Indian Peak Power demand Forecasting : Transformer Based Implementation of Temporal Architecture

TL;DR

This paper introduces a Transformer-based deep learning model for long-term peak power demand forecasting in India, outperforming existing models in accuracy and variance modeling.

Contribution

The paper presents a novel application of Temporal Fusion Transformer for long-term power demand forecasting, demonstrating improved performance over traditional models.

Findings

The proposed model outperforms other forecasting techniques in benchmarks.

It accurately models the variance in power demand.

The approach is effective for long-term demand prediction in India.

Abstract

The long-term forecasting of electricity demand has been a prevalent research topic, primarily because of its economic and strategic relevance. Several machine learning as well as deep learning techniques have been developed in parallel with the growing complexity of the peak demand, planning for generation facilities and transmission augmentation in future. 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.