Wang, Xueyi, Li, Shancang and Iqbal, Muddesar
2025.
Artificial intelligence enabled microgrid power generation prediction.
Open Computer Science
15
(1)
, 20240010.
10.1515/comp-2024-0010
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Abstract
The rapidly increasing photovoltaic technology is one of the key renewable energies expected to mitigate the impact of climate change and the energy crisis, which has been widely installed in the past few years. However, the variability of PV power generation creates different negative impacts on the electric grid systems and a resilient and predictable PV power generation is crucial to stabilize and secure grid operation and promote large-scale PV power integration. This paper proposed machine learning based short-term PV power generation forecasting techniques by using XGBoost, SARIMA, and LSTM algorithms. {The experimental results demonstrated that the proposed resilient LSTM solution can accurately predict (around 90% R2 and 0.028 root mean squared error) PV power generation with minimum input data.
| Item Type: | Article |
|---|---|
| Date Type: | Published Online |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | De Gruyter |
| Date of First Compliant Deposit: | 28 November 2024 |
| Date of Acceptance: | 28 October 2024 |
| Last Modified: | 28 Oct 2025 11:46 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/169210 |
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