Research on a Novel Combination System on the Basis of Deep Learning and Swarm Intelligence Optimization Algorithm for Wind Speed Forecasting

Xiaohui He,Ying Nie,Hengliang Guo,Jianzhou Wang

doi:10.1109/access.2020.2980562

Abstract

Wind speed forecasting takes a significant place in electric system owing to the fact that it has significant influence on operation efficiency and economic benefits. Aimming at improving forecast performance, a substantial number of wind speed prediction models have been proposed. However, these models have disregarded the limits of individual prediction models and the necessity of data preprocessing, resulting in poor prediction accuracy. In this study, a novel forecasting system is proposed consisting of three modules: data preprocessing module, individual forecasting module and weight optimization module, which effectively achieve better forecasting ability. For data preprocessing and individual forecasting module, more regular sequences are obtained by decomposition technology, and association features are extracted by deep learning algorithm in prediction module. In the weight optimized module, the combination method base on the multi-objective optimization algorithm and nonnegative constraint theory are used to improve the prediction effectiveness. The combination model successfully exceeds the limits of individual predicton models and comparatively improves prediction accuracy. The effectiveness of the developed combination system is evaluated by 10-min wind speed in Penglai, China. The experiment results indicate that proposed forecasting system is better than other traditional forecasting models on three real wind speed datasets indeed.

Highlights

With the improving attention to clean energy, the utilization rate of resources is raising day by day
1) EXPERIMENTAL RESULTS DESCRIPTION The experiment is set as a comparison the forecasting performance between proposed model and three single models, including ENSEMBLE EMPIRICAL MODE DECOMPOSITION (EEMD)-MULTI-LAYER PERCEPTRON (MLP), EEMD-LONG SHORT-TERM MEMORY (LSTM) and EEMD-AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)
The conclusion can be obtained as follows: (1) From Table 4, it can be clearly seen that based on the three standards of IMAE (%), IMSE (%) and IMAPE (%), the proposed combination model is more accuracy than other models discussed in this work

Summary

Introduction

With the improving attention to clean energy, the utilization rate of resources is raising day by day. Pollution-free renewable energy, wind energy is the theme of new energy resources analysis and development. Wind energy is a kind of fastest growing renewables, and is considered as an alternative to traditional fuel-fired electricity generation. The precisely wind speed forecast is significant for improving wind energy utilization and stable electric system operation. Incorrect wind speed prediction can lead to unfavorable decisions and wind power systems can be caused huge economic losses. Wind power is advance in the aspects of its reliability, good ability and low price, and the utilization of wind energy helps to reduce air pollution, which is the largest environment task for most regions and countries [1]

Methods

Results

Conclusion