Cited by 1,211Solar Radiation and PhotovoltaicsEnergy Load and Power ForecastingPhotovoltaic System Optimization Techniques

Forecasting of photovoltaic power generation and model optimization: a review

Das, Utpal Kumar, Tey, Kok Soon, Seyedmahmoudian, Mehdi, Mekhilef, Saad, Idris, Moh Yamani Idna, Van Deventer, Willem, Horan, Bend, Stojcevski, Alex·2018·Renewable and Sustainable Energy Reviews

Integrity signals

Registry

Crossref · 10.1016/j.rser.2017.08.017

Cites

130 of 130 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Efforts to conserve energy and promote renewable sources have been supported by ML techniques such as LR, ANN, SVM, NC, and EC.

    “Efforts to conserve energy and promote renewable sources have been supported by ML techniques such as LR, ANN, SVM, NC, and EC. Researchers and scholars like Akhter et al. [82], Alsadi and Khatib [183], Das et al. [83], Dawoud, Lin, and Okba [184], Khan et al. [84], Youssef, El-Telbany, and Zekry [185], and Wang and Srinivasan [186] have made contributions in this field of study.”
  • Many ML methods have been used to forecast electricity supply and demand.

    “To date, many ML methods have been used to forecast electricity supply and demand.”

    From Tackling climate change with machine learning (2023) · cites this paper as [161]

  • Since weather fundamentally drives both variable generation and electricity demand, ML algorithms forecasting these quantities should draw from innovations in climate modeling and weather forecasting and in hybrid physics-plus-ML modeling techniques.

    “since weather fundamentally drives both variable generation and electricity demand, ML algorithms forecasting these quantities should draw from innovations in climate modeling and weather forecasting (Section 8) and in hybrid physics-plus-ML modeling techniques [161,818,822].”

    From Tackling climate change with machine learning (2023) · cites this paper as [161]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., Van Deventer, W., Horan, B., & Stojcevski, A.
Affiliations
  • University of Malaya (MY)
  • Deakin University (AU)
  • Swinburne University of Technology (AU)
Corresponding author
Tey, Kok Soon
Venue
Renewable and Sustainable Energy Reviews
Publisher
Elsevier BV
Published
2018-01
Volume
81
Pages
912-928
ISSN
1364-0321 (ISSN-L)
OpenAlex
W2751698537
Cited by
1,211
References
130
Citations by year
2026: 128 · 2025: 172 · 2024: 190 · 2023: 183 · 2022: 192 · 2021: 144 · 2020: 119 · 2019: 56 · 2018: 27
FWCI
8.14
Type
journal article
Language
en
Open access
closed
Locations
  • Renewable and Sustainable Energy Reviews (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Swinburne figshare (Swinburne University of Technology) (submittedVersion)
Funders
  • Fundamental Research Grant Scheme (FP061-2016)
  • University of Malaya (BK052-2016)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Solar Radiation and Photovoltaics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Photovoltaic System Optimization Techniques
Keywords
photovoltaic power generation forecasting, renewable energy integration, hybrid forecasting model, machine learning forecasting models, statistical forecasting models, data preprocessing, forecast evaluation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review