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Artificial intelligence enabled microgrid power generation prediction
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AI
DOI：10.1515/comp-2024-0010.png)
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Abstract
En 中文
The rapidly increasing photovoltaic (PV) 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 article proposed machine learning-based short-term PV power generation forecasting techniques by using XGBoost, SARIMA, and long short-term memory network (LSTM) algorithms. The experimental results demonstrated that the proposed resilient LSTM solution can accurately predict (around 90% R 2 {R}<^>{2} and 0.028 root mean squared error) PV power generation with minimum input data.
Keywords:
PV generation
solar panel
machine learning
SARIMA
XGBoost
LSTM
Resilient
Journal
O
IF:
1.2
Papers:
7
Citations:
295