A Framework to Forecast Electricity Consumption of Meters using Automated Ranking and Data Preprocessing

dc.authorscopusid58614387500
dc.authorscopusid58613794700
dc.authorscopusid58613945700
dc.authorscopusid58614237700
dc.authorscopusid24823628300
dc.authorscopusid57193505462
dc.contributor.authorHekimoğlu, Mustafa
dc.contributor.authorÇınar, H.
dc.contributor.authorÇenet, M.N.
dc.contributor.authorOguz, K.D.
dc.contributor.authorYucekaya, A.
dc.contributor.authorHekimoglu, M.
dc.date.accessioned2023-10-19T15:05:22Z
dc.date.available2023-10-19T15:05:22Z
dc.date.issued2023
dc.department-tempGuzel, T., AIMS Analytics Solutions, Istanbul, Turkey; Çınar, H., AIMS Analytics Solutions, Istanbul, Turkey; Çenet, M.N., AIMS Analytics Solutions, Istanbul, Turkey; Oguz, K.D., AIMS Analytics Solutions, Istanbul, Turkey; Yucekaya, A., Department of Industrial Engineering, Kadir Has University, Istanbul, Turkey; Hekimoglu, M., Department of Industrial Engineering, Kadir Has University, Istanbul, Turkeyen_US
dc.description.abstractForecasting electricity consumption is crucial for the operation planning of distribution companies and suppliers and for the success of deregulated electricity markets as a whole. Distribution companies often need consumption forecasting for meters to better plan operations and demand fulfillment. Although it is easier to forecast the aggregated demand for a region, meter based demand forecasting brings challenging issues such as non-uniform usage and uncertain customer consumption patterns. The stochastic nature of the demand for electricity, along with parameters such as temperature, humidity, and work habits, eventually causes deviations from the expected demand. In this paper, real meter data from a regional distribution company is used to cluster the customer using their non-uniform usage and automated ranking mechanism is proposed to select the best method to forecast the consumption. The proposed end-to-end methodology includes data processing, missing value detection and filling, abnormal value detection, and mass reading for meters and is applied to regional data for the period 2017-2018 and provides a powerful tool to forecasts the demand in hourly and daily horizons using only the past demand data. Besides proposing effective methodologies for data preprocessing, 10 different regression methods, 7 regressors, 5 machine learning methods that include LSTM and Ar-net models are used to forecast the meter based consumption. The hourly forecasting errors in the demand, in the Mean Absolute Percentage Error (MAPE) norm, are <4% for most customer groups. The meter based forecast is then aggregated to reach a final demand which is then used for operation and demand planning. The proposed framework can be considered reliable and practical in the circumstances needed to make demand and operation decisions. © 2023, Econjournals. All rights reserved.en_US
dc.description.sponsorshipThe authors acknowledge the financial assistance from the Tubitaken_US
dc.description.sponsorshipThis research received partial funding from Tubitak, Turkey.en_US
dc.identifier.citation0
dc.identifier.doi10.32479/ijeep.13834en_US
dc.identifier.endpage193en_US
dc.identifier.issn2146-4553
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85171990760en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage179en_US
dc.identifier.urihttps://doi.org/10.32479/ijeep.13834
dc.identifier.urihttps://hdl.handle.net/20.500.12469/4856
dc.identifier.volume13en_US
dc.identifier.wosqualityN/A
dc.khas20231019-Scopusen_US
dc.language.isoenen_US
dc.publisherEconjournalsen_US
dc.relation.ispartofInternational Journal of Energy Economics and Policyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectForecastingen_US
dc.subjectMeter Based Consumptionen_US
dc.subjectPredictionen_US
dc.subjectRegressionen_US
dc.subjectSegmentationen_US
dc.subjectTime Series Analysisen_US
dc.titleA Framework to Forecast Electricity Consumption of Meters using Automated Ranking and Data Preprocessingen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublication533132ce-5631-4068-91c5-2806df0f65bb
relation.isAuthorOfPublication.latestForDiscovery533132ce-5631-4068-91c5-2806df0f65bb

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