A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer

Sen Guo,Huiru Zhao,Haoran Zhao

doi:10.3390/en10070922

Abstract

As one of the most promising kinds of the renewable energy power, wind power has developed rapidly in recent years. However, wind power has the characteristics of intermittency and volatility, so its penetration into electric power systems brings challenges for their safe and stable operation, therefore making accurate wind power forecasting increasingly important, which is also a challenging task. In this paper, a new hybrid wind power forecasting method, named the BND-ALO-RVM forecaster, is proposed. It combines the Beveridge-Nelson decomposition method (BND), relevance vector machine (RVM) and ant lion optimizer (ALO). Considering the nonlinear and non-stationary characteristics of wind power data, the wind power time series were firstly decomposed into deterministic, cyclical and stochastic components using BND. Then, these three decomposed components were respectively forecasted using RVM. Meanwhile, to improve the forecasting performance, the kernel width parameter of RVM was optimally determined by ALO, a new Nature-inspired meta-heuristic algorithm. Finally, the wind power forecasting result was obtained by multiplying the forecasting results of those three components. The proposed BND-ALO-RVM wind power forecaster was tested with real-world hourly wind power data from the Xinjiang Uygur autonomous region in China. To verify the effectiveness and feasibility of the proposed forecaster, it was compared with single RVM without time series decomposition and parameter optimization, RVM with time series decomposition based on BND (BND-RVM), RVM with parameter optimization (ALO-RVM), and Generalized Regression Neural Network with data decomposition based on Wavelet Transform (WT-GRNN) using three forecasting performance criteria, namely MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error). The results indicate the proposed BND-ALO-RVM wind power forecaster has the best forecasting performance of all the tested options, which confirms its validity.

Highlights

Facing the unfavorable situation of fossil energy resource depletion and environmental deterioration, people are increasingly focusing on the exploitation and utilization of renewable energy resources, such as wind power and solar photovoltaic power [1]
With parameter optimization (ALO-relevance vector machine (RVM)), and Generalized Regression Neural Network with data decomposition based on Wavelet Transform (WT-GRNN) using three forecasting performance criteria, namely MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and root mean square error (RMSE)
The results indicate the proposed Beveridge-Nelson decomposition (BND)-ant lion optimizer (ALO)-RVM wind power forecaster has the best forecasting performance of all the tested options, which confirms its validity

Summary

Introduction

Facing the unfavorable situation of fossil energy resource depletion and environmental deterioration, people are increasingly focusing on the exploitation and utilization of renewable energy resources, such as wind power and solar photovoltaic power [1]. Energies 2017, 10, 922 one of the fastest growing and most promising renewable energy power sources, and the share of wind power generation in the total electricity output has been increasing yearly [2,3]. The cumulative installed wind power capacity in China amounts to 168.7 GW, which accounts for 34.7% of the world total. Wind power output has the characteristics of stochastic fluctuation, intermittency and uncertainty [4]. When a wind generator is connected to the power grid, it will impose new requirements and challenges on electric power systems, such as efficient scheduling of power resources and continuing guarantee of smooth power system operation [5]

Methods

Results

Conclusion