Forecasting for smart energy: an accurate and effificient negative binomial additive model
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 a...
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| Main Authors: | , , , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2020-11-01.
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| Online Access: | Get fulltext |
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| LEADER | 02698 am a22003253u 4500 | ||
|---|---|---|---|
| 001 | ijeecs21624_14312 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Daraghmi, Yousef-Awwad |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Yaser Daraghmi, Eman |e author |
| 700 | 1 | 0 | |a Daadoo, Motaz |e author |
| 700 | 1 | 0 | |a Alsaadi, Samer |e author |
| 245 | 0 | 0 | |a Forecasting for smart energy: an accurate and effificient negative binomial additive model |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-11-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21624 | ||
| 520 | |a 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. | ||
| 540 | |a Copyright (c) 2020 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Negative binomial additive models; Nonlinearity; Overdispersion; Seasonal patterns; Short-term load forecasting; Smart energy; Temporal autocorrelation | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 20, No 2: November 2020; 1000-1006 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v20.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21624/14312 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21624/14312 |z Get fulltext |