A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing. (1st June 2021)

• Record Type: Journal Article
• Authors: Tan, Daniel; Suvarna, Manu; Shee Tan, Yee; Li, Jie; Wang, Xiaonan
• Is Part Of: Applied energy. Volume 291(2021)
• Publication Date: 2021-06-01
• Subjects: Smart energy management -- Smart manufacturing -- Energy disaggregation -- Machine activity state -- Machine learning -- Industrial big data; AI Artificial Intelligence -- BLSTM Bi-directional Long Short-Term Memory -- CPPS Cyber Physical Production Systems -- DES Discrete Event Simulation -- ED Energy Disaggregation -- EnBLSTM Ensemble Bi-directional Long Short-Term Memory -- EnLSTM Ensemble Long Short-Term Memory -- FM Index Fowlkes Mallows Index -- GBDT Gradient Boost Decision Trees -- GMM Gaussian Mixture Models -- GOSS Gradient-Based One-Side Sampling -- HMM Hidden Markov Models -- IBD Industrial Big Data -- IIOT Industrial Internet of Things -- IOT Internet of Things -- LGBM Light Gradient Boosting Machines -- LSTM Long Short-Term Memory -- LT Laser Trimmer -- LW Laser Welder -- MAE Mean Absolute Error -- MF Model Factory -- ML Machine Learning -- NILM Non-Intrusive Load Monitoring -- REDD Reference Energy Disaggregation Data Set -- RMSE Root Mean Square Error -- RNN Recurrent Neural Networks -- XGBOOST Extreme Gradient Boost; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.116808
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: A three-step framework MIGRATE for smart energy management in manufacturing is proposed. Industrial big data and machine learning foster the development of MIGRATE . Supervised learning predicts machine-specific load profiles via energy disaggregation. Unsupervised learning estimates the machine's activity state from disaggregated load. Framework developed is purely data-driven and cross-deployable. Abstract: The dynamic nature of chemical processes and manufacturing environments, along with numerous machines, their unique activity states, and mutual interactions, render challenges to energy monitoring at a machine level. In this study, we introduce MIGRATE (Machine learnInG foR smArT Energy), a novel three-step framework to predict the machine-specific load profiles via energy disaggregation, which are in turn used to predict the machine's activity state and the respective production capacities. Various supervised tree-based and recurrent neural network algorithms were evaluated on their capacities to predict load profiles and production capacities of four machines investigated in this study. Light gradient boosting machines and ensemble bi-directional long-term short memory were identified as the respective best performing algorithms with a mean absolute error and root mean squared error of 0.035 and 0.105 (units in Watts) for the disaggregation studies and 1.639 and 11.401 (units in quantities of samples processed) for production estimation. Four unsupervised machine learning algorithms were evaluated to cluster the machine's activity state from their disaggregated load profiles, where the gaussian mixture model had a superior performance with the V score and Fowlkes Mallows index of 0.852 and 0.983, respectively. The MIGRATE framework is purely data-driven, cross-deployable and serves as promising catalyst to foster smart energy management practices and sustainable productions in the chemical and industrial manufacturing processes.