Topic Editors

Department of Energy, Politecnico di Milano, 20156 Milan, Italy
Department of Energy, Politecnico di Milano, 20156 Milan, Italy
Department of Energy, Politecnico di Milano, 20156 Milan, Italy

Solar and Wind Power and Energy Forecasting, 2nd Edition

Abstract submission deadline
31 May 2026
Manuscript submission deadline
31 July 2026
Viewed by
7687

Topic Information

Dear Colleagues,

This Topic is a continuation of the previous successful Topic “Solar and Wind Power and Energy Forecasting”.

The renewable-energy-based generation of electricity is currently experiencing rapid growth in electric grids. The intermittent input from renewable energy sources (RES), as a consequence, creates problems in balancing the energy supply and demand. Thus, forecasting of RES power generation is vital to help grid operators to better manage the electric balance between power demand and supply and to improve the penetration of distributed renewable energy sources and, in standalone hybrid systems, for the optimum size of all its components and to improve the reliability of the isolated systems.

This Topic on “Solar and Wind Power and Energy Forecasting, 2nd Edition” is intended to disseminate new promising methods and techniques to forecast the output power and energy of intermittent renewable energy sources.

Dr. Emanuele Ogliari
Dr. Alessandro Niccolai
Prof. Dr. Sonia Leva
Topic Editors

Keywords

  • RES integration
  • forecasting techniques
  • machine learning
  • computational intelligence
  • optimization
  • PV system
  • wind system

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.5 5.5 2011 19.8 Days CHF 2400 Submit
Energies
energies
3.2 7.3 2008 16.2 Days CHF 2600 Submit
Forecasting
forecasting
3.2 7.1 2019 22.9 Days CHF 1800 Submit
Solar
solar
- 4.3 2021 21.3 Days CHF 1000 Submit
Wind
wind
1.7 2.9 2021 28.3 Days CHF 1200 Submit
Batteries
batteries
4.8 6.6 2015 18.5 Days CHF 2700 Submit

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Published Papers (5 papers)

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25 pages, 6191 KB  
Article
Machine Learning Forecasting of Direct Solar Radiation: A Multi-Model Evaluation with Trigonometric Cyclical Encoding
by Latif Bukari Rashid, Shahzada Zaman Shuja and Shafiqur Rehman
Forecasting 2025, 7(4), 58; https://doi.org/10.3390/forecast7040058 - 17 Oct 2025
Abstract
As the world is shifting toward cleaner energy sources, accurate forecasting of solar radiation is critical for optimizing the performance and integration of solar energy systems. In this study, we explore eight machine learning models, namely, Random Forest Regressor, Linear Regression Model, Artificial [...] Read more.
As the world is shifting toward cleaner energy sources, accurate forecasting of solar radiation is critical for optimizing the performance and integration of solar energy systems. In this study, we explore eight machine learning models, namely, Random Forest Regressor, Linear Regression Model, Artificial Neural Network, k-Nearest Neighbors, Support Vector Regression, Gradient Boosting Regressor, Gaussian Process Regression, and Deep Learning, as to their use in forecasting direct solar radiation across six climatically diverse regions in the Kingdom of Saudi Arabia. The models were evaluated using eight statistical metrics along with time-series and absolute error analyses. A key contribution of this work is the introduction of Trigonometric Cyclical Encoding, which has significantly improved temporal representation learning. Comparative SHAP-based feature-importance analysis revealed that Trigonometric Cyclical Encoding enhanced the explanatory power of temporal features by 49.26% for monthly cycles and 53.30% for daily cycles. The findings show that Deep Learning achieved the lowest root mean square error, as well as the highest coefficient of determination, while Artificial Neural Network demonstrated consistently high accuracy across the sites. Support Vector Regression performed optimally but was less reliable in some regions. Error and time-series analyses reveal that Artificial Neural Network and Deep Learning maintained stable prediction accuracy throughout high solar radiation seasons, whereas Linear Regression, Random Forest Regressor, and k-Nearest Neighbors showed greater fluctuations. The proposed Trigonometric Cyclical Encoding technique further enhanced model performance by maintaining the overall fitness of the models, which ranged between 81.79% and 94.36% in all scenarios. This paper supports the effective planning of solar energy and integration in challenging climatic conditions. Full article
(This article belongs to the Topic Solar and Wind Power and Energy Forecasting, 2nd Edition)
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26 pages, 5742 KB  
Article
Multiscale Time Series Modeling in Energy Demand Prediction: A CWT-Aided Hybrid Model
by Elif Sezer, Güngör Yıldırım and Mahmut Temel Özdemir
Appl. Sci. 2025, 15(19), 10801; https://doi.org/10.3390/app151910801 - 8 Oct 2025
Viewed by 474
Abstract
In the contemporary energy landscape, the increasing demand for electricity and the inherent uncertainties associated with the integration of renewable resources have rendered the accurate and reliable forecasting of short- and long-term demand imperative. Energy demand forecasting, fundamentally a time series problem, can [...] Read more.
In the contemporary energy landscape, the increasing demand for electricity and the inherent uncertainties associated with the integration of renewable resources have rendered the accurate and reliable forecasting of short- and long-term demand imperative. Energy demand forecasting, fundamentally a time series problem, can be inherently complex, nonlinear, and multi-scale. Therefore, interest in artificial intelligence–based methods that provide high performance for short- and long-term forecasting, rather than traditional methods, has increased in order to solve these problems. In this study, a hybrid artificial intelligence model based on LSTM, GRU, and Random Forest, utilizing a distinct mechanism to address these types of problems, is proposed. The Multi-Scale Sliding Window (MSSW) approach was utilized for the model’s input data to capture the dynamics of the time series at different scales. The optimization of windows was conducted using the Continuous Wavelet Transform (CWT) method to determine the optimal window sizes within the MSSW structure in a data-driven manner. Experimental studies on Panama’s real energy demand data from 2015 to 2020 show that the CWT-aided MSSW-hybrid model forecasts better with lower error rates (0.007 MAE, 0.009 RMSE, 1.051% MAPE) than single models and manually determined window sizes. The results of the study demonstrate the importance of hybrid structures and window optimization in energy demand forecasting. Full article
(This article belongs to the Topic Solar and Wind Power and Energy Forecasting, 2nd Edition)
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29 pages, 2173 KB  
Review
A Review and Prototype Proposal for a 3 m Hybrid Wind–PV Rotor with Flat Blades and a Peripheral Ring
by George Daniel Chiriță, Viviana Filip, Alexis Daniel Negrea and Dragoș Vladimir Tătaru
Appl. Sci. 2025, 15(16), 9119; https://doi.org/10.3390/app15169119 - 19 Aug 2025
Viewed by 728
Abstract
This paper presents a literature review of low-power hybrid wind–photovoltaic (PV) systems and introduces a 3 m diameter prototype rotor featuring twelve PV-coated pivoting blades stiffened by a peripheral rim. Existing solutions—foldable umbrella concepts, Darrieus rotors with PV-integrated blades, and morphing blades—are surveyed, [...] Read more.
This paper presents a literature review of low-power hybrid wind–photovoltaic (PV) systems and introduces a 3 m diameter prototype rotor featuring twelve PV-coated pivoting blades stiffened by a peripheral rim. Existing solutions—foldable umbrella concepts, Darrieus rotors with PV-integrated blades, and morphing blades—are surveyed, and current gaps in simultaneous wind + PV co-generation on a single moving structure are highlighted. Key performance indicators such as power coefficient (Cp), DC ripple, cell temperature difference (ΔT), and levelised cost of energy (LCOE) are defined, and an integrated assessment methodology is proposed based on blade element momentum (BEM) and computational fluid dynamics (CFD) modelling, dynamic current–voltage (I–V) testing, and failure modes and effects analysis (FMEA) to evaluate system performance and reliability. Preliminary results point to moderate aerodynamic penalties (ΔCp ≈ 5–8%), PV output during rotation equal to 15–25% of the nominal PV power (PPV), and an estimated 70–75% reduction in blade–root bending moment when the peripheral ring converts each blade from a cantilever to a simply supported member, resulting in increased blade stiffness. Major challenges include the collective pitch mechanism, dynamic shading, and wear of rotating components (slip rings); however, the suggested technical measures—maximum power point tracking (MPPT), string segmentation, and redundant braking—keep performance within acceptable limits. This study concludes that the concept shows promise for distributed microgeneration, provided extensive experimental validation and IEC 61400-2-compliant standardisation are pursued. This paper has a dual scope: (i) a concise literature review relevant to low-Re flat-blade aerodynamics and ring-stiffened rotor structures and (ii) a multi-fidelity aero-structural study that culminates in a 3 m prototype proposal. We present the first evaluation of a hybrid wind–PV rotor employing untwisted flat-plate blades stiffened by a peripheral ring. Using low-Re BEM for preliminary loading, steady-state RANS-CFD (k-ω SST) for validation, and elastic FEM for sizing, we assemble a coherent load/performance dataset. After upsizing the hub pins (Ø 30 mm), ring (50 × 50 mm), and spokes (Ø 40 mm), von Mises stresses remain < 25% of the 6061-T6 yield limit and tip deflection ≤ 0.5%·R acrosscut-in (3 m s−1), nominal (5 m s−1), and extreme (25 m s−1) cases. CFD confirms a broad efficiency plateau at λ = 2.4–2.8 for β ≈ 10° and near-zero shaft torque at β = 90°, supporting a three-step pitch schedule (20° start-up → 10° nominal → 90° storm). Cross-model deviations for Cp, torque, and pressure/force distributions remain within ± 10%. This study addresses only the rotor; off-the-shelf generator, brake, screw-pitch, and azimuth/tilt drives are intended for later integration. The results provide a low-cost manufacturable architecture and a validated baseline for full-scale testing and future transient CFD/FEM iterations. Full article
(This article belongs to the Topic Solar and Wind Power and Energy Forecasting, 2nd Edition)
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30 pages, 2575 KB  
Review
The Potential of Utility-Scale Hybrid Wind–Solar PV Power Plant Deployment: From the Data to the Results
by Luis Arribas, Javier Domínguez, Michael Borsato, Ana M. Martín, Jorge Navarro, Elena García Bustamante, Luis F. Zarzalejo and Ignacio Cruz
Wind 2025, 5(3), 16; https://doi.org/10.3390/wind5030016 - 7 Jul 2025
Cited by 1 | Viewed by 1974
Abstract
The deployment of utility-scale hybrid wind–solar PV power plants is gaining global attention due to their enhanced performance in power systems with high renewable energy penetration. To assess their potential, accurate estimations must be derived from the available data, addressing key challenges such [...] Read more.
The deployment of utility-scale hybrid wind–solar PV power plants is gaining global attention due to their enhanced performance in power systems with high renewable energy penetration. To assess their potential, accurate estimations must be derived from the available data, addressing key challenges such as (1) the spatial and temporal resolution requirements, particularly for renewable resource characterization; (2) energy balances aligned with various business models; (3) regulatory constraints (environmental, technical, etc.); and (4) the cost dependencies of the different components and system characteristics. When conducting such analyses at the regional or national scale, a trade-off must be achieved to balance accuracy with computational efficiency. This study reviews existing experiences in hybrid plant deployment, with a focus on Spain, identifying the lack of national-scale product cost models for HPPs as the main gap and establishing a replicable methodology for hybrid plant mapping. A simplified example is shown using this methodology for a country-level analysis. Full article
(This article belongs to the Topic Solar and Wind Power and Energy Forecasting, 2nd Edition)
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22 pages, 3562 KB  
Article
Resilience Under Heatwaves: Croatia’s Power System During the July 2024 Heatwave and the Role of Variable Renewable Energy by 2030
by Paolo Blecich, Igor Bonefačić, Tomislav Senčić and Igor Wolf
Appl. Sci. 2025, 15(12), 6440; https://doi.org/10.3390/app15126440 - 7 Jun 2025
Cited by 1 | Viewed by 3841
Abstract
This study analyzes the record electricity consumption in Croatia during the July 2024 heatwave and evaluates how the increased deployment of onshore wind and solar photovoltaics (PV) could mitigate a similar event in the future. Electricity demand and generation patterns under current (2024) [...] Read more.
This study analyzes the record electricity consumption in Croatia during the July 2024 heatwave and evaluates how the increased deployment of onshore wind and solar photovoltaics (PV) could mitigate a similar event in the future. Electricity demand and generation patterns under current (2024) and projected (2030) scenarios have been simulated using a sub-hourly power system model. The findings show that during the July 2024 heatwave, Croatia imported 35% of the electricity, with prices exceeding 400 €/MWh during peak hours. By 2030, the expanded wind and solar PV sectors (1.5 GW each) will increase the renewable share from 38.8% in July 2024 to 54.7% in July 2030. On the annual level, renewable energy generation increases from 53.8% in 2024 up to 66.9% in 2030. As result, the carbon intensity of the power sector will reduce from 223 gCO2eq/kWhel in 2024 to 197 gCO2eq/kWhel in 2030. The share of fossil fuel generation will increase slightly, from 19.7% in 2024 to 22% in 2030, but more significantly in the summer to meet the heatwave-induced electricity demand. Besides that, short-term energy storage of 2 GWh (400 MW discharge over 5 h) could effectively manage evening peak demands after solar PV ceases production. Full article
(This article belongs to the Topic Solar and Wind Power and Energy Forecasting, 2nd Edition)
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