能源消耗
物理模型
能量(信号处理)
消费(社会学)
物理系统
计算机科学
工业工程
机器学习
工程类
制造工程
人工智能
数学
统计
物理
电气工程
社会科学
岩土工程
量子力学
社会学
作者
Tanveer Ahmad,Jun‐Ki Choi,Taehoon Hong
标识
DOI:10.1016/j.rser.2022.113045
摘要
The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. New modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities. • Industrial energy data from 45 manufacturing facilities are obtained from on-site energy audits. • A supervised ML model is developed to facilitate predicting industrial energy consumption. • Basis function model presented a grounding in the physical nature of industrial energy systems. • Enables energy engineers or plant personnel to construct a more descriptive energy model.
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