| Title |
Ultra-short term PV power forecasting under diverse environmental conditions: a case study of Norway |
| Authors |
Das, Opy ; Dahlioui, Dounia ; Hamza Zafar, Muhammad ; Akhtar, Naureen ; Kumayl Raza Moosavi, Syed ; Sanfilippo, Filippo |
| DOI |
10.1016/j.ecmx.2025.101072 |
| Full Text |
|
| Is Part of |
Energy conversion and management: X.. Amsterdam : Elsevier. 2025, vol. 27, art. no. 101072, p. 208-221.. ISSN 2590-1745 |
| Keywords [eng] |
PV power forecasting ; Temporal attention ; Temporal data ; Ultra short term power forecasting |
| Abstract [eng] |
Accurate short-term solar forecasting is critical for power plant operations, grid balancing, real-time dispatching, automatic generation control, and energy trading. In Norway, where solar radiation is limited in winter and highly variable in summer, accurate predictions are essential. This study focuses on ultra-short-term forecasting of solar radiation and power output from a 37.8 kWp solar photovoltaic (PV) power plant at the University of Agder (UiA), Grimstad (58.335322°N, 8.577718°E), in southern Norway. We propose a novel forecasting model that integrates Spatial Attention, Temporal Attention, Self-Attention, CNN, and BiLSTM architectures to enhance prediction accuracy. Using a custom dataset collected from the UiA PV plant, the model's effectiveness was validated through comprehensive ablation studies and comparative analysis with state-of-the-art methods. The proposed model achieved a low RMSE of 0.162 kW using seven days of data, demonstrating its superiority in predicting short-term PV power outputs and associated uncertainties, outperforming conventional forecasting techniques. |
| Published |
Amsterdam : Elsevier |
| Type |
Journal article |
| Language |
English |
| Publication date |
2025 |
| CC license |
|
