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Advances in Wind and Solar Farm Forecasting

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 (31 March 2022) | Viewed by 20173

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Guest Editor
Industrial AI Research Centre, UniSA STEM, University of South Australia, Adelaide, Australia
Interests: time series analysis and forecasting for climate variables; renewable energy utilization; climate change and risk analysis; heat transfer and energy efficient buildings; water harvesting, ecological footprint; sustainable diet
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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

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Keywords

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

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Published Papers (8 papers)

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Research

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20 pages, 5502 KiB  
Article
On Solar Radiation Prediction for the East–Central European Region
by Michał Mierzwiak, Krzysztof Kroszczyński and Andrzej Araszkiewicz
Energies 2022, 15(9), 3153; https://doi.org/10.3390/en15093153 - 26 Apr 2022
Cited by 4 | Viewed by 1571
Abstract
The aim of this paper is to present the results of the Weather Research and Forecasting (WRF) model of solar radiation for moderate climatic zones. This analysis covered the area of northeastern Germany. Due to very unfavorable solar energy conditions in this region [...] Read more.
The aim of this paper is to present the results of the Weather Research and Forecasting (WRF) model of solar radiation for moderate climatic zones. This analysis covered the area of northeastern Germany. Due to very unfavorable solar energy conditions in this region for at least 1/3 of the year, we decided to select the dates with the most representative conditions: passing warm fronts, cold fronts, and occluded fronts (two cases each). As the reference, two cloudless conditions during high-pressure situations were chosen. Two different shortwave radiation schemes—Rapid Radiative Transfer Model for general circulation model (RRTMG) and Dudhia—were tested. The obtained results were compared with in situ data measured at Deutscher Wetterdienst (DWD) stations and then with European Medium-Range Weather Forecast reanalysis (ERA5) data. The results showed that for high-pressure situations, the mean correlations with measured data were above 90%. The Dudhia scheme, in addition to the expected good results for the high-pressure situation, showed better results than RRTMG for the warm and cold fronts as well. The forecast using the RRTMG scheme gave the best results for the occluded front, which were also better than those of the ERA5 model. Full article
(This article belongs to the Special Issue Advances in Wind and Solar Farm Forecasting)
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26 pages, 6182 KiB  
Article
A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification
by Jayesh Thaker and Robert Höller
Energies 2022, 15(8), 2837; https://doi.org/10.3390/en15082837 - 13 Apr 2022
Cited by 5 | Viewed by 2703
Abstract
Sustainable energy systems rely on energy yield from renewable resources such as solar radiation and wind, which are typically not on-demand and need to be stored or immediately consumed. Solar irradiance is a highly stochastic phenomenon depending on fluctuating atmospheric conditions, in particular [...] Read more.
Sustainable energy systems rely on energy yield from renewable resources such as solar radiation and wind, which are typically not on-demand and need to be stored or immediately consumed. Solar irradiance is a highly stochastic phenomenon depending on fluctuating atmospheric conditions, in particular clouds and aerosols. The complexity of weather conditions in terms of many variable parameters and their inherent unpredictability limit the performance and accuracy of solar power forecasting models. As renewable power penetration in electricity grids increases due to the rapid increase in the installation of photovoltaics (PV) systems, the resulting challenges are amplified. A regional PV power prediction system is presented and evaluated by providing forecasts up to 72 h ahead with an hourly time resolution. The proposed approach is based on a local radiation forecast model developed by Blue Sky. In this paper, we propose a novel method of deriving forecast equations by using an irradiance classification approach to cluster the dataset. A separate equation is derived using the GEKKO optimization tool, and an algorithm is assigned for each cluster. Several other linear regressions, time series and machine learning (ML) models are applied and compared. A feature selection process is used to select the most important weather parameters for solar power generation. Finally, considering the prediction errors in each cluster, a weighted average and an average ensemble model are also developed. The focus of this paper is the comparison of the capability and performance of statistical and ML methods for producing a reliable hourly day-ahead forecast of PV power by applying different skill scores. The proposed models are evaluated, results are compared for different models and the probabilistic time series forecast is presented. Results show that the irradiance classification approach reduces the forecasting error by a considerable margin, and the proposed GEKKO optimized model outperforms other machine learning and ensemble models. These findings also emphasize the potential of ML-based methods, which perform better in low-power and high-cloud conditions, as well as the need to build an ensemble or hybrid model based on different ML algorithms to achieve improved projections. Full article
(This article belongs to the Special Issue Advances in Wind and Solar Farm Forecasting)
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24 pages, 10730 KiB  
Article
Short-Term Deterministic Solar Irradiance Forecasting Considering a Heuristics-Based, Operational Approach
by Armando Castillejo-Cuberos, John Boland and Rodrigo Escobar
Energies 2021, 14(18), 6005; https://doi.org/10.3390/en14186005 - 21 Sep 2021
Cited by 3 | Viewed by 2021
Abstract
Solar energy is an economic and clean power source subject to natural variability, while energy storage might attenuate it, ultimately, effective and operationally feasible forecasting techniques for energy management are needed for better grid integration. This work presents a novel deterministic forecast method [...] Read more.
Solar energy is an economic and clean power source subject to natural variability, while energy storage might attenuate it, ultimately, effective and operationally feasible forecasting techniques for energy management are needed for better grid integration. This work presents a novel deterministic forecast method considering: irradiance pattern classification, Markov chains, fuzzy logic and an operational approach. The method developed was applied in a rolling manner for six years to a target location with no prior data to assess performance and its changes as new local data becomes available. Clearness index, diffuse fraction and irradiance hourly forecasts are analyzed on a yearly basis but also for 20 day types, and compared against smart persistence. Results show the proposed method outperforms smart persistence by ~10% for clearness index and diffuse fraction on the base case, but there are significant differences across the 20 day types analyzed, reaching up to +60% for clear days. Forecast lead time has the greatest impact in forecasting performance, which is important for any practical implementation. Seasonality in data gaps or rejected data can have a definite effect in performance assessment. A novel, comprehensive and detailed analysis framework was shown to present a better assessment of forecasters’ performance. Full article
(This article belongs to the Special Issue Advances in Wind and Solar Farm Forecasting)
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20 pages, 4584 KiB  
Article
Prediction of Solar Power Using Near-Real Time Satellite Data
by Abhnil Amtesh Prasad and Merlinde Kay
Energies 2021, 14(18), 5865; https://doi.org/10.3390/en14185865 - 16 Sep 2021
Cited by 12 | Viewed by 2222
Abstract
Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a [...] Read more.
Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts. Full article
(This article belongs to the Special Issue Advances in Wind and Solar Farm Forecasting)
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15 pages, 1479 KiB  
Article
Probabilistic Forecasting of Wind and Solar Farm Output
by John Boland and Sleiman Farah
Energies 2021, 14(16), 5154; https://doi.org/10.3390/en14165154 - 20 Aug 2021
Cited by 1 | Viewed by 1562
Abstract
Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This includes not only forecasting the expected level, but also putting error bounds on the forecast. The National Electricity [...] Read more.
Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This includes not only forecasting the expected level, but also putting error bounds on the forecast. The National Electricity Market (NEM) in Australia operates on a five minute basis. We used statistical forecasting tools to generate forecasts with prediction intervals, trialing them on one wind and one solar farm. In classical time series forecasting, construction of prediction intervals is rudimentary if the error variance is constant—Termed homoscedastic. However, if the variance changes—Either conditionally as with wind farms, or systematically because of diurnal effects as with solar farms—The task is much more complicated. The tools were trained on segments of historical data and then tested on data not used in the training. Results from the testing set showed good performance using metrics, including Coverage and Interval Score. The methods used can be adapted to various time scales for short term forecasting. Full article
(This article belongs to the Special Issue Advances in Wind and Solar Farm Forecasting)
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19 pages, 1025 KiB  
Article
A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors
by Thomas Carrière, Rodrigo Amaro e Silva, Fuqiang Zhuang, Yves-Marie Saint-Drenan and Philippe Blanc
Energies 2021, 14(16), 4951; https://doi.org/10.3390/en14164951 - 12 Aug 2021
Cited by 9 | Viewed by 2601
Abstract
Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which [...] Read more.
Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index (kc) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the kc and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric. Full article
(This article belongs to the Special Issue Advances in Wind and Solar Farm Forecasting)
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Review

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22 pages, 1727 KiB  
Review
Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints
by Yuan-Kang Wu, Cheng-Liang Huang, Quoc-Thang Phan and Yuan-Yao Li
Energies 2022, 15(9), 3320; https://doi.org/10.3390/en15093320 - 02 May 2022
Cited by 29 | Viewed by 3834
Abstract
Solar power has rapidly become an increasingly important energy source in many countries over recent years; however, the intermittent nature of photovoltaic (PV) power generation has a significant impact on existing power systems. To reduce this uncertainty and maintain system security, precise solar [...] Read more.
Solar power has rapidly become an increasingly important energy source in many countries over recent years; however, the intermittent nature of photovoltaic (PV) power generation has a significant impact on existing power systems. To reduce this uncertainty and maintain system security, precise solar power forecasting methods are required. This study summarizes and compares various PV power forecasting approaches, including time-series statistical methods, physical methods, ensemble methods, and machine and deep learning methods, the last of which there is a particular focus. In addition, various optimization algorithms for model parameters are summarized, the crucial factors that influence PV power forecasts are investigated, and input selection for PV power generation forecasting models are discussed. Probabilistic forecasting is expected to play a key role in the PV power forecasting required to meet the challenges faced by modern grid systems, and so this study provides a comparative analysis of existing deterministic and probabilistic forecasting models. Additionally, the importance of data processing techniques that enhance forecasting performance are highlighted. In comparison with the extant literature, this paper addresses more of the issues concerning the application of deep and machine learning to PV power forecasting. Based on the survey results, a complete and comprehensive solar power forecasting process must include data processing and feature extraction capabilities, a powerful deep learning structure for training, and a method to evaluate the uncertainty in its predictions. Full article
(This article belongs to the Special Issue Advances in Wind and Solar Farm Forecasting)
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18 pages, 447 KiB  
Review
Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review
by John Boland, Sleiman Farah and Lei Bai
Energies 2022, 15(1), 370; https://doi.org/10.3390/en15010370 - 05 Jan 2022
Cited by 2 | Viewed by 2321
Abstract
Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This is especially the case in Australia, where there has been a massive increase in solar and wind farms [...] Read more.
Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This is especially the case in Australia, where there has been a massive increase in solar and wind farms in the last 15 years, as well as in roof top solar, both domestic and commercial. For example, in 2020, 27% of the electricity in Australia was from renewable sources, and in South Australia almost 60% was from wind and solar. In the literature, there has been extensive research reported on solar and wind resource, entailing both point and interval forecasts, but there has been much less focus on the forecasting of output from wind and solar systems. In this review, we canvass both what has been reported and also what gaps remain. In the case of the latter topic, there are numerous aspects that are not well dealt with in the literature. We have added discussion on the value of forecasts, rather than just focusing on forecast skill. Further, we present a section on how to deal with conditionally changing variance, a topic that has little focus in the literature. One other topic may be particularly important in Australia at the moment, but may become more widespread. This is how to deal with the concept of a clear sky output from a solar farm when the field is oversized compared to the inverter capacity, resulting in a plateau for the output. Full article
(This article belongs to the Special Issue Advances in Wind and Solar Farm Forecasting)
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