<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, the constraints faced by the power sector in terms of energy, economy and ecology are first studied in depth, and the relationship between investment efficiency and new power development is explored by using the Data Envelopment Analysis (DEA) method. Secondly, the parameters of the least squares support vector machine (LSSVM) model are optimized based on phase space reconstruction and the artificial bee colony (ABC) algorithm, and an optimized ABC- LSSVM wind abandonment power assessment model is proposed. Finally, the proposed wind power abandonment assessment model’s accuracy and precision are verified by comparing it to the traditional maximum probability method. The results show that the evaluation result obtained by using the traditional maximum probability method is 4580.15 MWh, and the evaluation result of the ABC-LSSVM algorithm is 5123.12 MWh, while the actual output power is calculated as 4975.63 MWh, which indicates that the ABC-LSSVM algorithm is better than the traditional maximum probability method model in the evaluation of wind power abandonment. This paper accurately evaluates the wind power abandoned by wind turbines, which is of guiding significance for realizing wind reuse and rational planning of the power grid.</p>
</abstract>

In this paper, the constraints faced by the power sector in terms of energy, economy and ecology are first studied in depth, and the relationship between investment efficiency and new power development is explored by using the Data Envelopment Analysis (DEA) method. Secondly, the parameters of the least squares support vector machine (LSSVM) model are optimized based on phase space reconstruction and the artificial bee colony (ABC) algorithm, and an optimized ABC- LSSVM wind abandonment power assessment model is proposed. Finally, the proposed wind power abandonment assessment model’s accuracy and precision are verified by comparing it to the traditional maximum probability method. The results show that the evaluation result obtained by using the traditional maximum probability method is 4580.15 MWh, and the evaluation result of the ABC-LSSVM algorithm is 5123.12 MWh, while the actual output power is calculated as 4975.63 MWh, which indicates that the ABC-LSSVM algorithm is better than the traditional maximum probability method model in the evaluation of wind power abandonment. This paper accurately evaluates the wind power abandoned by wind turbines, which is of guiding significance for realizing wind reuse and rational planning of the power grid.

Modelling and implementation mechanisms for power assessment under ecological environment and sustainable development

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, the constraints faced by the power sector in terms of energy, economy and ecology are first studied in depth, and the relationship between...

Modelling and implementation mechanisms for power assessment under ecological environment and sustainable development

Publicado en línea: 08 nov 2023

Páginas: -

Recibido: 28 nov 2022

Aceptado: 19 may 2023

DOI: https://doi.org/10.2478/amns.2023.2.01062

Palabras clave
Ecology, Data envelopment analysis, ABC- LSSVM, Wind power abandonment assessment, Phase space reconstruction

© 2023 Qiujia Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Modelling and implementation mechanisms for power assessment under ecological environment and sustainable development

Publicado en línea: 08 nov 2023

Páginas: -

Recibido: 28 nov 2022

Aceptado: 19 may 2023

DOI: https://doi.org/10.2478/amns.2023.2.01062

Palabras claveEcology, Data envelopment analysis, ABC- LSSVM, Wind power abandonment assessment, Phase space reconstruction

© 2023 Qiujia Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Ecology, Data envelopment analysis, ABC- LSSVM, Wind power abandonment assessment, Phase space reconstruction