Linear Regression Based Demand Forecast Model in Electric Vehicles -LRDF
DOI:
https://doi.org/10.5281/zenodo.7900508Keywords:
Forecasting With Machine Learning, Electric Vehicles, Cloud, Un-Supervised AlgorithmAbstract
Machine learning is an Artificial Intelligence (AI) software application that uses algorithms to analyze data, make inferences from that data, and then use what they've learned to create well-informed conclusions. Machine learning cannot process characters or strings; in order to process them, we must transform them to numerics. Otherwise, there will be exceptions or mistakes of some kind. After pre-processing, which removes the null or empty data from the original dataset, machine learning algorithms can predict or forecast future events using the provided dataset. Machine learning is now widely used in the software industry, and as a result, many software applications now offer predictions like weather forecast, impact of covid spread, future sales, etc. So, the problem of the demand of EVs in future is investigated using novel strategies for entirely eliminating problems with forecasting models, based on machine learning approach. The proposed LRDF model for Electric Vehicle’s is based on machine learning that accepts a dataset as an input in the form of an CSV file containing electric vehicles data that are useful for predicting demand for electric vehicles. The accuracy of three distinct classification algorithms, including the SVM algorithm, the Random Forest system, and the Linear Regression algorithm, will be examined. The results demonstrate that the linear regression approach performs significantly better than the other two algorithms. The LRDF model for Electric Vehicle’s when mounted on the public cloud server- which belongs to Infrastructure as a Service (IaaS), would increases the speed of dataset processing
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Copyright (c) 2023 Shantala Devi Patil, Sreedevi Satheesh, Seema S M, R Mothish Chowdary
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