To Study the Impact on Power Consumption in the COVID-19 Paradigm and Data-Driven Load Forecasting Technique with LSTM-RNN

This research underscores the importance of accurate demand forecasting for future power generation within smart grid and building designs. It highlights the need for integrating renewable energy sources and demand response effectively to enhance the reliability of the smart grid. Energy forecasting faces challenges due to weather, population growth, and seasonal fluctuations, compounded by rapid socioeconomic changes, particularly during pandemics. The study aims to forecast future energy consumption under pandemic scenarios, considering evolving consumption patterns. Divided into two sections, it examines the impact of COVID-19 on customer consumption and employs the LSTM-RNN approach for accurate load forecasting. Demonstrating superior effectiveness, the model utilizes hourly-driven power consumption data from Houston, Texas, USA, across various scenarios, offering insights into its limitations and influencing factors.