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

Abstract

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.

Bio-Inspired Design of Future Solar Power Systems for Smart Grid Applications

“…Alkahtani [18] stated that bio-inspired models such as ANN (artificial neural networks) would become widely used in solar radiation prediction. It examines the performance of different ANN models in solar radiation prediction, such as: convolution neural network (CNN); long short-term memory (LSTM), and the hybrid model (CNN-LSTM).…”

“…The hybrid model was found to enhance the prediction accuracy in comparison to the CNN, and the LSTM. The study in [18] focuses on the prediction of meteorological factors affecting solar power output. Further work is required as the proposed solution is location dependent.…”

“…These capabilities are examined in the context of the next generation smart grid. The discussion in [17], [18], [19], [20], and [21] notes that the bio-inspired ANN has been significantly used in the renewable solar energy systems. The areas of use have been in power output, solar radiation prediction and enhancing system performance.…”

“…It focuses on the manufacturing of solar panels and demonstrates the feasibility of producing flexible solar panels. This review shows that bio-inspired models find applications in renewable solar energy systems, with respect to insolation prediction and management related configuration [17][18][19][20][21]. The use of bio-inspired models to determine solar panel geometry is yet to significantly benefit from bio-inspired models as seen in [22][23][24][25].…”

“…The consideration of biological inspired models in this regard is novel. This is because bio-inspired models that have been considered as seen in [17][18][19][20][21] have been mostly leveraged in a computational perspective. The use of a bioinspired models from a geometrical viewpoint is yet to receive sufficient research consideration.…”