Citation

Ahmadi M, Khashei M. Eng. Appl. Artif. Intell. 2021; 99: e104133.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.engappai.2020.104133

PMID

unavailable

Abstract

Wind power is one of the most important clean energy and alternative to fossil fuels. More attention has been paid to this renewable resource in today's world due to increasing public awareness, concerns about greenhouse gas emissions and environmental issues, and reducing the oil and gas reservoirs. Accurate and precise wind speed and wind power forecasts are the most critical and influential factors in making desired and efficient managerial and operational decisions in the wind energy area. Wind power and speed forecasting play an essential role in the planning, controlling, and monitoring of intelligent wind power systems. Therefore, several different models have been developed in the subject literature in order to predict this energy source more accurately. However, there is no general consensus on the model that must be selected and used in a specific situation of time horizon, sample size, complexity, uncertainty, etc. Hybrid models are the most frequently used and the most popular forecasting models in the energy literature. In this paper, combined approaches used in the wind energy forecasting field are first categorized into four main categories: 1) Data preprocessing based approaches, 2) Parameter optimization-based approaches, 3) Post processing based approaches, and 4) component combination-based approaches.

RESULTS indicate that the component combination-based category is the most diverse and extensive hybrid approach in the literature. Thus, in the next section of the paper, more attention is paid to these approaches and then classified into two major classes of series and parallel hybrid models. The literature review demonstrates that parallel hybrid models are more popular approaches in comparison with series hybrid models and more used for wind forecasting. Other specific and detailed conclusions and remarks are introduced in related sections.

Language: en

Keywords

Hybrid forecasting approaches; Parallel hybrid models; Series hybrid models; Time series forecasting; Wind power forecasting; Wind speed forecasting