Publication detail

A hybrid framework for forecasting power generation of multiple renewable energy sources

Zheng, J., Du, J., Wang, B., Klemeš, J.J., Liao, Q., Liang, Y.

Original Title

A hybrid framework for forecasting power generation of multiple renewable energy sources

Type

journal article in Web of Science

Language

English

Original Abstract

The accurate power generation forecast of multiple renewable energy sources is significant for the power scheduling of renewable energy systems. However, previous studies focused more on the prediction of a single energy source, ignoring the relationship among different energy sources, and failing to predict accurate power generation for all energy sources simultaneously. This paper proposes a hybrid framework for the power generation forecast of multiple renewable energy sources to overcome deficiencies. A Convolutional Neural Network (CNN) is developed to extract the local correlations among multiple energy sources, the Attention-based Long Short-Term Memory (A-LSTM) network is developed to capture the nonlinear time-series characteristics of weather conditions and individual energy, and the Auto-Regression model is applied to extract the linear time-series characteristics of each energy source. The accuracy and practicality of the proposed method are verified by taking a renewable energy system as an example. The results show that the hybrid framework is more accurate than other advanced models, such as artificial neural networks and decision trees. Mean absolute errors of the proposed method are reduced by 13.4%, 22.9%, and 27.1% for solar PV, solar thermal, and wind power compared with A-LSTM. The sensitivity analysis has been conducted to test the effectiveness of each component of the proposed hybrid framework to prove the significance of energy correlation patterns with higher accuracy and stability compared with the other two patterns.

Keywords

Forecasting; Hybrid framework; Long short-term memory; Power generation; Renewable energy system

Authors

Zheng, J., Du, J., Wang, B., Klemeš, J.J., Liao, Q., Liang, Y.

Released

1. 2. 2023

Publisher

Elsevier Ltd

ISBN

1364-0321

Periodical

RENEWABLE & SUSTAINABLE ENERGY REVIEWS

Number

172

State

United States of America

Pages count

14

URL

https://www.sciencedirect.com/science/article/pii/S1364032122009273

BibTex

@article{BUT180391,
  author="Bohong {Wang} and Jiří {Klemeš}",
  title="A hybrid framework for forecasting power generation of multiple renewable energy sources",
  journal="RENEWABLE & SUSTAINABLE ENERGY REVIEWS",
  year="2023",
  number="172",
  pages="14",
  doi="10.1016/j.rser.2022.113046",
  issn="1364-0321",
  url="https://www.sciencedirect.com/science/article/pii/S1364032122009273"
}