A Solar Irradiance Prediction Model with Recurrent Neural Networks and Computer Graphics Methods

This paper presents a computer simulation model for predicting solar irradiance in a three-dimensional (3D) environment. Solar irradiance prediction is critical for solar energy systems and related fields. Machine learning techniques, such as recurrent neural networks (RNNs), are employed for more accurate predictions. Integrating a 3D environmental simulation with the RNN models achieves accurate predictions with reasonable resolutions. The training model uses selected astronomical and atmospheric factors to train the RNN models. The proposed method allows the user to obtain the corresponding solar irradiance prediction values for arbitrary periods. Astronomical and atmospheric factors affect solar irradiance; hence, data from the Korea Meteorological Administration are used for training. The RNNs, including the long short-term memory (LSTM) and gated recurrent unit methods, are employed for the prediction. The LSTM layers outperformed other configurations, accurately predicting zero irradiation values. A set of solar irradiance models is presented using RNNs by configuring their layers, and the layout consisting of four LSTM layers performed best. This layout achieved reasonable error bounds, with relatively good root mean squared error and mean absolute error values. A computer graphics-based solar irradiance prediction model is proposed based on this prediction model, incorporating simulations of the surrounding environment. A case study is presented with surrounding buildings to analyze the solar irradiance over the year with a one-hour forecasting horizon to demonstrate its feasibility. Moreover, we plan to improve the results with other neural network models, such as the fuzzy- embedded RNN.