A physics-constrained deep learning framework enhanced with signal decomposition for accurate short-term photovoltaic power generation forecasting

Xifeng Gao, Yuesong Zang, Qian Ma, Mengmeng Liu, Yiming Cui and Dazhi Dang

Energy, 2025, vol. 326, issue C

Abstract: Accurate short-term forecasting of photovoltaic power generation is vital for maintaining the stability and efficiency of modern power systems. However, the variability and complexity of photovoltaic power, driven by meteorological factors, pose challenges for traditional models in achieving reliable forecasts. This study introduces a physics-constrained deep learning framework enhanced with signal decomposition to address these challenges. The framework employs complete ensemble empirical mode decomposition with adaptive noise to decompose photovoltaic power time series into intrinsic mode functions and a residual component, effectively extracting key dynamic features. These components are integrated with meteorological variables to construct a comprehensive feature matrix. A hybrid convolutional neural network-long short-term memory model captures spatial and temporal dependencies within the data. Furthermore, a customized photovoltaic power generation loss function, incorporating mean square error, regularization terms, and physical constraints, ensures the forecasts align with physical laws governing photovoltaic power generation. Evaluation results from extensive experiments demonstrate the framework's superior accuracy, robustness, and adherence to physical principles compared to baseline models. This work provides a novel and effective approach to enhancing photovoltaic power forecasting, supporting renewable energy integration into power grids, and improving overall system reliability.

Keywords: Photovoltaic power generation forecasting; CEEMDAN; CNN-LSTM; Physical constraints; Renewable energy (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225018626
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:326:y:2025:i:c:s0360544225018626

DOI: 10.1016/j.energy.2025.136220

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().