Forecasting annual solar power output from geographic location

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dc.contributor.authorAl-Dabbagh, Nayyef Sami Nayyef
dc.contributor.authorAl-Musawi, Sura Saadi Jaafar
dc.contributor.authorHamodat, Zaid
dc.date.accessioned2022-08-05T13:59:14Z
dc.date.available2022-08-05T13:59:14Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractPower determining of environmentally friendly power plants is an exceptionally dynamic examination field, as dependable data about the future power age takes into consideration a protected activity of the power framework and assists with limiting the functional expenses of these energy sources. AI calculations have demonstrated to be exceptionally strong in anticipating errands, like financial time series or discourse acknowledgment. Using random forest regression method, daily mean solar output generation can yield promising result rather than conventional NWP model for forecasting. Using that in practice also the goal was to create a user-friendly application, with easy access, to provide accurate forecasting regarding saving and conservation. This paper's goal was accomplished in three stages: To begin with, make an AI model that predicts the yearly energy result of an expected sun based establishment. From that point onward, make a model that predicts establishment costs. Carrying out these models on an easy to use web application that shows clients the amount they can hope to save money on their yearly energy bill by changing to sun oriented. The random forest model out-performs its other rivals and also conventional models, thus providing a better suited model to run with for forecasting.en_US
dc.identifier.citationAl-Dabbagh, N. S. N., Al-Musawi, S. S. J., Hamodat, Z. (2022). Forecasting annual solar power output from geographic location. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), IEEE.en_US
dc.identifier.isbn9781665468350
dc.identifier.scopus2-s2.0-85133973372
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2781
dc.indekslendigikaynakScopus
dc.institutionauthorAl-Dabbagh, Nayyef Sami Nayyef
dc.institutionauthorAl-Musawi, Sura Saadi Jaafar
dc.institutionauthorHamodat, Zaid
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofHORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
dc.relation.isversionof10.1109/HORA55278.2022.9799821en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAI Modelen_US
dc.subjectNWP Modelen_US
dc.subjectPower Forecastingen_US
dc.titleForecasting annual solar power output from geographic location
dc.typeConference Object

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