Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: Case studies of an offshore wind farm

Xu, Xuefang; Hu, Shiting; Shi, Pengfei; Shao, Huaishuang; Li, Ruixiong; Li, Zhi

doi:10.1016/j.energy.2022.125342

Cited by 51 publications

(12 citation statements)

References 42 publications

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“…On the other hand, the uncertainties of various loads and renewable energy sources seriously affect the safe and reliable operation of the RIES (Wang et al, 2020), which is an important problem that cannot be ignored in the optimal operation process of the RIES. In the study by Xu et al (2022), an efficient wind speed prediction model based on phase space reconstruction and BLS was proposed, which could evaluate the regularity of wind speed effectively without the overfitting issue. In the study by Xiang et al (2021), an uncertainty model was proposed for the integrated demand response (IDR) of energy prices based on fuzzy and probabilistic variables.…”

Section: Introductionmentioning

confidence: 99%

Two-stage robust optimization of regional integrated energy systems considering uncertainties of distributed energy stations

Zong

Yuan²

2023

Front. Energy Res.

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For a regional integrated energy system (RIES) composed of an energy supply network and distributed energy station, the uncertainty of distributed photovoltaic (PV) output and the fluctuation of various loads pose significant challenges to the stability of system operation and the accuracy of optimal scheduling. In order to enhance the operational reliability of regional integrated energy systems and reduce the impact of photovoltaic and load uncertainties on distributed energy stations, this study proposes robust optimization method of regional integrated energy systems that takes into account the uncertainty of the distributed energy station. First, the regional integrated energy system is divided into an upper electric-gas energy supply network and a lower distributed energy station. The upper model mainly realizes energy transmission, while the lower model is a two-stage robust optimization model of distributed energy stations in the form of min–max–min, which mainly realizes flexible energy supply of different types of energy. Then, the lower two-stage robust optimization model is simplified and solved using a column and constraint generation (CCG) algorithm. After that, an alternating direction method of multipliers (ADMM) is used to solve the upper and lower models of the regional integrated energy system, and the solution scale is reduced while ensuring the correlation between the energy transmission network and the distributed energy stations. Finally, a test example is provided to illustrate the effectiveness and usefulness of the proposed method. It follows from simulation results that the robust optimization method can effectively reduce the instability of the system operation caused by uncertainty factors and improve the system’s anti-interference ability, and in addition, systems with high penetration levels of photovoltaic output will benefit more from robust optimization.

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Section: Introductionmentioning

confidence: 99%

Two-stage robust optimization of regional integrated energy systems considering uncertainties of distributed energy stations

Zong

Yuan²

2023

Front. Energy Res.

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“…Therefore, developing renewable and pollution-free energy is a hot issue in today's society. In recent years, various new energy sources have been constantly emerging, among which wind power is one of the fastest growing renewable energy sources due to its advantages such as friendly to environment, zero carbon dioxide emissions, abundant resources, and cheap prices [2,3]. In 2021, renewable energy accounts for 38.3% of global electricity generation with wind power accounting for 6.7% [4].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, most of the existing methods ignore the pediction lad which is brought by sharp changes in natural wind speed [24]. Obviously, this will increase the error between the actual wind speed and predicted values [2].…”

Section: Introductionmentioning

confidence: 99%

Wind Speed Prediction Based VMD-BLS and Error Compensation

Jiao¹,

Zhang²,

Song³

et al. 2023

Preprint

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As one of the fastest-growing new energy sources, wind power technology has attracted widespread attention from all over the world. In order to improve the quality of wind power generation, wind speed prediction is an indispensable task. In this paper, an error correction based Variational Mode Decomposition and Broad Learning System (VMD-BLS) hybrid model is proposed for wind speed prediction. First, the wind speed is decomposed into multiple components by the VMD algorithm, and then an ARMA model is established for each component to find the optimal number of sequence divisions. Second, the BLS model is used to predict each component, and the prediction results are summed to obtain the wind speed forecast value. However, in some traditional methods, there is always time lag, which will reduce the forecast accuracy. To deal with this, a novel error correction technique is developed by utilizing BLS. Through verification experiment with actual data, it proves that the proposed method can reduce the phenomenon of prediction lag, and can achieve higher prediction accuracy than traditional approaches, which shows our method’s effectiveness in practice.

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“…In the context of the recorded nonlinear time series, phase space represents a well-established approach for investigating the underlying structure from the perspective of chaos. [32][33][34] Phase space reconstruction is highly effective for illustrating the dynamical characteristics of chaotic attractors, and it represents an essential step towards calculating chaotic features based on geometry and information theory. From open literature, Packard et al [35] proposed the phase space reconstruction theory for the first time.…”

Section: Introductionmentioning

confidence: 99%

Dynamic chaos of imaging measurements for characterizing gas–liquid nonlinear flow behaviour in a metallurgical reactor stirred by top‐blown air

Yang,

Zhang,

Yang

et al. 2023

Can J Chem Eng

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A modern chaos detection strategy for imaging measurements is proposed in this work to quantify the thermal mixing state quality in a metallurgical reactor stirred by top‐blown air. Specifically, the improved C‐C algorithm is proposed to reconstruct the phase space of the nonlinear time series of the bubble characteristics under thermal conditions. Moreover, a chaos decision tree algorithm is introduced to extract the chaotic mixing characteristics of the thermal two‐phase mixing system for the first time. Experimental and calculated results show that all the possible mixing states of the mixing system are visualized by reconstructing the phase space with the help of the visualization technique of the thermal gas–liquid two‐phase flow. It is found that the attractor of the nonlinear time series of bubbles exhibited more serious variations while the chosen working condition was optimal. Furthermore, the obtained parameter represents the chaotic characteristics of the thermal gas–liquid two‐phase mixing system which has chaotic characteristics under various experimental conditions. Hence, the new strategy would be helpful and effective in beneficial exploration for understanding the nonlinear intensification mechanism of metallurgical thermal processes.

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Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: Case studies of an offshore wind farm

Cited by 51 publications

References 42 publications

Two-stage robust optimization of regional integrated energy systems considering uncertainties of distributed energy stations

Two-stage robust optimization of regional integrated energy systems considering uncertainties of distributed energy stations

Wind Speed Prediction Based VMD-BLS and Error Compensation

Dynamic chaos of imaging measurements for characterizing gas–liquid nonlinear flow behaviour in a metallurgical reactor stirred by top‐blown air

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