Machine Learning Based Prediction of Energy Consumption

Sk, Saruk Chand; Singh, Manish Kumar; Sanyal, Judhajit

doi:10.17148/ijireeice.2019.7506

Cited by 2 publications

(2 citation statements)

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“…Due to the importance of optimizing energy consumption, many researchers are exploring the use of different types of ML algorithms to forecast energy consumption. Some examples are Decision trees and Random Forest, Linear and non-linear regression and Artificial Neural Networks (ANN) [21]. For example, the authors in [22] studied various models to predict energy consumption for a smart small-scale steel industry, namely: general linear regression, decision tree-based classification and regression trees, Random Forest, Support Vector Machine with a radial basis kernel, K-nearest neighbors and CUBIST.…”

Section: Machine Learning For Energy Consumption Prediction and Manag...mentioning

confidence: 99%

“…Linear Regression was used as a baseline and Random Forest was chosen since it represents a general-purpose ensemble approach (multiple decision trees), which is generally more robust, less prone to Two ML regression models were trained with the intention of creating the desired energy profiles: Linear Regression and Random Forest. Linear Regression was used as a baseline and Random Forest was chosen since it represents a general-purpose ensemble approach (multiple decision trees), which is generally more robust, less prone to overfitting and can handle larger datasets more efficiently, often being used in the literature for predicting energy consumption [21]. tradeoff in this case is that it may require more computational power and resources; however, this was not a constraint for the use case at hand.…”

Section: Machine Learning-creating the Predictive Modelsmentioning

confidence: 99%

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Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding

Freitas

Araújo²,

Alemão³

et al. 2023

Processes

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The energy consumption of production processes is increasingly becoming a concern for the industry, driven by the high cost of electricity, the growing concern for the environment and the greenhouse emissions. It is necessary to develop and improve energy efficiency systems, to reduce the ecological footprint and production costs. Thus, in this work, a system is developed capable of extracting and evaluating useful data regarding production metrics and outputs. With the extracted data, machine learning-based models were created to predict the expected energy consumption of an automotive spot welding, proving a clear insight into how the input values can contribute to the energy consumption of each product or machine, but also correlate the real values to the ideal ones and use this information to determine if some process is not working as intended. The method is demonstrated in real-world scenarios with robotic cells that meet Volkswagen and Ford standards. The results are promising, as models can accurately predict the expected consumption from the cells and allow managers to infer problems or optimize schedule decisions based on the energy consumption. Additionally, by the nature of the conceived architecture, there is room to expand and build additional systems upon the currently existing software.

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Section: Machine Learning For Energy Consumption Prediction and Manag...mentioning

confidence: 99%

Section: Machine Learning-creating the Predictive Modelsmentioning

confidence: 99%

Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding

Freitas

Araújo²,

Alemão³

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

Machine Learning - Learning Techniques, CNN, Languages and APIs

Joshi

Joshi²,

Chaudhari

2020

IJSRCSEIT

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Nowadays, Artificial intelligence is an important part in everyone's life. It can be derived in two categories named as Machine learning and deep learning. Machine learning is the emerging field of the current era. With the help of the machine learning, we can develop the computers in such a way so that they can learn themselves. There are various types of leaning algorithms used for machine learning. With the help of these algorithms, machines can learn various things and they can behave almost like the human beings. Nowadays, the role of the machine is not limited in some defined fields only; it is playing an important role in almost every field such as education, entertainment, medical diagnosis etc. In this research paper, the basics about machine learning is discussed we have discussed about various learning techniques such as supervised learning, unsupervised learning and reinforcement learning in detail. A small portion is also used to cover some basics about the Convolutional Neural Networks (CNN). Some information about the various languages and APIs, designed and mostly used for Machine Learning and its applications are also provided in this paper.

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Machine Learning Based Prediction of Energy Consumption

Cited by 2 publications

References 4 publications

Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding

Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding

Machine Learning - Learning Techniques, CNN, Languages and APIs

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