Sliding window approach with first-order differencing for very short-term solar irradiance forecasting using deep learning models. (March 2022)

• Record Type: Journal Article
• Authors: Bhatt, Ankit; Ongsakul, Weerakorn; M., Nimal Madhu; Singh, Jai Govind
• Is Part Of: Sustainable energy technologies and assessments. Volume 50(2022)
• Publication Date: 2022-03
• Subjects: AIT Asian Institute of Technology -- ANN Artificial Neural Network -- ARIMA Auto-regressive Integrated Moving Average -- DNN Deep Neural Network -- ELM Extrement Learning Machine -- GRU Gated Recurrent Unit -- BPNN Back Propagation Neural Network -- WPD Wavelet Packet Decomposition -- WMI Wrapper Mutual Information; Sliding window technique -- First-order differencing -- Deep neural networks -- Multi-step solar irradiance prediction; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2021.101864
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: 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.