Abstract
In recent years, photovoltaic solar power plants have been widely adopted worldwide due to their favorable energy potential and accessibility. However, despite its accessibility, the output power characteristics of solar energy are inherently unstable. When integrating this energy generation into the power grid, the presence of unbalanced electric currents can adversely affect various control components of the system. Consequently, there is a growing demand for accurate short-term power forecasting. This paper introduces a novel hybrid model to address the aforementioned challenge. The proposed method encompasses three interconnected networks: Boost by Refinement (BF), Mixture of Experts (MoE), and Enhanced MoE (EMoE). Within these networks, the problem space is initially divided into distinct classes, which are then combined using a specific approach. The obtained results provide compelling evidence supporting the effectiveness and superiority of the proposed method.
Original language | English |
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Title of host publication | Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023 |
Editors | Zbigniew Leonowicz |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9798350347432 |
DOIs | |
Publication status | Published - 2023 |
Event | 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023 - Madrid, Spain Duration: Jun 6 2023 → Jun 9 2023 |
Publication series
Name | Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023 |
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Conference
Conference | 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023 |
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Country/Territory | Spain |
City | Madrid |
Period | 6/6/23 → 6/9/23 |
Funding
ACKNOWLEDGMENT The authors acknowledge the financial support of this study provided by the Collaborative Research Project (CRP) Grant of Nazarbayev University under grant no. (021220CRP0322).
Keywords
- hybrid model
- neural networks
- prediction
- solar energy
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering
- Safety, Risk, Reliability and Quality
- Environmental Engineering