Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDaraghmi, Yousef-
dc.contributor.authorDaraghmi, Eman-
dc.contributor.authorDaadoo, Motaz-
dc.contributor.authorAlsadi, Samer-
dc.identifier.citationYousef Daraghmi, Eman Daraghmi, Motaz Daadoo, Samer Alsadi(2020).Forecasting for smart energy: an accurate and efficient negative binomial additive model.20(2),1000-1006.en_US
dc.description.abstractSmart energy requires accurate and effificient short-term electric load forecasting to enable effificient energy management and active real-time power control. Forecasting accuracy is inflfluenced by the char acteristics of electrical load particularly overdispersion, nonlinearity, autocorrelation and seasonal patterns. Although several fundamental forecasting methods have been proposed, accurate and effificient forecasting methods that can consider all electric load characteristics are still needed. Therefore, we propose a novel model for short-term electric load forecasting. The model adopts the negative binomial additive models (NBAM) for handling overdispersion and capturing the nonlinearity of electric load. To address the season ality, the daily load pattern is classifified into high, moderate, and low seasons, and the autocorrelation of load is modeled separately in each season. We also consider the effificiency of forecasting since the NBAM captures the behavior of predictors by smooth functions that are estimated via a scoring algorithm which has low computational demand. The proposed NBAM is applied to real-world data set from Jericho city, and its accuracy and effificiency outperform those of the other models used in this context.en_US
dc.publisherIndonesian Journal of Electrical Engineering and Computer Scienceen_US
dc.subjectNegative binomial additiveen_US
dc.subjectSeasonal patternsen_US
dc.subjectShort-term load forecastingen_US
dc.subjectSmart energyen_US
dc.subjectTemporal autocorrelationen_US
dc.titleForecasting for smart energy: an accurate and efficient negative binomial additive modelen_US
Appears in Collections:Engineering and Technology Faculty

Files in This Item:
File Description SizeFormat 
Forecasting_for_smart_energy_an_accurate_and_effif.pdf326.95 kBAdobe PDFThumbnail

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.