Comparative analysis of strategies for multi-step ahead wind power forecasting for different forecast horizons and wind power datasets

Abstract

In light of the stochastic nature of wind, the main obstacle to reliable penetration of wind power into the power grid is its variability. An accurate multi-step prediction of wind power is the most efficient way to address this issue, along with challenges in wind power management and maintenance. Several strategies have been presented in the literature for multi-step ahead wind power prediction, however, a comprehensive comparison of these strategies has not been performed to determine how outliers in power datasets influence the accuracy of multi-step ahead predictions at different forecasting horizons. To fill this gap, in this study, by reviewing the existing strategies, three main approaches including the recursive, direct, and multi-input multi-output (MIMO) strategies are investigated using two real-world datasets from two wind turbines in Turkey and Scotland. A hybrid prediction method based on application of the Isolation Forest for outlier treatment, long short-term memory (LSTM) as the core of the prediction model and a new hyperparameter optimization algorithm for the tuning of the LSTM model is used for predictions in different strategies. According to the results of the experiments (1) in two-step ahead wind power forecasting, all strategies produce similar results, in both wind turbines; (2) in all forecast horizons of more than two steps ahead, the MIMO approach is best when the dataset does not contain any outliers – however, when there are outliers, the direct approach performs better; (3) in both datasets, the recursive approach to wind power forecasting produces the highest error rates.

Keywords

Multi-step prediction wind power forecasting recursive strategy direct strategy multi-input multi-output (MIMO) strategy

Get full access to this article

View all access options for this article.

References

Xia

Xiao

, et al. Combined model with secondary decomposition-model selection and sample selection for multi-step wind power forecasting. Appl Energy 2020; 261: 114345.

Hanifi

Lotfian

Zare-behtash

, et al. Offshore wind power forecasting—A new hyperparameter optimisation algorithm for deep learning models. Energies 2022; 15: 6919.

Garrigle

EVM

Leahy

. The value of accuracy in wind energy forecasts. In: 2013 12th International Conference on Environment and Electrical Engineering. Wroclaw, 2013.

Hanifi

Liu

Lin

, et al. A critical review of wind power forecasting methods-past, present and future. Energies 2020; 13: 3764.

Hanifi

. Development and application of AI for offshore wind power forecasting. PhD Thesis. Glasgow, UK: University of Glasgow, 2024.

Bao

Xiong

. Multi-step-ahead time series prediction using multiple-output support vector regression. Neurocomputing 2014; 129: 482–493.

Hanifi

Zare-behtash

Cammarano

, et al. Offshore wind power forecasting based on WPD and optimised deep learning methods. Renew Energy 2023; 218: 119241.

Hanifi

Cammarono

Zare-behtash

. Advanced hyperparameter optimization of deep learning models for wind power prediction. Renew Energy 2024; 221: 119700.

Xiao

Qian

Shao

. Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm. Energy Convers Manag 2017; 143: 410–430.

10.

Jorgensen

Shaker

. Wind power forecasting using machine learning: state of the art, trends and challenges. In: 2020 IEEE 8th International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, 2020, vol 2020, pp. 44–50.

11.

Ela

Kirby

Navid

, et al. Effective ancillary services market designs on high wind power penetration systems. In: IEEE Power and Energy Society General Meeting, San Diego, CA, 2012.

12.

. Multi-step ahead wind power forecasting based on recurrent neural networks, 2021, pp. 217–222. Conference: 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

13.

Wang

. Multi-step ahead wind speed prediction based on optimal feature extraction, long short-term memory neural network and error correction strategy. Appl Energy 2018; 230: 429–443.

14.

Huang

Yang

Liu

, et al. Multi-step wind speed forecasting based on ensemble empirical mode decomposition, long short-term memory network and error correction strategy. Energies 2019; 12: 1822.

15.

Liang

Wang

, et al. Short-term wind power combined forecasting based on error forecast correction. Energy Convers Manag 2016; 119: 215–226.

16.

Liu

Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short-term memory neural network and Elman neural network

Energy Convers Manag 2018; 156: 498–514.

17.

Yaghoubirad

Azizi

Farajollahi

, et al. Deep learning-based multistep ahead wind speed and power generation forecasting using direct method. Energy Convers Manag 2023; 281: 116760.

18.

Shi

Lei

Huang

, et al. Hourly day-ahead wind power prediction using the hybrid model of variational model decomposition and long short-term memory. Energies 2018; 11: 3227.

19.

Liu

. Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction. Energy Convers Manag 2018; 167: 203–219.

20.

Liu

. Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short-term memory network. Energy Convers Manag 2018; 166: 120–131.

21.

Liu

. An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm. Renew Energy 2018; 123: 694–705.

22.

Meng

Yin

, et al. Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm. Energy Convers Manag 2016; 114: 75–88.

23.

Ben Taieb

Bontempi

Atiya

, et al. A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst Appl 2012; 39: 7067–7083.

24.

Galarza-Chavez

Martinez-Rodriguez

Domínguez-Cruz

, et al. Multi-step wind energy forecasting in the Mexican Isthmus using machine and deep learning. Energy Rep 2025; 13: 1–15.

25.

Lin

Liu

Collu

. Wind power prediction based on high-frequency SCADA data along with isolation forest and deep learning neural networks. Electr Power Energy Syst 2020; 118: 105835.

26.

Bergstra

Bardenet

Bengio

, et al. Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems 24 (NIPS 2011), Spain, 2011, pp. 1–9.

27.

Akiba

Sano

Yanase

, et al. Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, 4-8 August 2019.