Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

TL;DR

This paper introduces a hybrid approach combining Hilbert-Huang transform and machine learning techniques like neural networks and support vector regression to improve short-term power system parameter forecasting, especially for non-stationary data.

Contribution

It develops a novel hybrid forecasting model that integrates mode decomposition with machine learning, enhancing accuracy for non-stationary power system time-series data.

Findings

Effective in forecasting active power flow, electricity prices, wind speed, and direction.

Uses variable importance ranking to optimize model features.

Employs hybrid models with RBF neural networks and support vector regression.

Abstract

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. Apart from introduction and references the paper is organized as follows. The section 2 presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learning-based algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.