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Cited by 63Energy Load and Power ForecastingWind Energy Research and DevelopmentSolar Radiation and Photovoltaics

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

A unique Markov chain Monte Carlo method for forecasting wind power utilizing time series model

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Al-Duais, Fuad S., Al-Sharpi, Razaz S.·2023·Alexandria Engineering Journal
DOI 10.1016/j.aej.2023.05.019
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Abstract

Concerns that impair human societies frequently include a heavy dependence on petroleum and coal and emissions of greenhouse gases. Thus, adopting renewable energy sources, such as wind power, has become a practical solution to this problem. Therefore, to carry out the research on wind velocity energy, a time-series structure is necessary. This study uses the Markov chain Monte Carlo approach and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to estimate short-term and long-term sustained winds. The significance of building a wind energy system is initially discussed, after which a wind velocity time-series framework based on a SARIMA is presented, followed by a short-term and Long-term wind speed projection. Furthermore, a methodology utilizing the Markov chain Monte Carlo method (MCMC) is suggested to establish a wind energy time-series analysis. This framework draws a Markov chain for time-series data on wind energy to maintain stochasticity and realize the probability transition matrix. Gibbs sampling is employed as well. The model's forecasting abilities were tested using the original database and various efficiency assessment measures, including Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) with efficiency of 13.09 and 1.03. In this study, a framework with the highest KGE and WI as well as the lowest RMSE and MAE was chosen. The findings demonstrate that the approach used in the operation provides outstanding predictabilit…

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Metadata

Authors
Al-Duais, F. S., & Al-Sharpi, R. S.
Affiliations
Prince Sattam Bin Abdulaziz University (SA)
Venue
Alexandria Engineering Journal
Publisher
Elsevier BV
Published
2023-07
Volume
74
Pages
51-63
ISSN
1110-0168 (ISSN-L)
DOI
10.1016/j.aej.2023.05.019
OpenAlex
W4376880932
Cited by
63
References
1
Citations by year
2026: 17 · 2025: 24 · 2024: 16 · 2023: 6
FWCI
4.54
Type
journal article
Language
en
Open access
Licence
Locations
  • Alexandria Engineering Journal (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Prince Sattam bin Abdulaziz University (PSAU2022/01/20216)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Wind Energy Research and Development
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Solar Radiation and Photovoltaics
Keywords
wind power forecasting, wind speed time series, Markov chain Monte Carlo, SARIMA, Gibbs sampling, short-term wind speed forecasting, transition matrix, renewable energy

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