Uncertainty reduction in measuring and verification of energy savings by statistical learning in manufacturing environments

Abstract

Industry 4.0 methodological advancements based on continuous analytics and on the sensorization of manufacturing lines make it possible to design and develop integrated systems for measurement and verification of the impact of implemented energy conservation measures (ECM) in industrial plants. The pilot study presented here has focused on developing a model of the energy consumption of the injection machines in a manufacturing facility. The energy savings are calculated by comparing energy consumption of the post- and pre-ECM periods, adjusted so that the comparison is made in the pre-ECM operating conditions. The contribution of the model is to reduce the uncertainty, i.e. to provide narrower limits for the possible values of the estimate of consumed energy, by taking advantage of the fact that the period in which the energy savings are to be measured is usually quite larger than the time intervals in which the energy performance measurements are taken. This better approximation of the range of possible values for the estimate is achieved by combining traditional statistics and machine learning methods.