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Accurate Solar System Models: Enhancing Precision in Photovoltaic and Solar Irradiance Forecasting
Introduction to Accurate Solar System Models
Accurate modeling of solar systems is crucial for optimizing the performance and efficiency of photovoltaic (PV) power systems. This involves precise estimation of solar cell parameters and accurate forecasting of solar irradiance. Recent advancements in parameter estimation techniques and deep learning models have significantly improved the accuracy of these models.
Parameter Estimation in Photovoltaic Models
Single Diode Equivalent Circuit
A common approach to modeling solar cells is using a single diode equivalent circuit. However, the accuracy of this model can be compromised due to assumptions made about circuit parameters based on limited commercial datasheet information. A new parameterization approach has been proposed to enhance the accuracy of PV models by accurately estimating circuit parameters using data from commercial modules. This method simplifies the modeling process and improves simulation accuracy for various types of solar modules, including mono-crystalline, multi-crystalline, and thin-film1.
Optimization Algorithms for Parameter Extraction
The Coyote Optimization Algorithm (COA) has been applied to extract unknown parameters in different PV models, such as single diode, double diode, and three diode models. COA's simplicity and effective tracking characteristics make it a reliable choice for parameter estimation. Studies have shown that COA can achieve high accuracy with low Root Mean Square Error (RMSE) values, validating its superiority over other optimization algorithms2. Additionally, the Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO) has been introduced to enhance the accuracy and reliability of PV models by incorporating fractional chaos maps into the algorithm9.
Solar Irradiance Forecasting
Deep Learning Models
Accurate forecasting of solar irradiance is essential for efficient energy planning and management. Traditional models often fall short due to the complex and non-stationary nature of solar irradiance data. Deep learning models, such as Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and hybrid models, have been proposed to address these challenges. For instance, a novel hybrid model combining wavelet packet decomposition, CNN, LSTM, and multi-layer perceptron networks has shown superior prediction accuracy for hourly solar irradiance forecasting3. Comprehensive reviews have highlighted the effectiveness of deep learning models in improving prediction accuracy compared to traditional methods5.
High-Precision Forecasting Systems
High-precision forecasting models based on grid point value data analysis have been developed to simulate optimal PV power production. These models incorporate various climate variables, such as relative humidity, precipitation, and cloud cover, to achieve high correlation coefficients in different locations. The reliability of these models is further supported by their consistent performance across different datasets4.
Machine Learning for Site-Adaptation
Machine learning techniques have been employed to enhance the spatio-temporal resolution of solar irradiance data by integrating satellite and in-situ measurements. This approach, known as site-adaptation, improves the accuracy of solar radiation forecasting models. Studies have demonstrated that machine learning models for site-adaptation can outperform traditional methods by up to 38%10.
Conclusion
The advancements in parameter estimation techniques and deep learning models have significantly improved the accuracy of solar system models. Accurate PV models and precise solar irradiance forecasting are essential for optimizing the performance and efficiency of solar energy systems. The integration of optimization algorithms and machine learning techniques continues to push the boundaries of what is possible in solar system modeling, ensuring reliable and efficient energy production.
Sources and full results
Most relevant research papers on this topic
A Parameterization Approach for Enhancing PV Model Accuracy
This paper proposes a parameterization approach for PV models that improves accuracy and reduces implementation complexity, relying on commercial module datasheets for circuit parameter estimation.
Highly CitedCoyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules
The COA algorithm effectively estimates unknown parameters in various solar cell and PV module models, providing accurate and stable models for performance evaluation under various operating scenarios.
Highly CitedHybrid deep neural model for hourly solar irradiance forecasting
The WPD-CNN-LSTM-MLP deep learning model improves hourly solar irradiance forecasting accuracy compared to traditional models.
Highly CitedHigh-Precision Forecasting Model of Solar Irradiance Based on Grid Point Value Data Analysis for an Efficient Photovoltaic System
This study developed a high-precision solar irradiance forecasting model using grid point value data, which is reliable and accurate for predicting optimal photovoltaic power production in Japan.
Deep learning models for solar irradiance forecasting: A comprehensive review
Deep learning models, such as CNN-LSTM, significantly improve solar irradiance forecasting accuracy, enhancing solar photovoltaic plant modeling and planning.
Literature ReviewHighly CitedAn intelligent hybrid wavelet-adversarial deep model for accurate prediction of solar power generation
The proposed intelligent hybrid wavelet-adversarial deep model accurately predicts solar power generation using sub-harmonics and a modified dragonfly algorithm, outperforming other models and algorithms.
Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China
Machine learning models, particularly ANFIS and MARS, outperform empirical models in estimating daily global solar radiation from sunshine duration in China.
Literature ReviewHighly CitedProviding an accurate global model for monthly solar radiation forecasting using artificial intelligence based on air quality index and meteorological data of different cities worldwide
The adaptive neuro-fuzzy inference system is the most accurate and precise model for predicting global solar radiation, with statistical indexes showing more formidable accuracy and credibility compared to other models.
Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models’ parameters
The FC-EPSO algorithm accurately models solar PV modules, enhancing performance prediction and reducing deviations from experimental data.
Highly CitedMachine learning for site-adaptation and solar radiation forecasting
Machine learning algorithms can improve site-adaptation and solar radiation forecasting accuracy by combining satellite and in-situ data, resulting in up to 38% better performance than traditional methods.
Highly Cited