Journal article
Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data
Maneesha Perera, Julian de Hoog, Kasun Bandara, Damith Senanayake, Saman Halgamuge
Applied Energy | Elsevier | Published : 2024
Abstract
Regional solar power forecasting, which involves predicting the total power generation from all rooftop photovoltaic (PV) systems in a region holds significant importance for various stakeholders in the energy sector to ensure a stable electricity supply. However, the vast amount of solar power generation and weather time series from geographically dispersed locations that need to be considered in the forecasting process makes accurate regional forecasting challenging. Therefore, previous studies have limited the focus to either forecasting a single time series (i.e., aggregated time series) which is the addition of all solar generation time series in a region, disregarding the location-spec..
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Awarded by Australian Research Council
Funding Acknowledgements
The authors are grateful to Solar Analytics for providing anonymised solar power generation data to conduct this research. This work was supported by the Melbourne Research Scholarship awarded to the first author. SH and JDH acknowledge Australian Research Council grant DP220101035. The authors acknowledge the support of Tamasha Malepathirana, Rashindrie Perera, Ken Chen and Will Bodewes for their valuable feedback and input on the paper. The research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne (this facility was established with the assistance of LIEF, Australia Grant LE170100200) .