Accurate solar system model
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Accurate Solar System Model: Insights from Recent Research
Optimization Algorithms for Solar Cell and PV Module Modeling
Coyote Optimization Algorithm (COA) for PV Models
The Coyote Optimization Algorithm (COA) has been effectively applied to estimate parameters in various solar cell and PV module models, including single diode, double diode, and three diode models. The COA is favored for its simplicity and balance between exploration and exploitation phases, requiring only two control parameters. This algorithm has demonstrated superior accuracy and reliability in parameter extraction under different operating conditions, achieving low Root Mean Square Error (RMSE) values, thus validating its effectiveness in creating precise PV models1.
Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO)
Another innovative approach is the Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO), which incorporates fractional chaos maps to enhance accuracy and reliability. This method has been validated against multiple experimental datasets, showing high accuracy and consistency in modeling single, double, and three diode PV models. The FC-EPSO algorithm is noted for its fast convergence rate and short execution time, making it a robust tool for real-time PV system modeling4.
Deep Learning Models for Solar Irradiance Forecasting
Hybrid Deep Neural Models
Accurate solar irradiance forecasting is crucial for integrating photovoltaic systems into power networks. The WPD-CNN-LSTM-MLP model, a hybrid deep neural network, combines wavelet packet decomposition, convolutional neural networks, long short-term memory networks, and multi-layer perceptron networks. This model uses multi-variable inputs, including temperature, relative humidity, and wind speed, to achieve superior forecasting accuracy. Comparative studies have shown that this hybrid model outperforms traditional methods, making it a powerful tool for hourly solar irradiance forecasting2.
Comprehensive Review of Deep Learning Models
A comprehensive review of deep learning models for solar irradiance forecasting highlights the superiority of these models over traditional statistical and machine learning approaches. Models such as LSTM, deep belief networks, and convolutional neural networks have shown significant improvements in prediction accuracy. Hybrid models, particularly those combining CNN and LSTM, have demonstrated enhanced performance, making them highly recommended for solar forecasting applications5.
Parameterization and Time Resolution in PV Models
Enhanced Parameterization Approach
A novel parameterization approach has been proposed to improve the accuracy of PV models by accurately estimating circuit parameters using data from commercial module datasheets. This method simplifies the modeling process and enhances simulation accuracy, making it a valuable tool for designing efficient PV systems3.
Impact of Time Resolution
The effect of time resolution on the modeling accuracy of small-scale solar energy systems has been investigated, revealing that higher time resolutions (e.g., 5-minute intervals) significantly improve the accuracy of battery behavior predictions and energy consumption estimates. This finding suggests that increasing time resolution in energy models can lead to more accurate and cost-effective solar energy system designs6.
Machine Learning for Solar Radiation Forecasting
Site-Adaptation and Forecasting
Machine learning algorithms 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 shown that machine learning models for site-adaptation perform significantly better than traditional methods, making them essential for optimal solar energy system management9.
Temperature-Based Models
New temperature-based models have been developed to estimate global solar radiation, providing an alternative to sunshine-based models. These models have been validated using extensive datasets and have shown superior performance, especially in locations where sunshine data is unavailable. The accurate estimation of global solar radiation using these models can significantly enhance the design and evaluation of solar applications10.
Conclusion
Recent advancements in optimization algorithms, deep learning models, and parameterization approaches have significantly improved the accuracy of solar system models. These innovations are crucial for the efficient integration and management of solar energy systems, ensuring reliable performance and optimal energy utilization.
Sources and full results
Most relevant research papers on this topic
Coyote 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 CitedA 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 CitedFractional 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 CitedDeep 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 CitedThe effect of time resolution on the modelling of domestic solar energy systems
Increasing time resolution from hourly to 5-min in solar energy models improves accuracy in battery behavior and predicted self-consumption, highlighting the benefits of battery storage.
A novel SGD-DLSTM-based efficient model for solar power generation forecasting system
The SGD-DLSTM methodology provides more accurate solar power generation forecasting with deviation analysis, achieving 97.25% accuracy compared to prevailing research methods.
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 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 CitedNew Temperature-based Models for Predicting Global Solar Radiation
New temperature-based models provide more accurate predictions of global solar radiation than sunshine-based models, especially at coastal locations.
Highly Cited