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Conference Paper Machine Learning-based Analysis of Correlation between Energy Consumption Data of the Company and its Sales
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Authors
Lee Jungi, Kim Nack Woo, Lee Hyun Yong, Park Sangjun, Lee Byung-Tak
Issue Date
202010
Source
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1258-1260
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289575
Project Code
20PK1100, Development and Trial of New Business Model and Service using Electric-Power Big-data, Lee Byung-Tak
Abstract
This paper has researched about the correlation between company data, and its annual sales data. Using the identified correlation, we propose a new method of predicting the company's annual sales data with energy consumption data. For this work, gradient boosting was applied to the predictive learning model for effective and better performance of prediction. To implement this method, district address-based energy consumption data is merged into company survey data with pre-processing. Then to predict the sales of each company, the gradient boosting based machine learning model has applied. Our approach to utilizing the energy consumption data can contribute to a new method of predicting the status of companies.
KSP Keywords
Company data, Company survey, Learning model, Learning-based, Performance of prediction, Pre-processing, Sales data, consumption data, energy consumption, gradient boosting, machine Learning