An efficient load forecasting technique by using Holt‐Winters and Prophet algorithms to mitigate the impact on power consumption in COVID‐19

Abstract

It is strongly recommended to implement effective long‐term load forecasting for future power generation in the new architecture of the smart grid and buildings. This method is essential for the smart grid's stability, power demand estimation, and an improved energy management system, which will enhance integration between efficient demand response and distributed renewable energy sources. However, due to influencing elements including climatic, societal, and seasonal aspects, it is quite challenging to perform energy prediction with high accuracy. To estimate the load demand before and during the time period of the COVID‐19 paradigm with its diversity and complexity, the authors present and integrate time series forecasting techniques such as Holt‐Winters and Prophet algorithms. In comparison to the Holt‐Winters model, the Prophet model has shown to be more noise‐resistant. Additionally, the Prophet model surpasses the Holt‐Winters model according to the generalisability test of the two models by using the hourly driven power consumption data from Houston, Texas, USA. The resultant constraints and influential factors are discussed, and experimental results can be validated from the pivotal outcome.