Design methodology of intelligent autonomous distributed hybrid power complexes with renewable energy sources

Asanov, Murat; Asanova, Salima; Safaraliev, Murodbek; Zicmane, Inga; Beryozkina, Svetlana; Suerkulov, S. M.

doi:10.1016/j.ijhydene.2023.04.290

Cited by 4 publications

(1 citation statement)

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“…The power sector accounts for more than 1/3 of the national carbon emissions in China. Therefore, exploring and utilizing new energy sources to generate electricity have become an essential solution for the decarbonization plan [2]. Owing to the vulnerability of this power generation type to weather conditions and geographical location, there is increased randomness and complexity in the power system [3].…”

Section: Introduction 1background and Challengesmentioning

confidence: 99%

A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction

Xiao,

An,

Zhang

2024

Processes

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Adequate power load data are the basis for establishing an efficient and accurate forecasting model, which plays a crucial role in ensuring the reliable operation and effective management of a power system. However, the large-scale integration of renewable energy into the power grid has led to instabilities in power systems, and the load characteristics tend to be complex and diversified. Aiming at this problem, this paper proposes a short-term power load transfer forecasting method. To fully exploit the complex features present in the data, an online feature-extraction-based deep learning model is developed. This approach aims to extract the frequency-division features of the original power load on different time scales while reducing the feature redundancy. To solve the prediction challenges caused by insufficient historical power load data, the source domain model parameters are transferred to the target domain model utilizing Kendall’s correlation coefficient and the Bayesian optimization algorithm. To verify the prediction performance of the model, experiments are conducted on multiple datasets with different features. The simulation results show that the proposed model is robust and effective in load forecasting with limited data. Furthermore, if real-time data of new energy power systems can be acquired and utilized to update and correct the model in future research, this will help to adapt and integrate new energy sources and optimize energy management.

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Section: Introduction 1background and Challengesmentioning

confidence: 99%