Smart Short-Term Load Forecasting through Coordination of LSTM-Based Models and Feature Engineering Methods during the COVID-19 Pandemic

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.

Keywords:

• short-term load forecasting
• machine learning
• deep learning
• uncertainty
• feature engineering
• long short-term memory
• COVID-19

Additional information

Notes on contributors

Seyed Mohammad Shobeiry

Seyed Mohammad Shobeiry was born in 1997 in Tehran, Iran. He received the B. Sc. degree in electrical engineering from Semnan University, Semnan, Iran, in 2019. He received the M. Sc. degree in electrical engineering from Shahid Beheshti University, Tehran, Iran, in 2021. At present, he is an electrical engineering Ph. D. student at Shahid Beheshti University, Tehran, Iran. He is also a researcher at Electricity Network Research Institute, Shahid Beheshti University, Tehran, Iran. His research interests include power systems operation and dispatching, power systems restructuring, load forecasting and state estimation.

Sasan Azad

Sasan Azad is a Ph.D. student in the Faculty of Electrical Engineering and a researcher at the Electrical Networks Institute of the Shahid Beheshti University. He obtained his BSc degree from the Razi University of Kermanshah and his MSc degree from the Shahid Beheshti University. His main areas of interest are the security and voltage stability of power systems, smart grids, and electric vehicles.

Mohammad Taghi Ameli

Mohammad Taghi Ameli graduated with a PhD in Electrical Engineering from the Technical University of Berlin, Germany, in 1997. He is currently a Professor of Department of Electrical Engineering and Head of Electrical Networks Institute at Shahid Beheshti University. He specializes in power systems operation and control, micro grid and smart grid.