Short-Term Electricity Load Forecasting of Hanoi City Based on Prophet Model

In recent years, the surging demand for electricity has exerted significant pressure on electricity generation and the overall functionality of the power grid. As a result, accurately anticipating electricity consumption holds considerable importance for the power industry. Precise forecasting of power load carries, and enhanced socioeconomic gains. Among popular forecasting techniques, machine learning has emerged as the most reliable tool for this purpose. In this paper, the Prophet algorithm has been applied to predict hourly and daily electricity consumption. The dataset encompasses hourly electricity consumption data in Hanoi, along with temperatures measured in Noi Bai- Hanoi, spanning from 2017 to 2020. The best model's parameters are selected using the Grid Search method. The study's results demonstrate the efficacy of the prediction model, yielding accurate forecasts.