Work

Ultra-short term PV power forecasting under diverse environmental conditions: A case study of Norway

Year: 2025

Type: article

Abstract: 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.

Source: Energy Conversion and Management X

DOI: 10.1016/j.ecmx.2025.101072

Authors: Opy Das, Dounia Dahlioui, Muhammad Hamza Zafar, Naureen Akhtar, Syed Kumayl Raza Moosavi, Filippo Sanfilippo

Institutions: University of Agder, Kaunas University of Technology

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,170