Wei, C.-C.; Yang, Y.-C. A Global Solar Radiation Forecasting System Using Combined Supervised and Unsupervised Learning Models. Energies2023, 16, 7693.
Wei, C.-C.; Yang, Y.-C. A Global Solar Radiation Forecasting System Using Combined Supervised and Unsupervised Learning Models. Energies 2023, 16, 7693.
Wei, C.-C.; Yang, Y.-C. A Global Solar Radiation Forecasting System Using Combined Supervised and Unsupervised Learning Models. Energies2023, 16, 7693.
Wei, C.-C.; Yang, Y.-C. A Global Solar Radiation Forecasting System Using Combined Supervised and Unsupervised Learning Models. Energies 2023, 16, 7693.
Abstract
One of the most important sources of energy is the sun. Taiwan is located at north 22-25° latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location which can obtain sustainable and stable solar resources. This study takes research on the forecast of solar radiation to maximize the benefits of solar power generation, and develops methods that can predict the future solar radiation pattern to help reduce the costs of solar power generation. This study builds supervised machine learning models, known as deep neural network (DNN) and long short-term memory neural network (LSTM). The hybrid supervised and unsupervised model, namely cluster-based artificial neural network (k-means clustering and fuzzy C-means clustering-based models), was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time updated solar radiation forecast system capable of predicting the next 12 hours. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models have their advantages and disadvantages in predicting different future times. Therefore, the hybrid prediction system can predict future solar radiation more accurately than a single model.
Keywords
solar radiation; prediction; cluster algorithm; neural network
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
