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Advances in Wind and Solar Farm Forecasting—3rd Edition
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Advances in Wind and Solar Farm Forecasting—3rd Edition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: closed (29 April 2025) | Viewed by 7908

Special Issue Editor

Prof. Dr. John Boland

E-Mail Website
Guest Editor
Industrial AI Research Centre, UniSA-STEM, University of South Australia, Mawson Lakes, Adelaide, SA 5095, Australia
Interests: energy; renewable energy; time series analysis and forecasting for climate variables; climate change and risk analysis; AI; machine learning; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intermittent electrical power output from grid-connected solar and wind farms increases the difficulty of managing and maintaining electricity grid stability. The difficulty arises from the uncertainty of the electrical power output from the farms, adversely affecting the control of dispatchable power to balance power supply and demand. Given the high rate of growth of these installations, and the majority of research in forecasting focussed on the resource, it is expedient to turn our attention more to the direct forecasting of output from both wind and solar farms. Additionally, it is extremely important to not only home in on point forecasting, but also to explore robust techniques for probabilistic forecasting. Allied to these topics is the issue of identifying the value of forecasts, both point and probabilistic.

Topics will include:

  • Point forecasting methods for wind or solar farm output
  • Probabilistic forecasting
  • Value of forecasting
  • Classical time series methods
  • Physical forecasting methods
  • Satellite image tools
  • Machine learning methods
  • Numerical weather prediction
  • Blended forecasting tools
  • Spatiotemporal forecasting

Prof. Dr. John Boland
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deterministic output forecasting
  • probabilistic forecasting
  • value of forecasting
  • minimised curtailment

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Related Special Issue

Published Papers (5 papers)

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Research

15 pages, 957 KiB  
Article
ARIMA Markov Model and Its Application of China’s Total Energy Consumption
by Chingfei Luo, Chenzi Liu, Chen Huang, Meilan Qiu and Dewang Li
Energies 2025, 18(11), 2914; https://doi.org/10.3390/en18112914 - 2 Jun 2025
Viewed by 238
Abstract
We propose an auto regressive integrated moving average Markov model (ARIMAMKM) for predicting annual energy consumption in China and enhancing the accuracy of energy consumption forecasts. This novel model extends the traditional auto regressive integrated moving average (ARIMA(p,d,q [...] Read more.
We propose an auto regressive integrated moving average Markov model (ARIMAMKM) for predicting annual energy consumption in China and enhancing the accuracy of energy consumption forecasts. This novel model extends the traditional auto regressive integrated moving average (ARIMA(p,d,q)) model. The stationarity of China’s energy consumption data from 2000 to 2018 is assessed, with an augmented Dickey–Fuller (ADF) test conducted on the d-order difference series. Based on the auto correlation function (ACF) and partial auto correlation function (PACF) plots of the difference time series, the optimal parameters p and q are selected using the Akaike information criterion (AIC) and Bayesian information criterion (BIC), thereby determining the specific ARIMA configuration. By simulating real values using the ARIMA model and calculating relative errors, the estimated values are categorized into states. These states are then combined with a Markov transition probability matrix to determine the final predicted values. The ARIMAMKM model is validated using China’s energy consumption data, achieving high prediction accuracy as evidenced by metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), STD, and R2. Comparative analysis demonstrates that the ARIMAMKM model outperforms five other competitive models: the grey model (GM(1,1)), ARIMA(0,4,2), quadratic function model (QFM), nonlinear auto regressive neural network (NAR), and fractional grey model (FGM(1,1)) in terms of fitting performance. Additionally, the model is applied to Guangdong province’s resident population data to further verify its validity and practicality. Full article
(This article belongs to the Special Issue Advances in Wind and Solar Farm Forecasting—3rd Edition)
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20 pages, 1950 KiB  
Article
Wind Power Prediction Method and Outlook in Microtopographic Microclimate
by Jia He, Fangchun Tang, Junxin Feng, Chaoyang Liu, Mengyan Ni, Youguang Chen, Hongdeng Mei, Qin Hu and Xingliang Jiang
Energies 2025, 18(7), 1686; https://doi.org/10.3390/en18071686 - 27 Mar 2025
Viewed by 329
Abstract
With the increase in installed capacity of wind turbines, the stable operation of the power system has been affected. Accurate prediction of wind power is an important condition to ensure the healthy development of the wind power industry and the safe operation of [...] Read more.
With the increase in installed capacity of wind turbines, the stable operation of the power system has been affected. Accurate prediction of wind power is an important condition to ensure the healthy development of the wind power industry and the safe operation of the power grid. This paper first introduces the current status of wind power prediction methods under normal weather, and introduces them in detail from three aspects: physical model method, statistical prediction method and combined prediction method. Then, from the perspectives of numerical simulation analysis and statistical prediction methods, the wind power prediction method under icy conditions is introduced, and the problems faced by the existing methods are pointed out. Then, the accurate prediction of wind power under icing weather is considered, and two possible research directions for wind power prediction under icy weather are proposed: a statistical prediction method for classifying and clustering wind turbines according to microtopography, combining large-scale meteorological parameters with small-scale meteorological parameter correlation models and using machine learning for cluster power prediction, and a power prediction model converted from the power prediction model during normal operation of the wind turbine to the power prediction model during icing. Finally, the research on wind power prediction under ice-covered weather is summarized, and further research in this area is prospected. Full article
(This article belongs to the Special Issue Advances in Wind and Solar Farm Forecasting—3rd Edition)
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18 pages, 2515 KiB  
Article
Analysis of the Effectiveness of ARIMA, SARIMA, and SVR Models in Time Series Forecasting: A Case Study of Wind Farm Energy Production
by Kamil Szostek, Damian Mazur, Grzegorz Drałus and Jacek Kusznier
Energies 2024, 17(19), 4803; https://doi.org/10.3390/en17194803 - 25 Sep 2024
Cited by 9 | Viewed by 3974
Abstract
The primary objective of this study is to evaluate the accuracy of different forecasting models for monthly wind farm electricity production. This study compares the effectiveness of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Support Vector Regression (SVR). [...] Read more.
The primary objective of this study is to evaluate the accuracy of different forecasting models for monthly wind farm electricity production. This study compares the effectiveness of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Support Vector Regression (SVR). This study utilizes data from two wind farms located in Poland—‘Gizałki’ and ‘Łęki Dukielskie’—to exclude the possibility of biased results due to specific characteristics of a single farm and to allow for a more comprehensive comparison of the effectiveness of both time series analysis methods. Model parameterization was optimized through a grid search based on the Mean Absolute Percentage Error (MAPE). The performance of the best models was evaluated using Mean Bias Error (MBE), MAPE, Mean Absolute Error (MAE), and R2Score. For the Gizałki farm, the ARIMA model outperformed SARIMA and SVR, while for the Łęki Dukielskie farm, SARIMA proved to be the most accurate, highlighting the importance of optimizing seasonal parameters. The SVR method demonstrated the lowest effectiveness for both datasets. The results indicate that the ARIMA and SARIMA models are effective for forecasting wind farm energy production. However, their performance is influenced by the specificity of the data and seasonal patterns. The study provides an in-depth analysis of the results and offers suggestions for future research, such as extending the data to include multidimensional time series. Our findings have practical implications for enhancing the accuracy of wind farm energy forecasts, which can significantly improve operational efficiency and planning. Full article
(This article belongs to the Special Issue Advances in Wind and Solar Farm Forecasting—3rd Edition)
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15 pages, 2912 KiB  
Article
A Standardized Sky Condition Classification Method for Multiple Timescales and Its Applications in the Solar Industry
by Shukla Poddar, Merlinde Kay and John Boland
Energies 2024, 17(18), 4616; https://doi.org/10.3390/en17184616 - 14 Sep 2024
Viewed by 1069
Abstract
The deployment of photovoltaic (PV) systems has increased globally to meet renewable energy targets. Intermittent PV power generated due to cloud-induced variability introduces reliability and grid stability issues at higher penetration levels. Variability in power generation can induce voltage fluctuations within the distribution [...] Read more.
The deployment of photovoltaic (PV) systems has increased globally to meet renewable energy targets. Intermittent PV power generated due to cloud-induced variability introduces reliability and grid stability issues at higher penetration levels. Variability in power generation can induce voltage fluctuations within the distribution system and cause adverse effects on power quality. Therefore, it is essential to quantify resource variability to mitigate an intermittent power supply. In this study, we propose a new scheme to classify the sky conditions that are based on two common variability metrices: daily clear-sky index and normalized aggregate ramp rates. The daily clear-sky index estimates the cloudiness in the sky, and ramp rates account for the variability introduced in the system generation due to sudden cloud movements. This classification scheme can identify clear-sky, highly variable, low intermittent, high intermittent and overcast days. By performing a Chi-square test on the training and test sets, we obtain Chi-square statistic values greater than 3 with p-value > 0.05. This indicates that the distribution of the training and test clusters are similar, indicating the robustness of the proposed sky classification scheme. We have demonstrated the applicability of the scheme with diverse datasets to show that the proposed classification scheme can be homogenously applied to any dataset globally despite their temporal resolution. Using various case studies, we demonstrate the potential applications of the scheme for understanding resource allocation, site selection, estimating future intermittency due to climate change, and cloud enhancement effects. The proposed sky classification scheme enhances the precision and reliability of solar energy forecasts, optimizing system performance and maximizing energy production efficiency. This improved accuracy is crucial for variability control and planning, ensuring optimal output from PV plants. Full article
(This article belongs to the Special Issue Advances in Wind and Solar Farm Forecasting—3rd Edition)
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29 pages, 25782 KiB  
Article
An Integrated CEEMDAN to Optimize Deep Long Short-Term Memory Model for Wind Speed Forecasting
by Yingying He, Likai Zhang, Tengda Guan and Zheyu Zhang
Energies 2024, 17(18), 4615; https://doi.org/10.3390/en17184615 - 14 Sep 2024
Cited by 1 | Viewed by 1296
Abstract
Accurate wind speed forecasting is crucial for the efficient operation of renewable energy platforms, such as wind turbines, as it facilitates more effective management of power output and maintains grid reliability and stability. However, the inherent variability and intermittency of wind speed present [...] Read more.
Accurate wind speed forecasting is crucial for the efficient operation of renewable energy platforms, such as wind turbines, as it facilitates more effective management of power output and maintains grid reliability and stability. However, the inherent variability and intermittency of wind speed present significant challenges for achieving precise forecasts. To address these challenges, this study proposes a novel method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a deep learning-based Long Short-Term Memory (LSTM) network for wind speed forecasting. In the proposed method, CEEMDAN is utilized to decompose the original wind speed signal into different modes to capture the multiscale temporal properties and patterns of wind speeds. Subsequently, LSTM is employed to predict each subseries derived from the CEEMDAN process. These individual subseries predictions are then combined to generate the overall final forecast. The proposed method is validated using real-world wind speed data from Austria and Almeria. Experimental results indicate that the proposed method achieves minimal mean absolute percentage errors of 0.3285 and 0.1455, outperforming other popular models across multiple performance criteria. Full article
(This article belongs to the Special Issue Advances in Wind and Solar Farm Forecasting—3rd Edition)
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