Topic Editors

Photovoltaic Solar Energy Unity (Renewable Energy Division) CIEMAT, 28040 Madrid, Spain
Department of Electrical and Thermal Engineering, Design and Projects, University of Huelva, 210047 Huelva, Spain

Solar Forecasting and Smart Photovoltaic Systems

Abstract submission deadline
30 September 2024
Manuscript submission deadline
30 November 2024
Viewed by
10682

Topic Information

Dear Colleagues,

Solar PV is gaining importance and presence in the energy mix with an increasing penetration today and foreseen in the near future. Solar forecasting (either irradiance or PV power) is of great significance in gird management and storage system operation. PV power modeling and forecasting is thus a topic of high interest, with noticeable contributions and developments so far. However, the growth rate of PV systems and the broad variety of configurations and applications (large PV plants, floating PV, agro-PV, BIPV and distributed PV power, and so on) foster the need to go further in modeling and forecasting capabilities.  It is a pleasure to invite the research community to submit review or regular research papers on, but not limited to, the following relevant topics related to “Solar Forecasting and Smart Photovoltaic Systems”:

  • Deterministic forecast;
  • Probabilistic forecast;
  • Digital twins in PV;
  • Smart grids in cities;
  • Grid management in near 100% renewable systems;
  • PV power modeling;
  • PV power forecasting;
  • Solar irradiance forecasting;
  • BIPV;
  • PV in urban environments;
  • Floating PV;
  • Agro-PV;
  • Machine learning in PV;
  • Firm PV power forecast;
  • Off-grid PV systems;
  • Flexibility market with very high renewables.

Dr. Jesús Polo
Dr. Gabriel López Rodríguez
Topic Editors

Keywords

  • PV modeling
  • PV forecasting
  • solar forecasting
  • solar resource assessment
  • solar grid management
  • BIPV
  • off-grid PV

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Energies
energies
3.2 5.5 2008 16.1 Days CHF 2600 Submit
Processes
processes
3.5 4.7 2013 13.7 Days CHF 2400 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit
Solar
solar
- - 2021 16.9 Days CHF 1000 Submit
Sustainability
sustainability
3.9 5.8 2009 18.8 Days CHF 2400 Submit

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Published Papers (7 papers)

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37 pages, 2630 KiB  
Review
A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence
by Khadija Barhmi, Chris Heynen, Sara Golroodbari and Wilfried van Sark
Solar 2024, 4(1), 99-135; https://doi.org/10.3390/solar4010005 - 22 Feb 2024
Cited by 1 | Viewed by 1609
Abstract
Solar energy forecasting is essential for the effective integration of solar power into electricity grids and the optimal management of renewable energy resources. Distinguishing itself from the existing literature, this review study provides a nuanced contribution by centering on advancements in forecasting techniques. [...] Read more.
Solar energy forecasting is essential for the effective integration of solar power into electricity grids and the optimal management of renewable energy resources. Distinguishing itself from the existing literature, this review study provides a nuanced contribution by centering on advancements in forecasting techniques. While preceding reviews have examined factors such as meteorological input parameters, time horizons, the preprocessing methodology, optimization, and sample size, our study uniquely delves into a diverse spectrum of time horizons, spanning ultrashort intervals (1 min to 1 h) to more extended durations (up to 24 h). This temporal diversity equips decision makers in the renewable energy sector with tools for enhanced resource allocation and refined operational planning. Our investigation highlights the prominence of Artificial Intelligence (AI) techniques, specifically focusing on Neural Networks in solar energy forecasting, and we review supervised learning, regression, ensembles, and physics-based methods. This showcases a multifaceted approach to address the intricate challenges associated with solar energy predictions. The integration of Satellite Imagery, weather predictions, and historical data further augments precision in forecasting. In assessing forecasting models, our study describes various error metrics. While the existing literature discusses the importance of metrics, our emphasis lies on the significance of standardized datasets and benchmark methods to ensure accurate evaluations and facilitate meaningful comparisons with naive forecasts. This study stands as a significant advancement in the field, fostering the development of accurate models crucial for effective renewable energy planning and emphasizing the imperative for standardization, thus addressing key gaps in the existing research landscape. Full article
(This article belongs to the Topic Solar Forecasting and Smart Photovoltaic Systems)
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18 pages, 1550 KiB  
Article
Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems
by Yunzhu Gao, Jun Wang, Lin Guo and Hong Peng
Sustainability 2024, 16(4), 1709; https://doi.org/10.3390/su16041709 - 19 Feb 2024
Viewed by 685
Abstract
To ensure high-quality electricity, improve the dependability of power systems, reduce carbon emissions, and promote the sustainable development of clean energy, short-term photovoltaic (PV) power prediction is crucial. However, PV power is highly stochastic and volatile, making accurate predictions of PV power very [...] Read more.
To ensure high-quality electricity, improve the dependability of power systems, reduce carbon emissions, and promote the sustainable development of clean energy, short-term photovoltaic (PV) power prediction is crucial. However, PV power is highly stochastic and volatile, making accurate predictions of PV power very difficult. To address this challenging prediction problem, in this paper, a novel method to predict the short-term PV power using a nonlinear spiking neural P system-based ESN model has been proposed. First, we combine a nonlinear spiking neural P (NSNP) system with a neural-like computational model, enabling it to effectively capture the complex nonlinear trends in PV sequences. Furthermore, an NSNP system featuring a layer is designed. Input weights and NSNP reservoir weights are randomly initialized in the proposed model, while the output weights are trained by the Ridge Regression algorithm, which is motivated by the learning mechanism of echo state networks (ESNs), providing the model with an adaptability to complex nonlinear trends in PV sequences and granting it greater flexibility. Three case studies are conducted on real datasets from Alice Springs, Australia, comparing the proposed model with 11 baseline models. The outcomes of the experiments exhibit that the model performs well in tasks of PV power prediction. Full article
(This article belongs to the Topic Solar Forecasting and Smart Photovoltaic Systems)
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18 pages, 31253 KiB  
Article
Photovoltaic Power Forecast Using Deep Learning Techniques with Hyperparameters Based on Bayesian Optimization: A Case Study in the Galapagos Islands
by Richard Guanoluisa, Diego Arcos-Aviles, Marco Flores-Calero, Wilmar Martinez and Francesc Guinjoan
Sustainability 2023, 15(16), 12151; https://doi.org/10.3390/su151612151 - 9 Aug 2023
Cited by 4 | Viewed by 1842
Abstract
Hydropower systems are the basis of electricity power generation in Ecuador. However, some isolated areas in the Amazon and Galapagos Islands are not connected to the National Interconnected System. Therefore, isolated generation systems based on renewable energy sources (RES) emerge as a solution [...] Read more.
Hydropower systems are the basis of electricity power generation in Ecuador. However, some isolated areas in the Amazon and Galapagos Islands are not connected to the National Interconnected System. Therefore, isolated generation systems based on renewable energy sources (RES) emerge as a solution to increase electricity coverage in these areas. An extraordinary case occurs in the Galapagos Islands due to their biodiversity in flora and fauna, where the primary energy source comes from fossil fuels despite their significant amount of solar resources. Therefore, RES use, especially photovoltaic (PV) and wind power, is essential to cover the required load demand without negatively affecting the islands’ biodiversity. In this regard, the design and installation planning of PV systems require perfect knowledge of the amount of energy available at a given location, where power forecasting plays a fundamental role. Therefore, this paper presents the design and comparison of different deep learning techniques: long-short-term memory (LSTM), LSTM Projected, Bidirectional LSTM, Gated Recurrent Units, Convolutional Neural Networks, and hybrid models to forecast photovoltaic power generation in the Galapagos Islands of Ecuador. The proposed approach uses an optimized hyperparameter-based Bayesian optimization algorithm to reduce the forecast error and training time. The results demonstrate the accurate performance of all the methods by achieving a low-error short-term prediction, an excellent correlation of over 99%, and minimizing the training time. Full article
(This article belongs to the Topic Solar Forecasting and Smart Photovoltaic Systems)
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17 pages, 3143 KiB  
Article
An Efficient Hybrid Particle Swarm and Gradient Descent Method for the Estimation of the Hosting Capacity of Photovoltaics by Distribution Networks
by Esau Zulu, Ryoichi Hara and Hiroyuki Kita
Energies 2023, 16(13), 5207; https://doi.org/10.3390/en16135207 - 6 Jul 2023
Cited by 3 | Viewed by 1617
Abstract
With many distribution networks adopting photovoltaic (PV) generation systems in their networks, there is a significant risk of over-voltages, reverse power flow, line congestion, and increased harmonics. Therefore, there is a need to estimate the amount of PV that can be injected into [...] Read more.
With many distribution networks adopting photovoltaic (PV) generation systems in their networks, there is a significant risk of over-voltages, reverse power flow, line congestion, and increased harmonics. Therefore, there is a need to estimate the amount of PV that can be injected into the distribution network without pushing the network towards these threats. The largest amount of PV a distribution system can accommodate is the PV hosting capacity (PVHC). The paper proposes an efficient method for estimating the PVHC of distribution networks that combines particle swarm optimization (PSO) and the gradient descent algorithm (GD). PSO has a powerful exploration of the solution space but poor exploitation of the local search. On the other hand, GD has great exploitation of local search to obtain local optima but needs better global search capabilities. The proposed method aims to harness the advantages of both PSO and GD while alleviating the ills of each. The numerical case studies show that the proposed method is more efficient, stable, and superior to the other meta-heuristic approaches. Full article
(This article belongs to the Topic Solar Forecasting and Smart Photovoltaic Systems)
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13 pages, 4112 KiB  
Article
Evaluation of High Resolution WRF Solar
by Jayesh Thaker and Robert Höller
Energies 2023, 16(8), 3518; https://doi.org/10.3390/en16083518 - 18 Apr 2023
Cited by 1 | Viewed by 1751
Abstract
The amount of solar irradiation that reaches Earth’s surface is a key quantity of solar energy research and is difficult to predict, because it is directly affected by the changing constituents of the atmosphere. The numerical weather prediction (NWP) model performs computational simulations [...] Read more.
The amount of solar irradiation that reaches Earth’s surface is a key quantity of solar energy research and is difficult to predict, because it is directly affected by the changing constituents of the atmosphere. The numerical weather prediction (NWP) model performs computational simulations of the evolution of the entire atmosphere to forecast the future state of the atmosphere based on the current state. The Weather Research and Forecasting (WRF) model is a mesoscale NWP. WRF solar is an augmented feature of WRF, which has been improved and configured specifically for solar energy applications. The aim of this paper is to evaluate the performance of the high resolution WRF solar model and compare the results with the low resolution WRF solar and Global Forecasting System (GFS) models. We investigate the performance of WRF solar for a high-resolution spatial domain of resolution 1 × 1 km and compare the results with a 3 × 3 km domain and GFS. The results show error metrices rMAE {23.14%, 24.51%, 27.75%} and rRMSE {35.69%, 36.04%, 37.32%} for high resolution WRF solar, coarse domain WRF solar and GFS, respectively. This confirms that high resolution WRF solar performs better than coarse domain and in general. WRF solar demonstrates statistically significant improvement over GFS. Full article
(This article belongs to the Topic Solar Forecasting and Smart Photovoltaic Systems)
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18 pages, 8163 KiB  
Article
LightGBM-Integrated PV Power Prediction Based on Multi-Resolution Similarity
by Yan Peng, Shichen Wang, Wenjin Chen, Junchao Ma, Chenxu Wang and Jingwei Chen
Processes 2023, 11(4), 1141; https://doi.org/10.3390/pr11041141 - 7 Apr 2023
Cited by 5 | Viewed by 1083
Abstract
Improving the accuracy of PV power prediction is conducive to PV participation in economic dispatch and power market transactions in the distribution network, as well as safe dispatch and operation of the grid. Considering that the selection of highly correlated historical data can [...] Read more.
Improving the accuracy of PV power prediction is conducive to PV participation in economic dispatch and power market transactions in the distribution network, as well as safe dispatch and operation of the grid. Considering that the selection of highly correlated historical data can improve the accuracy of PV power prediction, this study proposes an integrated PV power prediction method based on a multi-resolution similarity consideration that considers both trend similarity and detail similarity. Firstly, using irradiance as the similarity variable, similar-days were selected using grey correlation analysis to form a set of similar data to control the similarity, with the overall trend of the day to be predicted at a macro level. Using irradiance to calculate the similarity at each specific point in time via Euclidean distance, similar-times were identified to form another set of similar data to consider the degree of similarity in detail. The above approach enables the selection of similarity data for both resolutions. Then, a 1DCNN-LSTM prediction model that considers the feature correlation of different variables and the temporal dependence of a single variable was proposed. Three important features were selected by a random forest model as inputs to the prediction model, and two similar data training models with different resolutions were used to generate a photovoltaic power prediction model based on similar-days and similar-times. Ultimately, the learning of the two predictions integrated with LightGBM compensate for each other, generating highly accurate predictions that combine the advantages of multi-resolution similarity considerations. Actual operation data of a PV power station was used for verification. The simulation results show that the prediction effect of ensemble learning was better than that of the single 1DCNN-LSTM model. The proposed method was compared with other commonly used PV power prediction models. In the data case of this study, it was found that the proposed method reduced the prediction error rate by 1.48%, 11.4%, and 6.45%, compared to the LSTM, CNN, and BP, respectively. Experiments show that model prediction results considering the selection of similar data at multiple resolutions can provide more extensive information to an ensemble learner and reduce the deviation in model predictions. Therefore, the proposed method can provide a reference for PV integration into the grid and participation in market-based electricity trading, which is of great significance. Full article
(This article belongs to the Topic Solar Forecasting and Smart Photovoltaic Systems)
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17 pages, 3268 KiB  
Article
Increasing the Resolution and Spectral Range of Measured Direct Irradiance Spectra for PV Applications
by Gabriel López, Christian A. Gueymard, Jesús Polo, Joaquín Alonso-Montesinos, Aitor Marzo, Nuria Martín-Chivelet, Pablo Ferrada, Martha Isabel Escalona-Llaguno and Francisco Javier Batlles
Remote Sens. 2023, 15(6), 1675; https://doi.org/10.3390/rs15061675 - 20 Mar 2023
Cited by 1 | Viewed by 1315
Abstract
The spectral distribution of the solar irradiance incident on photovoltaic (PV) modules is a key variable controlling their power production. It is required to properly simulate the production and performance of PV plants based on technologies with different spectral characteristics. Spectroradiometers can only [...] Read more.
The spectral distribution of the solar irradiance incident on photovoltaic (PV) modules is a key variable controlling their power production. It is required to properly simulate the production and performance of PV plants based on technologies with different spectral characteristics. Spectroradiometers can only sense the solar spectrum within a wavelength range that is usually too short compared to the actual spectral response of some PV technologies. In this work, a new methodology based on the Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) spectral code is proposed to extend the spectral range of measured direct irradiance spectra and to increase the spectral resolution of such experimental measurements. Satisfactory results were obtained for both clear and hazy sky conditions at a radiometric station in southern Spain. This approach constitutes the starting point of a general methodology to obtain the instantaneous spectral irradiance incident on the plane of array of PV modules and its temporal variations, while evaluating the magnitude and variability of the abundance of atmospheric constituents with the most impact on surface irradiance, most particularly aerosols and water vapor. Full article
(This article belongs to the Topic Solar Forecasting and Smart Photovoltaic Systems)
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