Work

Particle-LSTM: Advancing wind energy forecasting through a synergistic fusion of LSTM-VAR model and particle filter

Year: 2025

Type: article

Abstract: Wind energy forecasting is essential for the optimal functioning of power grids and energy markets. In this study, we introduce the Particle-LSTM, which revolutionizes wind power forecasting by merging a Long Short-Term Memory-Vector Autoregression (LSTM-VAR) model with a Particle Filter. The LSTM-VAR model adeptly executes an affine transformation of time-varying VAR parameters utilizing an LSTM network, while the Particle Filter dramatically enhances forecast accuracy through its integration with the LSTM-VAR model. A comparative analysis against established models clearly demonstrates that Particle-LSTM surpasses existing methods across a range of error metrics, underscoring its dominance in wind power forecasting.

Source: Nonlinear Theory and Its Applications IEICE

DOI: 10.1587/nolta.16.335

Authors: Alisha Maity, Nitish Das

Institutions: Amity University, MIT Art, Design and Technology University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: diamond

Funder: Amity University