Enhanced multi-horizon photovoltaic power forecasting: A novel approach integrating ICEEMDAN decomposition with hierarchical frequency neural networks
Yaopeng Han, Chenxi Li, Siqi Chen, Jinghao Zhao, Yajun Tian, Jun Wang, Sibarama Panigrahi, Sibarama Panigrahi, Sibarama Panigrahi
TL;DR
This paper introduces a new method for predicting solar power output using advanced data decomposition and neural networks, achieving higher accuracy than existing models.
Contribution
A novel hybrid model combining ICEEMDAN decomposition and hierarchical frequency neural networks with IRPE for improved photovoltaic forecasting.
Findings
The model achieves nMAE values of 0.1142 and 0.1490 for 120-minute and 2880-minute forecasts, outperforming baselines by 14.6% and 8.1%.
Statistical stability and robustness are confirmed through 30 Wilcoxon signed-rank tests and uncertainty analysis under varying weather conditions.
Abstract
As a crucial renewable energy source, solar PV power generation drives environmental protection and energy transformation. However, existing forecasting models struggle to accurately capture the complex dynamics of photovoltaic (PV) power, primarily due to monolithic modeling paradigms and inadequate representation of temporal information. To address these challenges, this paper proposes a novel hybrid model that leverages data decomposition and frequency-stratified prediction. The model employs the advanced ICEEMDAN algorithm to address complex non-stationarity. Additionally, it introduces a frequency-stratified heterogeneous network for precise component-wise modeling and integrates Improved Relative Positional Encoding (IRPE) to accurately capture temporal dependencies. To comprehensively evaluate model performance, this study employs quantile regression to generate probabilistic…
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Taxonomy
TopicsSolar Radiation and Photovoltaics · Energy Load and Power Forecasting · Photovoltaic System Optimization Techniques