Please use this identifier to cite or link to this item:
Title: Forecasting for smart energy: an accurate and efficient negative binomial additive model
Authors: Daraghmi, Yousef
Daraghmi, Eman
Daadoo, Motaz
Alsadi, Samer
Keywords: Negative binomial additive;models;Nonlinearity;Overdispersion;Seasonal patterns;Short-term load forecasting;Smart energy;Temporal autocorrelation
Issue Date: 1-May-2020
Publisher: Indonesian Journal of Electrical Engineering and Computer Science
Citation: Yousef Daraghmi, Eman Daraghmi, Motaz Daadoo, Samer Alsadi(2020).Forecasting for smart energy: an accurate and efficient negative binomial additive model.20(2),1000-1006.
Series/Report no.: 20(2);,1000-1006.
Abstract: Smart 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.
ISSN: 2502-4752
metadata.dc.identifier.doi: 10.11591
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.