Multi-layer cooperative combined forecasting system for short-term wind speed forecasting

Wang, Jianzhou; Li, Qiwei; Zeng, Bo

doi:10.1016/j.seta.2020.100946

Cited by 23 publications

(12 citation statements)

References 48 publications

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“…The methods based on machine learning have strong learning ability and can handle the non-linear components in the time series, so they have been widely used in some fields (Gündüz et al, 2019;Henrique et al, 2019;Volk et al, 2020;Wang et al, 2021b). In this study, three different networks were selected to analyze the series, since the features of the series are uncertain.…”

Section: Machine Learning Techniquementioning

confidence: 99%

Design of a Combined System Based on Multi-Objective Optimization for Fine Particulate Matter (PM2.5) Prediction

Bai

Zeng

et al. 2022

Front. Environ. Sci.

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Air pollution forecasting plays a pivotal role in environmental governance, so a large number of scholars have devoted themselves to the study of air pollution forecasting models. Although numerous studies have focused on this field, they failed to consider fully the linear feature, non-linear feature, and fuzzy features contained in the original series. To fill this gap, a new combined system is built to consider features in the original series and accurately forecast PM2.5 concentration, which incorporates an efficient data decomposition strategy to extract the primary features of the PM2.5 concentration series and remove the noise component, and five forecasting models selected from three types of models to obtain the preliminary forecasting results, and a multi-objective optimization algorithm to combine the prediction results to produce the final prediction values. Empirical studies results indicated that in terms of RMSE the developed combined system achieves 0.652 6%, 0.810 1%, and 0.775 0% in three study cities, respectively. Compared to other prediction models, the RMSE improved by 60% on average in the study cities.

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Section: Machine Learning Techniquementioning

confidence: 99%

Design of a Combined System Based on Multi-Objective Optimization for Fine Particulate Matter (PM2.5) Prediction

Bai

Zeng

et al. 2022

Front. Environ. Sci.

Self Cite

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“…[17,34]. The use of the DM-test has been reported for large-scale wind farm applications [18,35]; however, in these applications, the DM-test of the error residuals was applied to a normal distribution, which may not be suitable at the microgrid scale.…”

Section: Introductionmentioning

confidence: 99%

Statistical evaluation of wind speed forecast models for microgrid distributed control

2022

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With the increasing needs to decarbonise existing energy systems, there is an effort to integrate small-scale distributed generation sources, such as wind generators, with the electric demand in circuits known as microgrids. The operation of distributed variable renewable resources is subject to an optimum operating regime, ahead of real-time, which relies on output forecast. However, many wind speed forecast models are designed for centralised controllers, which are vulnerable to control failures. A suitable wind forecast model for a distributed control system is, therefore, required for optimal and reliable use of renewable generation. This paper presents a comparison of wind speed forecast models suited for distributed control, evaluating them in terms of the statistical significant difference in accuracy and computational resource requirements. This is essential since computational resources are limited in distributed control schemes. The data used in this paper is the historical wind speed of the Auchencorth Moss Atmospheric Observatory from 2016 to the end of 2019. Two forecast model types based on Auto-regression and Artificial Neural Network (ANN) are compared using the Diebold-Mariano test. Results show that ANN models with parallel hidden layers have the highest accuracy with statistical significant difference, while remaining suitable for microgrid distributed control.

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“…The application of typical statistical models in the air quality includes the autoregressive (AR) model (Slottje et al, 2001), the Markov chain model (Zakaria et al, 2019), the autoregressive integrated moving average (ARima) method (Kumar & Goyal, 2011), and grey forecasting model (Wu et al, 2017). These models have been employed for air quality predictions; however, they have shortcomings as well (Wang, Li, & Zeng, 2021;Wang, Li, Wang, & Lu, 2021). Priori assumptions about data distribution need to be made and the nonlinear features of AQI series cannot be extracted, which leave the accuracy and reliability of the model to be improved.…”

Section: Introductionmentioning

confidence: 99%

Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm

Wang

et al. 2022

Journal of Forecasting

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This research presents a hybrid model for multi-step, interval forecasting of air quality indices. An efficient preprocessing module is applied to split the raw data into various sub-series, and the optimal mode of data input is determined through feature selection. A multi-objective optimization algorithm is proposed to tune the parameters of kernel extreme learning machine to achieve high accuracy and stability. An evaluation with several error criteria, benchmark models, and critique is conducted using three daily air quality index datasets from three cities of China. Empirical results indicate that the developed model achieves superior predictions of air quality indices, which may be useful in policies for mitigating air pollution.

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Multi-layer cooperative combined forecasting system for short-term wind speed forecasting

Cited by 23 publications

References 48 publications

Design of a Combined System Based on Multi-Objective Optimization for Fine Particulate Matter (PM2.5) Prediction

Design of a Combined System Based on Multi-Objective Optimization for Fine Particulate Matter (PM2.5) Prediction

Statistical evaluation of wind speed forecast models for microgrid distributed control

Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm

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