Solar Irradiance Forecasting Based on Deep Learning Methodologies and Multi-Site Data

Abstract

The ever-growing interest in and requirement for green energy have led to an increased focus on research related to forecasting solar irradiance recently. This study aims to develop forecast models based on deep learning (DL) methodologies and multiple-site data to predict the daily solar irradiance in two locations of India based on the daily solar radiation data obtained from NASA’s POWER project repository over 36 years (1983–2019). The forecast modeling of solar irradiance data is performed for extracting and learning the symmetry latent in data patterns and relationships by the machine learning models and utilizing it to predict future solar data. The goodness of fit and model performance are compared with rolling window evaluation using mean squared error, root-mean-square error and coefficient of determination (R2) for evaluation. The contributions of this study can be summarized as follows: (i) time series models based on deep learning methodologies were implemented to forecast the daily solar irradiance of two locations in India in consideration of the historical data collected by NASA; (ii) the models were developed on the basis of single-location univariate data as well as multiple-location data; (iii) the accuracy, performance and reliability of the model were investigated on the basis of standard performance evaluation metrics and rolling window evaluation; (iv) the feature importance of the nearby locations with respect to forecasting target location solar irradiance was analyzed and compared based on the solar irradiance data obtained from NASA over 36 years. The results indicate that the bidirectional long short-term memory (LSTM) and attention-based LSTM models can be used for forecasting daily solar irradiance data. According to the findings, the multiple-site data with solar irradiance historical data improve upon the forecast performance of single-location univariate solar data.

Analysis of Artificial Neural Networks for Forecasting Photovoltaic Energy Generation with Solar Irradiance

“…The meteorological variables of the Complete Input Set did not provide significant improvement in accuracy for the models evaluated, and ANNs with 30 and 60 neurons in the hidden layer(s) showed better performance than the models with 10 neurons, which, possibly, were not able to capture the variability of the data because they constitute more simple evaluated ANN structures. For future research, we plan to apply and analyze Deep Learning models [15,36] in short-term predictions.…”

“…[13,14]. In this context, the most applied method in the PSPEG are Artificial Neural Networks (ANN's) [10] and, more recently Deep Learning models [15].…”

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

“…Further, the DL techniques, and gradient boosted trees are used in [64] to forecast the solar irradiance directly from an extracted sub-image surrounding the sun. A detailed overview of different DL models for solar irradiance forecasting are discussed in [65][66][67][68][69]. In [66,68,69], a long short-term memory (LSTM) neural network technique is used to develop a multi-time scale model for solar irradiance forecasting.…”

“…This enhances the forecasting ability and accuracy when compared to the conventional DL-based forecasting approaches. Similarly, in [67], the DL methodologies are adapted to develop time series models for solar irradiance forecasting in different areas. The developed models consider both single location and multilocation univariate data to achieve improved accuracy, performance, and reliability for both forecasting and the system operation.…”

A Multistage Hybrid Model ICEEMDAN-SE-VMD-RDPG for a Multivariate Solar Irradiance Forecasting

“…The commonly employed statistical measures for solar resource forecasting applications include RMSE, MAE and MAPE [29,30,55]. The accurate forecasting of solar irradiance is an intricate process due to the stochastic and intermittent nature of the solar irradiance [28,56]. The solar irradiance is only present during the daytime, whereas it has a very low or zero value during the night.…”