Solar Irradiance Forecasting Using an Artificial Intelligence Model

Ray, Pravat Kumar; Bharatee, Anindya; Puhan, Pratap Sekhar; Sahoo, Swaroop

doi:10.1109/iciccsp53532.2022.9862494

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…(DL), and ANNs [32,33]. Applications of AI may benefit greatly by identifying the DL hyperparameters that can be used to narrow the search space and then afterwards implementing hyperparameter optimization [34,35].…”

Section: Methodsmentioning

confidence: 99%

“…As a result of developments in technology over the last several decades [29][30][31], the use of AI has become more widespread across almost all technical fields. Predictions of solar radiation data were made using a variety of artificial intelligence techniques, including support vector regression (SVR), kernel nearest neighbors (k-NNs), deep learning (DL), and ANNs [32,33]. Applications of AI may benefit greatly by identifying the DL hyperparameters that can be used to narrow the search space and then afterwards implementing hyperparameter optimization [34,35].…”

Section: Literature Reviewmentioning

confidence: 99%

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Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems

2023

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Solar power is an excellent alternative power source that can significantly cut our dependency on nonrenewable and destructive fossil fuels. Solar radiation (SR) can be predicted with great precision, and it may be possible to drastically minimize the impact cost associated with the development of solar energy. To successfully implement solar power, all projects using solar energy must have access to reliable sun radiation data. However, the deployment, administration, and performance of photovoltaic or thermal systems may be severely impacted by the lack of access to and the ambiguity of this data. Methods for estimating and predicting solar radiation can help solve these problems. Prediction techniques can be put to use in the real world to, for example, keep the power grid functioning smoothly and ensure that the supply of electricity exactly matches the demand at all times. Recently developed forecasting methods include the deep learning convolutional neural networks combined with long short-term memory (CNN-LSTM) model. This study provides a comprehensive examination of meteorological data, along with the CNN-LSTM methods, in order to design and train the most accurate SR forecasting artificial neural network model possible. Weather data was collected from a NASA meteorological station that included details such as the current temperature, the relative humidity, and the speed of the wind. This research revealed that SR is highly correlated with both temperature and radiation. Furthermore, the findings demonstrated that the CNN-LSTM algorithm outperformed the other algorithm-trained models, as evidenced by the performance score of the respective models, with a maximum coefficient determination (R²) > 95% and a minimum mean square error (MSE) of 0.000987 at the testing step. In comparison with different existing artificial intelligence models, the CNN-LSTM model outperformed the other models. These scenarios demonstrated that a basic implementation of CNN-LSTM can be used to supplement conventional methods for predicting SR, provide possibilities to monitor radiation at a low cost, and encourage the adoption of data-driven management.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems

2023

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Ensemble Machine-Learning Models for Accurate Prediction of Solar Irradiation in Bangladesh

et al. 2023

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Improved irradiance forecasting ensures precise solar power generation forecasts, resulting in smoother operation of the distribution grid. Empirical models are used to estimate irradiation using a wide range of data and specific national or regional parameters. In contrast, algorithms based on Artificial Intelligence (AI) are becoming increasingly popular and effective for estimating solar irradiance. Although there has been significant development in this area elsewhere, employing an AI model to investigate irradiance in Bangladesh is limited. This research forecasts solar radiation in Bangladesh using ensemble machine-learning models. The meteorological data collected from 32 stations contain maximum temperature, minimum temperature, total rain, humidity, sunshine, wind speed, cloud coverage, and irradiance. Ensemble machine-learning algorithms including Adaboost regression (ABR), gradient-boosting regression (GBR), random forest regression (RFR), and bagging regression (BR) are developed to predict solar irradiance. With the default parameters, the GBR provides the best performance as it has the lowest standard deviation of errors. Then, the important hyperparameters of the GRB are tuned with the grid-search algorithms to further improve the prediction accuracy. On the testing dataset, the optimized GBR has the highest coefficient of determination (R2) performance, with a value of 0.9995. The same approach also has the lowest root mean squared error (0.0007), mean absolute percentage error (0.0052), and mean squared logarithmic error (0.0001), implying superior performance. The absolute error of the prediction lies within a narrow range, indicating good performance. Overall, ensemble machine-learning models are an effective method for forecasting irradiance in Bangladesh. They can attain high accuracy and robustness and give significant information for the assessment of solar energy resources.

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Artificial Intelligent-Based Techniques in Solar Radiation Applications

Gidom

2024

Solar Radiation - Enabling Technologies, Recent Innovations, and Advancements for Energy Transition

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The evolving smart grid emerges as a response to the challenges posed by the unreliability and inefficiency of the traditional electric grid. This transformation is crucial due to issues like the aging infrastructure and the intermittency of renewable energy sources, particularly solar radiation. The smart grid is anticipated to facilitate two-way power flows and introduce innovative technologies. This study explores the impact of smart grid technologies, particularly those supported by artificial intelligence (AI), on-demand load, future energy consumption, and energy management services. The focus is on AI-based systems applied in solar energy applications, aiming to enhance efficiency and reduce costs. Various AI techniques, including neural network methods, are examined for their role in addressing challenges such as forecasting, fault diagnosis, and control in solar radiation applications. The research introduces and compares three AI models—gated recurrent unit (GRU), artificial neural network (ANN), and long short-term memory model (LSTM)—for predicting solar irradiance. The outcomes emphasize the versatility of AI, not only in solar systems but also in extending its applications to other renewable energy systems like wind and diverse fields such as security, reliability, and stability.

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Solar Irradiance Forecasting Using an Artificial Intelligence Model

Cited by 3 publications

References 10 publications

Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems

Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems

Ensemble Machine-Learning Models for Accurate Prediction of Solar Irradiation in Bangladesh

Artificial Intelligent-Based Techniques in Solar Radiation Applications

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