A Deep Learning Method for Photovoltaic Power Generation Forecasting Based on a Time-Series Dense Encoder

A Deep Learning Method for Photovoltaic Power Generation Forecasting Based on a Time-Series Dense Encoder

Deep learning has become a widely used approach in photovoltaic (PV) power generation forecasting due to its strong self-learning and parameter optimization capabilities. In this study, we apply a deep learning algorithm, known as the time-series dense encoder (TiDE), which is an MLP-based encoder–d...

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Bibliographic Details
Main Authors: Xingfa Zi, Feiyi Liu, Mingyang Liu, Yang Wang
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Energies
Subjects:
photovoltaic energy
renewable energy
PV power generation forecasting
time-series forecasting
deep learning
time-series dense encoder
Online Access:https://www.mdpi.com/1996-1073/18/10/2434
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