Sliding window approach with first-order differencing for very short-term solar irradiance forecasting using deep learning models

The intermittent behavior of solar is usually responsible for creating uncertainty while generating power. By implementing suitable forecasting techniques for solar irradiance (SI), we can overcome this intermittency which can be helpful in the economic load dispatch as well as to control, manage and optimize the power generation in the microgrid. This paper presents three deep learning (DL) models to forecast SI from 1 step (15-minute) to 6 steps (1 h 30 min) ahead. By implementing the sliding window technique, the input variables are converted into 12 steps lag datasets to train the model whereas outputs are transformed by first-order differencing. A total dataset of 18,277 from average 15-min interval global SI collected during January 1, 2016, to January 6, 2017, from the Asian Institute of Technology (AIT) Metrological station is used to train and evaluate the performance of the DL models. Based on the obtained results from different evaluation parameters such as maximum absolute error, confidence interval (CI), linear regression plot, mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R squared, we found that the deep hybrid model consists of convolutional neural network-long short term memory (CNN-LSTM) can outperform during multistep forecasting. The findings of the present work suggest that the proposed deep hybrid LSTM-CNN model is a reliable alternative for very short-term SI prediction due to its high predictive accuracy.

Automated summary

This study proposes three deep learning models for solar irradiance forecasting and finds that the deep hybrid model consisting of a convolutional neural network and long short term memory outperforms others in multistep forecasting. The results suggest that the proposed deep hybrid LSTM-CNN model is a reliable alternative for very short-term solar irradiance prediction due to its high predictive accuracy.