Short-Term Load Forecasting Using A Particle-Swarm Optimized Multi-Head Attention-Augmented CNN-LSTM Network

TL;DR

This paper introduces a novel deep learning framework combining Particle-Swarm Optimization, Multi-Head Attention, and CNN-LSTM to improve short-term load forecasting accuracy, robustness, and efficiency, demonstrated on real electricity demand data.

Contribution

The paper presents a new hybrid model that optimizes hyperparameters automatically and enhances feature extraction for load forecasting, surpassing existing methods.

Findings

Achieved a Mean Absolute Percentage Error of 1.9376

Demonstrated superior accuracy over state-of-the-art approaches

Showed improved computational efficiency and robustness

Abstract

Short-term load forecasting is of paramount importance in the efficient operation and planning of power systems, given its inherent non-linear and dynamic nature. Recent strides in deep learning have shown promise in addressing this challenge. However, these methods often grapple with hyperparameter sensitivity, opaqueness in interpretability, and high computational overhead for real-time deployment. In this paper, I propose a novel solution that surmounts these obstacles. Our approach harnesses the power of the Particle-Swarm Optimization algorithm to autonomously explore and optimize hyperparameters, a Multi-Head Attention mechanism to discern the salient features crucial for accurate forecasting, and a streamlined framework for computational efficiency. Our method undergoes rigorous evaluation using a genuine electricity demand dataset. The results underscore its superiority in terms of accuracy, robustness, and computational efficiency. Notably, our Mean Absolute Percentage Error of 1.9376 marks a significant advancement over existing state-of-the-art approaches, heralding a new era in short-term load forecasting.