Interval Prediction Method for Wind Speed Based on ARQEA Optimized by Beta Distribution and SWLSTM

Sun, Zengqi; Shen, Yongshan; Chen, Zhe; Teng, Yan; Xia, Qian

doi:10.3389/fenrg.2022.927260

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…In particular, China's total installed capacity accounted for 38.5% of global wind power capacity with the largest proportion (Wang et al, 2021). Despite such surprising growth, the randomness, uncertainty and intermittency of wind greatly affect the stable operation of large-scale power grid-integrated systems (Sun et al, 2022). An effective way to address this issue is to accurately predict wind speed.…”

Section: Introductionmentioning

confidence: 99%

Wind speed prediction model using ensemble empirical mode decomposition, least squares support vector machine and long short-term memory

Li²,

Xu³

2023

Front. Energy Res.

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Due to the randomness and intermittency of wind, accurate and reliable wind speed prediction is of great importance to the safe and stable operation of power grid. In this paper, a novel hybrid wind speed forecasting model based on EEMD (Ensemble Empirical Mode Decomposition), LSSVM (Least Squares Support Vector Machine), and LSTM (Long Short-Term Memory) is proposed, aiming at enhancing the forecasting accuracy of wind speed. The original data series is firstly processed by EEMD and SE into a series of components with different frequencies. Subsequently, a combined mechanism composed of LSSVM and LSTM is presented to train and predict the high-frequency and low-frequency sequences, respectively. Finally, the predicted values of all the data sequences are superimposed to obtain the ultimate wind speed forecasting results. In order to respectively illustrate the superiority of data feature processing and combined prediction mechanism in the proposed model, two experiments are performed on the two wind speed datasets. In accordance with the four performance metrics of the forecasting results, the EEMD-LSTM-LSSVM model obtains a higher accuracy in wind speed prediction task.

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Section: Introductionmentioning

confidence: 99%

Wind speed prediction model using ensemble empirical mode decomposition, least squares support vector machine and long short-term memory

Li²,

Xu³

2023

Front. Energy Res.

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“…V2G (vehicle to grid) technology can achieve the interaction between electric vehicles and the power grid [25]. In reference [26], considering the uncertainty of electric vehicles in the microgrid environment, planning research was conducted with the goal of minimizing the cost of the microgrid system.…”

Section: Introductionmentioning

confidence: 99%

Low-Carbon Optimal Scheduling Model for Peak Shaving Resources in Multi-Energy Power Systems Considering Large-Scale Access for Electric Vehicles

Dai

Zhang

et al. 2023

Processes

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Aiming at the synergy between a system’s carbon emission reduction demand and the economy of peak shaving operation in the process of optimizing the flexible resource peaking unit portfolio of a multi-energy power system containing large-scale electric vehicles, this paper proposes a low-carbon optimal scheduling model for peak shaving resources in multi-energy power systems considering large-scale access for electric vehicles. Firstly, the charging and discharging characteristics of electric vehicles were studied, and a comprehensive cost model for electric vehicles, heat storage, and hydrogen storage was established. At the same time, the carbon emission characteristics of multi-energy power systems and their emission cost models under specific carbon trading mechanisms were established. Secondly, the change characteristics of the system’s carbon emissions were studied, and a carbon emission cost model of multi-energy power was established considering the carbon emission reduction demand of the system. Then, taking the carbon emission of the system and the peak regulating operation costs of traditional units, energy storage, and new energy unit as optimization objectives, the multi-energy power system peak regulation multi-objective optimization scheduling model was established, and NSGA-II was used to solve the scheduling model. Finally, based on a regional power grid data in Northeast China, the improved IEEE 30 node multi-energy power system peak shaving simulation model was built, and the simulation analysis verified the feasibility of the optimal scheduling model proposed in this paper.

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Optimizing Hydrogen-Integrated Micro-CHP Systems with Demand Response for Sustainable Energy Management

Yu,

Li,

Pan

2024

Journal of Building Engineering

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Interval Prediction Method for Wind Speed Based on ARQEA Optimized by Beta Distribution and SWLSTM

Cited by 3 publications

References 27 publications

Wind speed prediction model using ensemble empirical mode decomposition, least squares support vector machine and long short-term memory

Wind speed prediction model using ensemble empirical mode decomposition, least squares support vector machine and long short-term memory

Low-Carbon Optimal Scheduling Model for Peak Shaving Resources in Multi-Energy Power Systems Considering Large-Scale Access for Electric Vehicles

Optimizing Hydrogen-Integrated Micro-CHP Systems with Demand Response for Sustainable Energy Management

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