N. Tolentino, D. Borges, M. Albertini, L. P. Pires, F. A. Moura, M.V. Rezende, G. Lima, J. O. Rezende
{"title":"Consumption Prediction and Evaluation of Harmonic Distortion in a Hospital using Neural Networks","authors":"N. Tolentino, D. Borges, M. Albertini, L. P. Pires, F. A. Moura, M.V. Rezende, G. Lima, J. O. Rezende","doi":"10.24084/repqj21.297","DOIUrl":null,"url":null,"abstract":"This article aims to present a proposal for a methodology to analyze the energy efficiency of a hospital according to the current consumption obtained through field measurements. In addition, it aims to present the prediction of the increase in consumption over the years and correlate it with the possible increase in the harmonic distortion of stress. This analysis is essential for the studies of the connection impacts, allowing the estimation and evaluation of the energy quality through the harmonic voltage distortions over the years. The study is validated by comparing the consumption prediction curve obtained by Neural Network training with the data extracted from measurements and analysis of energy bills. The results show that the model generates the best prediction performance.","PeriodicalId":21076,"journal":{"name":"Renewable Energy and Power Quality Journal","volume":"500 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Renewable Energy and Power Quality Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24084/repqj21.297","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Energy","Score":null,"Total":0}
引用次数: 0
Abstract
This article aims to present a proposal for a methodology to analyze the energy efficiency of a hospital according to the current consumption obtained through field measurements. In addition, it aims to present the prediction of the increase in consumption over the years and correlate it with the possible increase in the harmonic distortion of stress. This analysis is essential for the studies of the connection impacts, allowing the estimation and evaluation of the energy quality through the harmonic voltage distortions over the years. The study is validated by comparing the consumption prediction curve obtained by Neural Network training with the data extracted from measurements and analysis of energy bills. The results show that the model generates the best prediction performance.