State-of-the-Art Probabilistic Solar Power Forecasting: A Structured Review

In recent years, the installed capacity increment with regard to solar power generation has been highlighted as a crucial role played by Photovoltaic (PV) generation forecasting in integrating a growing number of distributed PV sites into power systems. Nevertheless, because of the PV generation’s u...

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Published in:Pertanika Journal of Science and Technology
Main Author: Rahman N.H.A.; Sulaiman S.I.; Hussin M.Z.; Hairuddin M.A.; Saat E.H.M.; Ashar N.D.K.
Format: Review
Language:English
Published: Universiti Putra Malaysia Press 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208605888&doi=10.47836%2fpjst.32.6.04&partnerID=40&md5=54ae9d2b5cef8ed9d9ec6d218e86e8c6
id 2-s2.0-85208605888
spelling 2-s2.0-85208605888
Rahman N.H.A.; Sulaiman S.I.; Hussin M.Z.; Hairuddin M.A.; Saat E.H.M.; Ashar N.D.K.
State-of-the-Art Probabilistic Solar Power Forecasting: A Structured Review
2024
Pertanika Journal of Science and Technology
32
6
10.47836/pjst.32.6.04
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208605888&doi=10.47836%2fpjst.32.6.04&partnerID=40&md5=54ae9d2b5cef8ed9d9ec6d218e86e8c6
In recent years, the installed capacity increment with regard to solar power generation has been highlighted as a crucial role played by Photovoltaic (PV) generation forecasting in integrating a growing number of distributed PV sites into power systems. Nevertheless, because of the PV generation’s unpredictable nature, deterministic point forecast methods struggle to accurately assess the uncertainties associated with PV generation. This paper presents a detailed structured review of the state-of-the-art concerning Probabilistic Solar Power Forecasting (PSPF), which covers forecasting methods, model comparison, forecasting horizon and quantification metrics. Our review methodology leverages the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to systematically identify primary data sources, focusing on keywords such as probabilistic forecasting, Deep Learning (DL), and Machine learning (ML). Through an extensive and rigorous search of renowned databases such as SCOPUS and Web of Science (WoS), we identified 36 relevant studies (n=36). Consequently, expert scholars decided to develop three themes: (1) Conventional PSPF, (2) PSPF utilizing ML, and (3) PSPF using DL. Probabilistic forecasting is an invaluable tool concerning power systems, especially regarding the rising proportion of renewable energy sources in the energy mix. We tackle the inherent uncertainty of renewable generation, maintain grid stability, and promote efficient energy management and planning. In the end, this research contributes to the development of a power system that is more resilient, reliable, and sustainable. © Universiti Putra Malaysia Press.
Universiti Putra Malaysia Press
1287680
English
Review
All Open Access; Hybrid Gold Open Access
author Rahman N.H.A.; Sulaiman S.I.; Hussin M.Z.; Hairuddin M.A.; Saat E.H.M.; Ashar N.D.K.
spellingShingle Rahman N.H.A.; Sulaiman S.I.; Hussin M.Z.; Hairuddin M.A.; Saat E.H.M.; Ashar N.D.K.
State-of-the-Art Probabilistic Solar Power Forecasting: A Structured Review
author_facet Rahman N.H.A.; Sulaiman S.I.; Hussin M.Z.; Hairuddin M.A.; Saat E.H.M.; Ashar N.D.K.
author_sort Rahman N.H.A.; Sulaiman S.I.; Hussin M.Z.; Hairuddin M.A.; Saat E.H.M.; Ashar N.D.K.
title State-of-the-Art Probabilistic Solar Power Forecasting: A Structured Review
title_short State-of-the-Art Probabilistic Solar Power Forecasting: A Structured Review
title_full State-of-the-Art Probabilistic Solar Power Forecasting: A Structured Review
title_fullStr State-of-the-Art Probabilistic Solar Power Forecasting: A Structured Review
title_full_unstemmed State-of-the-Art Probabilistic Solar Power Forecasting: A Structured Review
title_sort State-of-the-Art Probabilistic Solar Power Forecasting: A Structured Review
publishDate 2024
container_title Pertanika Journal of Science and Technology
container_volume 32
container_issue 6
doi_str_mv 10.47836/pjst.32.6.04
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208605888&doi=10.47836%2fpjst.32.6.04&partnerID=40&md5=54ae9d2b5cef8ed9d9ec6d218e86e8c6
description In recent years, the installed capacity increment with regard to solar power generation has been highlighted as a crucial role played by Photovoltaic (PV) generation forecasting in integrating a growing number of distributed PV sites into power systems. Nevertheless, because of the PV generation’s unpredictable nature, deterministic point forecast methods struggle to accurately assess the uncertainties associated with PV generation. This paper presents a detailed structured review of the state-of-the-art concerning Probabilistic Solar Power Forecasting (PSPF), which covers forecasting methods, model comparison, forecasting horizon and quantification metrics. Our review methodology leverages the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to systematically identify primary data sources, focusing on keywords such as probabilistic forecasting, Deep Learning (DL), and Machine learning (ML). Through an extensive and rigorous search of renowned databases such as SCOPUS and Web of Science (WoS), we identified 36 relevant studies (n=36). Consequently, expert scholars decided to develop three themes: (1) Conventional PSPF, (2) PSPF utilizing ML, and (3) PSPF using DL. Probabilistic forecasting is an invaluable tool concerning power systems, especially regarding the rising proportion of renewable energy sources in the energy mix. We tackle the inherent uncertainty of renewable generation, maintain grid stability, and promote efficient energy management and planning. In the end, this research contributes to the development of a power system that is more resilient, reliable, and sustainable. © Universiti Putra Malaysia Press.
publisher Universiti Putra Malaysia Press
issn 1287680
language English
format Review
accesstype All Open Access; Hybrid Gold Open Access
record_format scopus
collection Scopus
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