# Sliding Window Regression based Short-Term Load Forecasting of a Multi-Area Power System

arXiv: https://arxiv.org/abs/1905.08111 · HTML: https://mlsanity.com/paper/1905.08111/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2019.

## Built on by (most cited first, 1 of 1)

- [Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting](https://mlsanity.com/paper/2604.24788/) (2026)

## Abstract (arXiv)

Short term load forecasting has an essential medium for the reliable, economical and efficient operation of the power system. Most of the existing forecasting approaches utilize fixed statistical models with large historical data for training the models. However, due to the recent integration of large distributed generation, the nature of load demand has become dynamic. Thus because of the dynamic nature of the power load demand, the performance of these models may deteriorate over time. To accommodate the dynamic nature of the load demands, we propose a sliding window regression based dynamic model to predict the load demands of the multiarea power system. The proposed algorithm is tested on five zones of New York ISO. Results from our proposed algorithm are compared with four existing techniques to validate the performance superiority of the proposed algorithm.