Long-term Power Generation Prediction in Photovoltaics Using Machine Learning-based Models

Romanian Journal of Information Science and Technology, Год журнала: 2025, Номер 28(1), С. 39 - 50

Опубликована: Март 14, 2025

The research in the field of renewable energy has taken centre stage study reliable and effective photovoltaic (PV) systems. These systems are essential to a future powered by energy, where solar radiation is directly converted into electrical power. However, arrays have limited conversion efficiency. Hence, highly accurate forecasting strategies required mitigate impact this challenge. This focuses on proposing serial algorithms that combine machine learning global optimization solve stochastic problems. Gated Recurrent Unit (GRU) architecture, Support Vector Machine (SVM) for Regression (SVR) models Differential Evolution algorithm (DE) used developing forecast grid power generation across environmental variations. Initially, four GRU-SVR will be trained address prediction seasonal evolution. Afterwards, hybrid approach GRU-SVR-DE strategy defined integrate models, providing robust PV generation. In end, performances predictions analyzed demonstrate accuracy long-term forecasts.

Язык: Английский

Quantifying spatiotemporal shifts in photovoltaic potential across China under 1.5 °C and 2.0 °C global warming scenarios

Applied Energy, Год журнала: 2025, Номер 392, С. 125957 - 125957

Опубликована: Май 4, 2025

Язык: Английский

Addressing Intermittency in Medium-Term Photovoltaic and Wind Power Forecasting Using a Hybrid xLSTM-TCCNN Model with Numerical Weather Predictions

Renewable Energy, Год журнала: 2025, Номер unknown, С. 123618 - 123618

Опубликована: Июнь 1, 2025

Язык: Английский