Smart Short-Term Load Forecasting through Coordination of LSTM-Based Models and Feature Engineering Methods during the COVID-19 Pandemic. Issue 2 (20th January 2023)

• Record Type: Journal Article
• Authors: Shobeiry, Seyed Mohammad; Azad, Sasan; Ameli, Mohammad Taghi
• Is Part Of: Electric power components and systems. Volume 51:Issue 2(2023)
• Page Start: 171
• Page End: 187
• Publication Date: 2023-01-20
• Subjects: short-term load forecasting -- machine learning -- deep learning -- uncertainty -- feature engineering -- long short-term memory -- COVID-19; Electric machinery -- Periodicals; 621.3104205
• DOI: 10.1080/15325008.2023.2168092
• Languages: English
• ISSNs: 1532-5008

Abstract

Abstract: Short-term load forecasting is essential for power companies because it is necessary to ensure sufficient capacity. This article proposes a smart load forecasting scheme to forecast the short-term load for an actual sample network in the presence of uncertainties such as weather and the COVID-19 epidemic. The studied electric load data with hourly resolution from the beginning of 2020 to the first seven days of 2021 for the New York Independent Operator is the basis for the modeling. The new components used in this article include the coordination of stacked long short-term memory-based models and feature engineering methods. Also, more accurate and realistic modeling of the problem has been implemented according to the existing conditions through COVID-19 epidemic data. The influential variables for short-term load forecasting through various feature engineering methods have contributed to the problem. The achievements of this research include increasing the accuracy and speed of short-term electric load forecasting, reducing the probability of overfitting during model training, and providing an analytical comparison between different feature engineering methods. Through an analytical comparison between different feature engineering methods, the findings of this article show an increase in the accuracy and speed of short-term load forecasting. The results indicate that combining the stacked long short-term memory model and feature engineering methods based on extra-trees and principal component analysis performs well. The RMSE index for day-ahead load forecasting in the best engineering method for the proposed stacked long short-term memory model is 0.1071.