A Comparative Study of Energy Big Data Analysis for Product Management in a Smart Factory

Abstract

Energy has been obtained as one of the key inputs for a country's economic growth and social development. Analysis and modeling of industrial energy are currently a time-insertion process because more and more energy is consumed for economic growth in a smart factory. This study aims to present and analyse the predictive models of the data-driven system to be used by appliances and find out the most significant product item. With repeated cross-validation, three statistical models were trained and tested in a test set: 1) General Linear Regression Model (GLM), 2) Support Vector Machine (SVM), and 3) boosting Tree (BT). The performance of prediction models measured by R2 error, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Variation (CV). The best model from the study is the Support Vector Machine (SVM) that has been able to provide R2 of 0.86 for the training data set and 0.85 for the testing data set with a low coefficient of variation, and the most significant product of this smart factory is Skelp.

Probabilistic data structures in smart city: Survey, applications, challenges, and research directions

“…So in order to gather valuable information, they have collected a big database. This data is being used to raise the standard of living [231].…”

IoT Real-Time Production Monitoring and Automated Process Transformation in Smart Manufacturing

“…The introduction of automated processes can mitigate human errors and variations, ensuring more precise manufacturing and consequently enhancing overall product quality and consistency. Thirdly, it holds the potential to reduce energy consumption (Salman et al, 2022) and carbon emissions (Kumar et al, 2022). Automation technologies enable the optimization of production processes, precise control of energy usage, and the reduction of unnecessary waste, thereby rendering the manufacturing process more environmentally friendly and aligning with the overall eco-friendly philosophy of electric vehicles.…”

“…Currently, deep learning technology has found numerous applications in the automation of factory production processes (Tercan & Meisen, 2022), and these innovative applications have profound impacts on enhancing production efficiency (Salman et al, 2022), improving product quality, reducing costs, and driving digital transformation. Due to its capability to learn and comprehend vast amounts of production data, deep learning enables intelligent decision-making, resource optimization, and waste reduction through real-time monitoring and analysis of data on the production line, thereby achieving a higher level of production efficiency.…”

An Image-Text Matching Method for Multi-Modal Robots

“…With the rapid advancement of deep learning, cross-modal research has become a popular field (Ma et al, 2022). In the feature learning of multimodal data, deep learning has the capability to nonlinearly map low-level features of multimodal data into high-level abstract representations (Salman et al, 2022). Image-text matching tasks, as a fundamental task in cross-modal research, have garnered extensive attention from scholars.…”