monitoring system is applied at the Duna Refinery of MOL Hungarian Oil and Gas Company.