PREDICTION OF ELECTRICITY BILL USING SML TECHNIQUE.
One of the most crucial procedures is predicting the electricity bill. This is a highly tricky situation with no guarantees. To get around this, we can use machine learning approaches. As a result, anticipating power bills has become a hot study issue. The goal is to predict results with the highest...
| Publicado en: | Proteus Vol. 13; no. 10; pp. 55 - 68 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Proteus
Oct2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=160270475&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 160270475 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 08896348 THQ jtl: Proteus issn: 08896348 maglogo: N pubinfo: dt: Oct2022 vid: 13 iid: 10 pid: 73634 pub: Proteus artinfo: ui: 160270475 ppf: 55 ppct: 13 formats: fmt: @attributes: type: P size: 1.8MB tig: atl: PREDICTION OF ELECTRICITY BILL USING SML TECHNIQUE. aug: au: S., Aishwarya Franklin J., Biruntha S., Nivetha S., Kowsalya affil: Assistant Professor, CSE, Agni College of Technology, Chennai, Tamilnadu, India Student, B.E (CSE), Agni College of Technology, Chennai, Tamilnadu, India su: Supervised learning Machine learning Data modeling Electric power consumption Data scrubbing Electricity pricing sug: subj: Supervised learning Machine learning Data modeling Electric power consumption Data scrubbing Electricity pricing ab: One of the most crucial procedures is predicting the electricity bill. This is a highly tricky situation with no guarantees. To get around this, we can use machine learning approaches. As a result, anticipating power bills has become a hot study issue. The goal is to predict results with the highest possible accuracy using machine learning techniques. A example dataset will be made available for use. We must analyze the data in this dataset. Supervised Machine Learning Methods are used to analyse the data (SMIT). Data cleaning, data preparation, and data visualization will all be collected using this data analysis. To provide a machine learning-based strategy for accurately estimating the value of the Electricity Price Index by comparing supervise classification machine learning algorithms and predicting outcomes in the form of electricity price increase or stable state. In addition, the performance of several machine learning methods will be compared and discussed. Dataset with evaluation classification report, confusion matrix, and data prioritisation, and the results show that the proposed method is effective. machine learning algorithm technique can be compared to the best accuracy MAE, MSE,R2, and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared to the best accuracy MAE,MSE,R2. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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