Indian Peak Power demand Forecasting: Transformer Based Implementation of Temporal Architecture
Year: 2022
Type: conference-paper
Abstract: The long-term forecast of electricity demand has been a recurrent research topic, due to its economic and strategic relevance. Several machine learning as well as deep learning techniques have evolved in parallel with the complexity of the peak demand, planning for future generation facilities and transmission augmentation. Most of these proposed techniques work on short-term forecasting as long-term forecasting is considerably more challenging due to unpredictable and unforeseeable variables that may arise in the future. This paper proposes a Temporal Fusion Transformer based deep learning approach for long term forecasting of peak power demand. The dataset used in this paper consists of peak power demand in India for a period of 6 years and the prediction was done for a period of 1 year. Our proposed model was compared with other popular forecasting models and it performed considerably better in benchmarks and was also more accurate in modelling the variance in the power demand.
Authors: Shashwat Jha, Vishvaditya Luhach
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4309229374