Hourly photovoltaic power forecasting framework based on multi-scale convolutions and parallel stacking
Authors:
- Linfei YinLinfei Yin
- Yiling ZhangYZYiling Zhang
- 12 May 2026
- journal article
- Published by Elsevier in Electric Power Systems Research
- Vol. 259
Abstract
No abstract availableFunding Information
- National Natural Science Foundation of China (62463001)
This publication has 29 references indexed in Scilit:
- Short- and medium-term horizon photovoltaic power forecasting based on terrestrial solar radiation featuresEngineering Applications of Artificial Intelligence, 2025
- Optimizing deep learning predictive models: A comprehensive review of RNN and its variant architecturesApplied Soft Computing, 2025
- Multi-scale fused Graph Convolutional Network for multi-site photovoltaic power forecastingEnergy Conversion and Management, 2025
- Dynamic stacking ensemble hybrid model for enhanced short-term photovoltaic power forecasting with self-organizing maps and advanced deep learningEnergy Reports, 2025
- An adaptive weighted stacking ensemble framework for photovoltaic power generation forecasting with joint optimization of features and hyperparametersEngineering Applications of Artificial Intelligence, 2025
- Short-term PV-Wind forecasting of large-scale regional site clusters based on FCM clustering and hybrid Inception-ResNet embedded with InformerEnergy Conversion and Management, 2024
- Efficient shrinkage temporal convolutional network model for photovoltaic power predictionEnergy, 2024
- A review of bifacial solar photovoltaic applicationsFrontiers in Energy, 2023
- Weighted fully-connected regression networks for one-day-ahead hourly photovoltaic power forecastingApplied Energy, 2022
- Measured and forecasted weather and power dataset for management of an island and grid-connected microgridData in Brief, 2021