Summary of Enhanced Photovoltaic Power Forecasting: An Itransformer and Lstm-based Model Integrating Temporal and Covariate Interactions, by Guang Wu et al.
Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based Model Integrating Temporal and Covariate Interactions
by Guang Wu, Yun Wang, Qian Zhou, Ziyang Zhang
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of accurate photovoltaic (PV) power forecasting for integrating renewable energy sources into the grid, optimizing real-time energy management, and ensuring energy reliability. Existing models struggle with capturing complex relationships between target variables and covariates, as well as interactions between temporal dynamics and multivariate data, leading to suboptimal forecasting accuracy. The proposed model architecture leverages iTransformer for feature extraction from target variables, LSTM for extracting features from covariates, a cross-attention mechanism to fuse outputs, and a Kolmogorov-Arnold network (KAN) mapping for enhanced representation. This approach is validated using publicly available datasets from Australia across four seasons, demonstrating improved forecasting accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making sure we can predict how much energy solar panels will produce accurately. Right now, the models we use aren’t doing a great job of this because they don’t understand the complex relationships between different things that affect energy production. The researchers came up with a new way to do this by combining different techniques together. They tested it using real data from Australia and found that it worked much better than before. |
Keywords
» Artificial intelligence » Cross attention » Feature extraction » Lstm
