Effect Evaluation and Intelligent Prediction of Power Substation Project Considering New Energy
- Huiying Wu*, Meihua Zou, Ye Ke, Wenqi Ou, Yonghong Li, Minquan Ye
State Grid Fujian Economic Research Institute, Fuzhou, 350000, China
* Corresponding Author: Huiying Wu. Email:
(This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
* Corresponding Author: Huiying Wu. Email:
(This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)
Received 10 October 2021; Accepted 24 December 2021; Issue published 27 June 2022
Abstract
The evaluation of the implementation effect of the power substation project can find out the problems of the project more comprehensively, which has important practical significance for the further development of the power substation project. To ensure accuracy and real-time evaluation, this paper proposes a novel hybrid intelligent evaluation and prediction model based on improved TOPSIS and Long Short-Term Memory (LSTM) optimized by a Sperm Whale Algorithm (SWA). Firstly, under the background of considering the development of new energy, the influencing factors of power substation project implementation effect are analyzed from three aspects of technology, economy and society. Moreover, an evaluation model based on improved TOPSIS is constructed. Then, an intelligent prediction model based on SWA optimized LSTM is designed. Finally, the scientificity and accuracy of the proposed model are verified by empirical analysis, and the important factors affecting the implementation effect of power substation projects are pointed out.
Keywords
New energy; substation; implementation effect; evaluation and intelligent prediction; improved topsis; LSTM; SWA
Cite This Article
Wu, H., Zou, M., Ke, Y., Ou, W., Li, Y. et al. (2022). Effect Evaluation and Intelligent Prediction of Power Substation Project Considering New Energy. CMES-Computer Modeling in Engineering & Sciences, 132(3), 739–761.
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