Solar Forecasting and Smart Photovoltaic Systems

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Processes processes	2.8	5.1	2013	14.9 Days	CHF 2400	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit
Solar solar	-	-	2021	23.4 Days	CHF 1000	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit

31 pages, 12875 KiB

Open AccessArticle

Multi-Timescale Validation of Satellite-Derived Global Horizontal Irradiance in Côte d’Ivoire

by Pierre-Claver Konin Kakou, Dungall Laouali, Boko Aka, Janet Appiah Osei, Nicaise Franck Kassi Ette and Georg Frey

Remote Sens. 2025, 17(6), 998; https://doi.org/10.3390/rs17060998 - 12 Mar 2025

Viewed by 525

Abstract

Accurate solar radiation data are crucial for solar energy applications, yet ground-based measurements are limited in many regions. Satellite-derived and reanalysis products offer an alternative, but their accuracy varies across spatial and temporal scales. This study evaluated the performance of four widely used [...] Read more.

Accurate solar radiation data are crucial for solar energy applications, yet ground-based measurements are limited in many regions. Satellite-derived and reanalysis products offer an alternative, but their accuracy varies across spatial and temporal scales. This study evaluated the performance of four widely used GHI products—CAMS, SARAH-3, ERA5 and MERRA-2—against ground measurements at hourly, daily (summed from hourly) and monthly (averaged from daily) timescales. The analysis also examined how temporal aggregation influenced error characteristics using correlation coefficients, the rMBD, the rRMSD and the combined performance index (CPI). At an hourly scale under clear-sky conditions, satellite products outperformed reanalysis products, with

r \approx 1

and

R^{2} \approx 0.9

and the rMBD, rRMSD and CPI ranging from 0.1%, 11.4% and 11.8% to −14.7%, 33.3% and 75.1% for CAMS; 0.2%, 11.4% and 10.9% to 13.5%, 22.4% and 120.7% for SARAH-3; −0.2%, 21.6% and 23.8% to 21.5%, 40.9% and 128.8% for MERRA-2; and 0.8%, 14.6% and 16.3% to 22%, 48.2% and 88.3% for ERA5. Under cloudy conditions, all products overestimated GHI, with the rMBD reaching up to 39.7% (SARAH-3), 35.9% (CAMS), 22.9% (MERRA-2) and 28% (ERA5), while the rRMSD exceeded 40% for all. Overcast conditions yielded the poorest performance, with the rMBD ranging from 45.8% to 124.6% and the CPI exceeding 800% in some cases. From the hourly to daily and monthly datasets, aggregation reduced errors for reanalysis products by 5.5% and up to 12.4%, respectively, in clear-sky conditions, but for satellite-based products, deviations slightly increased up to 3.1% for the monthly dataset. Under all-sky conditions, all products showed reductions up to 23%. These results highlight the significant challenges in estimating GHI due to limited knowledge of aerosol and cloud dynamics in the region. They emphasize the need for improved parameterization in models and dedicated measurement campaigns to enhance satellite and reanalysis product accuracy in West Africa. Full article

(This article belongs to the Topic Solar Forecasting and Smart Photovoltaic Systems)

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16 pages, 1985 KiB

Open AccessArticle

Medium- and Long-Term Distributed Photovoltaic Power Prediction Based on Multiple Time Series Feature and Multiple-Model Fusion

by Xinting Yang, Shengyong Ye, Keteng Jiang, Chongbo Sun, Zongxiang Lu, Liyang Liu, Yuqi Han and Bin Zhang

Sustainability 2024, 16(20), 8806; https://doi.org/10.3390/su16208806 - 11 Oct 2024

Viewed by 1322

Abstract

Distributed photovoltaic power stations have advantages such as local direct power supply and reduced transmission energy consumption, and whose demands are constantly being developed. Conducting research on medium- and long-term distributed photovoltaic prediction will have significant value for applications such as the electricity [...] Read more.

Distributed photovoltaic power stations have advantages such as local direct power supply and reduced transmission energy consumption, and whose demands are constantly being developed. Conducting research on medium- and long-term distributed photovoltaic prediction will have significant value for applications such as the electricity trade market, power grid operation, and the planning of new power stations. Due to characteristics such as long time dependence, disperse power stations, and strong randomness, making accurate and stable predictions becomes very difficult. In this research, we propose a multiple time series feature and multiple-model fusion-based ensemble learning model for medium- and long-term distributed photovoltaic power prediction (M2E-DPV). Considering the wave influence and the differences in distributions in different areas of photovoltaic power, multiple feature combinations are designed to increase feature expression ability and adaptability. Based on the boost ensemble learning model, trained on a single model of different time scale features, the optimal scoring strategy is used for multiple model fusion in the rolling prediction process, and finally, time-segmented probabilistic correction is performed. The experiment results show the effectiveness of the M2E-DPV under multiple feature combinations and multi-model fusion strategies. The average MAPE, R², and ACC indicators are 0.15, 0.96, and 0.91, respectively. Compared with other methods, there is a significant improvement, indicating that the prediction ability of the model framework proposed in this paper is advanced and robust. Full article

(This article belongs to the Topic Solar Forecasting and Smart Photovoltaic Systems)

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23 pages, 14896 KiB

Open AccessArticle

Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting

by Shahad Mohammed Radhi, Sadeq D. Al-Majidi, Maysam F. Abbod and Hamed S. Al-Raweshidy

Energies 2024, 17(17), 4301; https://doi.org/10.3390/en17174301 - 28 Aug 2024

Cited by 4 | Viewed by 1452

Abstract

A photovoltaic (PV) power forecasting prediction is a crucial stage to utilize the stability, quality, and management of a hybrid power grid due to its dependency on weather conditions. In this paper, a short-term PV forecasting prediction model based on actual operational data [...] Read more.

A photovoltaic (PV) power forecasting prediction is a crucial stage to utilize the stability, quality, and management of a hybrid power grid due to its dependency on weather conditions. In this paper, a short-term PV forecasting prediction model based on actual operational data collected from the PV experimental prototype installed at the engineering college of Misan University in Iraq is designed using various machine learning techniques. The collected data are initially classified into three diverse groups of atmosphere conditions—sunny, cloudy, and rainy meteorological cases—for various seasons. The data are taken for 3 min intervals to monitor the swift variations in PV power generation caused by atmospheric changes such as cloud movement or sudden changes in sunlight intensity. Then, an artificial neural network (ANN) technique is used based on the gray wolf optimization (GWO) and genetic algorithm (GA) as learning methods to enhance the prediction of PV energy by optimizing the number of hidden layers and neurons of the ANN model. The Python approach is used to design the forecasting prediction models based on four fitness functions: R², MAE, RMSE, and MSE. The results suggest that the ANN model based on the GA algorithm accommodates the most accurate PV generation pattern in three different climatic condition tests, outperforming the conventional ANN and GWO-ANN forecasting models, as evidenced by the highest Pearson correlation coefficient values of 0.9574, 0.9347, and 0.8965 under sunny, cloudy, and rainy conditions, respectively. Full article

(This article belongs to the Topic Solar Forecasting and Smart Photovoltaic Systems)

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17 pages, 3442 KiB

Open AccessArticle

Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation

by Yiling Fan, Zhuang Ma, Wanwei Tang, Jing Liang and Pengfei Xu

Energies 2024, 17(14), 3435; https://doi.org/10.3390/en17143435 - 12 Jul 2024

Cited by 12 | Viewed by 2517

Abstract

Due to the inherent intermittency, variability, and randomness, photovoltaic (PV) power generation faces significant challenges in energy grid integration. To address these challenges, current research mainly focuses on developing more efficient energy management systems and prediction technologies. Through optimizing scheduling and integration in [...] Read more.

Due to the inherent intermittency, variability, and randomness, photovoltaic (PV) power generation faces significant challenges in energy grid integration. To address these challenges, current research mainly focuses on developing more efficient energy management systems and prediction technologies. Through optimizing scheduling and integration in PV power generation, the stability and reliability of the power grid can be further improved. In this study, a new prediction model is introduced that combines the strengths of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms, so we call this algorithm CNN-LSTM-Attention (CLA). In addition, the Crested Porcupine Optimizer (CPO) algorithm is utilized to solve the short-term prediction problem in photovoltaic power generation. This model is abbreviated as CPO-CLA. This is the first time that the CPO algorithm has been introduced into the LSTM algorithm for parameter optimization. To effectively capture univariate and multivariate time series patterns, multiple relevant and target variables prediction patterns (MRTPPs) are employed in the CPO-CLA model. The results show that the CPO-CLA model is superior to traditional methods and recent popular models in terms of prediction accuracy and stability, especially in the 13 h timestep. The integration of attention mechanisms enables the model to adaptively focus on the most relevant historical data for future power prediction. The CPO algorithm further optimizes the LSTM network parameters, which ensures the robust generalization ability of the model. The research results are of great significance for energy generation scheduling and establishing trust in the energy market. Ultimately, it will help integrate renewable energy into the grid more reliably and efficiently. Full article

(This article belongs to the Topic Solar Forecasting and Smart Photovoltaic Systems)

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19 pages, 16093 KiB

Open AccessFeature PaperArticle

Addressing Data Scarcity in Solar Energy Prediction with Machine Learning and Augmentation Techniques

by Aleksandr Gevorgian, Giovanni Pernigotto and Andrea Gasparella

Energies 2024, 17(14), 3365; https://doi.org/10.3390/en17143365 - 9 Jul 2024

Cited by 2 | Viewed by 1331

Abstract

The accurate prediction of global horizontal irradiance (GHI) is crucial for optimizing solar power generation systems, particularly in mountainous areas with complex topography and unique microclimates. These regions face significant challenges due to limited reliable data and the dynamic nature of local weather [...] Read more.

The accurate prediction of global horizontal irradiance (GHI) is crucial for optimizing solar power generation systems, particularly in mountainous areas with complex topography and unique microclimates. These regions face significant challenges due to limited reliable data and the dynamic nature of local weather conditions, which complicate accurate GHI measurement. The scarcity of precise data impedes the development of reliable solar energy prediction models, impacting both economic and environmental outcomes. To address these data scarcity challenges in solar energy prediction, this paper focuses on various locations in Europe and Asia Minor, predominantly in mountainous regions. Advanced machine learning techniques, including random forest (RF) and extreme gradient boosting (XGBoost) regressors, are employed to effectively predict GHI. Additionally, optimizing training data distribution based on cloud opacity values and integrating synthetic data significantly enhance predictive accuracy, with R² scores ranging from 0.91 to 0.97 across multiple locations. Furthermore, substantial reductions in root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE) underscore the improved reliability of the predictions. Future research should refine synthetic data generation, optimize additional meteorological and environmental parameter integration, extend methodology to new regions, and test for predicting global tilted irradiance (GTI). The studies should expand training data considerations beyond cloud opacity, incorporating sky cover and sunshine duration to enhance prediction accuracy and reliability. Full article

(This article belongs to the Topic Solar Forecasting and Smart Photovoltaic Systems)

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37 pages, 2630 KiB

Open AccessReview

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 20 | Viewed by 9395

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

Open AccessArticle

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

Cited by 4 | Viewed by 1645

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

Open AccessArticle

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 12 | Viewed by 3010

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

Open AccessArticle

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 8 | Viewed by 2558

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

Open AccessArticle

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 9 | Viewed by 3056

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

Open AccessArticle

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 10 | Viewed by 1837

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

Open AccessArticle

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 2 | Viewed by 2124

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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