Alapfogalmak
The author proposes two new deep-learning-based regional forecasting methods using hierarchical temporal convolutional neural networks to effectively leverage both aggregated and individual time series data with weather data in a region.
Kivonat
Regional solar power forecasting is crucial for ensuring stable electricity supply. The study introduces innovative deep learning methods to forecast solar power generation, comparing them with traditional models. Data from 101 locations in Western Australia is used to provide accurate day-ahead forecasts.
The study addresses the challenges of accurately forecasting regional solar power generation by leveraging both aggregated and individual time series data. Two strategies are proposed: direct regional forecasting and sub-region aggregation forecasting. The proposed approaches outperform traditional methods, reducing forecast errors significantly.
Deep learning methods like TCNs and LSTMs are compared with baseline models like SARIMA and Seasonal Naive for solar power forecasting. The research focuses on the South West Interconnected System in Western Australia, where rooftop solar installations are rapidly increasing.
The study highlights the importance of considering local weather conditions when forecasting solar power generation across large regions. By utilizing advanced deep learning techniques, more accurate forecasts can be achieved while reducing computational complexity.
Statisztikák
The sub-region-based HTCNN approach achieves a forecast skill score of 40.2%.
A statistically significant forecast error reduction of 6.5% is observed compared to the best-performing counterpart.