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

Abstract

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.

Artificial intelligence applications in solar energy

“…Knowledge about global solar radiation serves as the foundation for various solar energy applications and is critical for environmental and economic problems. On the other hand, precise global solar insolation statistics are sometimes problematic or complex because solar radiation is subject to change, and observations are not always readily accessible [267], [268]. On the other hand, models that are based on machine learning can solve very nonlinear problems [269].…”

Optimal Energy Tracking in a Solar Power System Utilizing Synthetic Neural Network

“…Each neuron performs a calculation locally, the result of which is then transmitted to the avalous neurons. The proven potential of machine learning techniques in pattern matching and computer vision has led researchers to use these techniques to predict the efficiency of solar cells [15][16][17][18][19][20][21][22]. The research work performed to date demonstrates the applications of these techniques for optimal efficiency prediction, best-suited design and materials for fabricating dye-sensitive solar cells (DSSCs).…”

Solar Irradiance Forecasting with Natural Language Processing of Cloud Observations and Interpretation of Results with Modified Shapley Additive Explanations

“…These models can analyse multiple input factors. In this case, models based on artificial neural networks [5,10] and ensemble models [3,11,12] can be distinguished. Deep neural network models have been implemented to model and forecast solar irradiance data with the use of meteorological and geographic parameters [13][14][15].…”