Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data
Maneesha Perera(The University of Melbourne), Saman Halgamuge(The University of Melbourne), Damith Senanayake(The University of Melbourne), Kasun Bandara(Monash University), Julian de Hoog(The University of Melbourne)
Cited by 2
Related Papers
Multiobjective Evolution of Fuzzy Rough Neural Network via Distributed Parallelism for Stock Prediction
|IEEE Transactions on Fuzzy Systems|2020|215
Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data
|Applied Energy|2024|69
A Single-Channel Consumer-Grade EEG Device for Brain–Computer Interface: Enhancing Detection of SSVEP and Its Amplitude Modulation
|IEEE Sensors Journal|2019|53
Multi-resolution, multi-horizon distributed solar PV power forecasting with forecast combinations
|Expert Systems with Applications|2022|50
Indigenous peoples and artificial intelligence: A systematic review and future directions
|Big Data & Society|2025|35