Machine Learning Based Electric Energy Consumption Prediction of a Large-Scaled Production Plant with Small-Scaled Data

Abstract

This report covers the statistical approach to predict consumed energy for a tire production plant. The reasons behind this study are also to optimize the energy consumption budget and to follow the production area wised KPIs which is also vital for ISO 50001 Energy management system standard. In order to make it happen, writers clarify the main problem, then start to apply the steps of the cross industry standard process for data mining (CRISP-DM) [1] methodology. The most important point of this study was that although the historical data is small scaled, the parameters have a higher dimension according to input examples. Hence, the data to be used as input could be explained with simple variables to be used in the budget period. The study introduces data preparation steps based on the production area, grid search for best regression algorithm, comparison of models, and seven-month validation results.