Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based Model Integrating Temporal and Covariate Interactions

• URL: http://arxiv.org/abs/2412.02302v1
• Date: Tue, 03 Dec 2024 09:16:13 GMT
• Title: Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based Model Integrating Temporal and Covariate Interactions
• Authors: Guang Wu, Yun Wang, Qian Zhou, Ziyang Zhang,
• Abstract summary: Existing models often struggle with capturing the complex relationships between target variables and covariates. We propose a novel model architecture that leverages the iTransformer for feature extraction from target variables. A cross-attention mechanism is integrated to fuse the outputs of both models, followed by a Kolmogorov-Arnold network mapping. Results demonstrate that the proposed model effectively capture seasonal variations in PV power generation and improve forecasting accuracy.
• Score: 16.705621552594643
• License:
• Abstract: Accurate photovoltaic (PV) power forecasting is critical for integrating renewable energy sources into the grid, optimizing real-time energy management, and ensuring energy reliability amidst increasing demand. However, existing models often struggle with effectively capturing the complex relationships between target variables and covariates, as well as the interactions between temporal dynamics and multivariate data, leading to suboptimal forecasting accuracy. To address these challenges, we propose a novel model architecture that leverages the iTransformer for feature extraction from target variables and employs long short-term memory (LSTM) to extract features from covariates. A cross-attention mechanism is integrated to fuse the outputs of both models, followed by a Kolmogorov-Arnold network (KAN) mapping for enhanced representation. The effectiveness of the proposed model is validated using publicly available datasets from Australia, with experiments conducted across four seasons. Results demonstrate that the proposed model effectively capture seasonal variations in PV power generation and improve forecasting accuracy.

Related papers

• Integrating Physics and Data-Driven Approaches: An Explainable and Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction [1.1270209626877075]
  The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations. Traditional empirical and physics-based models offer approximate predictions of power generation based on wind speed. Data-driven machine learning methods present a promising avenue for improving wind turbine modeling.
  arXiv  Detail & Related papers  (2025-02-11T08:16:48Z)
• Powerformer: A Transformer with Weighted Causal Attention for Time-series Forecasting [50.298817606660826]
  We introduce Powerformer, a novel Transformer variant that replaces noncausal attention weights with causal weights that are reweighted according to a smooth heavy-tailed decay. Our empirical results demonstrate that Powerformer achieves state-of-the-art accuracy on public time-series benchmarks. Our analyses show that the model's locality bias is amplified during training, demonstrating an interplay between time-series data and power-law-based attention.
  arXiv  Detail & Related papers  (2025-02-10T04:42:11Z)
• Ister: Inverted Seasonal-Trend Decomposition Transformer for Explainable Multivariate Time Series Forecasting [10.32586981170693]
  Inverted Seasonal-Trend Decomposition Transformer (Ister) We introduce a novel Dot-attention mechanism that improves interpretability, computational efficiency, and predictive accuracy. Ister enables intuitive visualization of component contributions, shedding lights on model's decision process and enhancing transparency in prediction results.
  arXiv  Detail & Related papers  (2024-12-25T06:37:19Z)
• Energy-Based Diffusion Language Models for Text Generation [126.23425882687195]
  Energy-based Diffusion Language Model (EDLM) is an energy-based model operating at the full sequence level for each diffusion step. Our framework offers a 1.3$times$ sampling speedup over existing diffusion models.
  arXiv  Detail & Related papers  (2024-10-28T17:25:56Z)
• Weather-Informed Probabilistic Forecasting and Scenario Generation in Power Systems [15.393565192962482]
  Integration of renewable energy sources into power grids presents significant challenges due to their intrinsicity and uncertainty. This paper proposes a method combining probabilistic forecasting and Gaussian copula for day-ahead prediction and scenario generation of wind, and solar power in high-dimensional contexts.
  arXiv  Detail & Related papers  (2024-09-11T21:44:59Z)
• MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting [0.47518865271427785]
  MATNet is a novel self-attention transformer-based architecture for PV power generation forecasting. It consists of a hybrid approach that combines the AI paradigm with the prior physical knowledge of PV power generation. Results show that our proposed architecture significantly outperforms the current state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-06-17T14:03:09Z)
• Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer [112.12271800369741]
  Wind power is attracting increasing attention around the world due to its renewable, pollution-free, and other advantages. Accurate wind power forecasting (WPF) can effectively reduce power fluctuations in power system operations. Existing methods are mainly designed for short-term predictions and lack effective spatial-temporal feature augmentation.
  arXiv  Detail & Related papers  (2023-05-30T04:03:15Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Cascaded Deep Hybrid Models for Multistep Household Energy Consumption Forecasting [5.478764356647437]
  This study introduces two hybrid cascaded models for forecasting multistep household power consumption in different resolutions. The proposed hybrid models achieve superior prediction performance compared to the existing multistep power consumption prediction methods.
  arXiv  Detail & Related papers  (2022-07-06T11:02:23Z)
• Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One [83.5162421521224]
  We propose a unique method termed E-ARM for training autoregressive generative models. E-ARM takes advantage of a well-designed energy-based learning objective. We show that E-ARM can be trained efficiently and is capable of alleviating the exposure bias problem.
  arXiv  Detail & Related papers  (2022-06-26T10:58:41Z)
• Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction [51.80191416661064]
  We propose a novel vision transformer with latent variables following an informative energy-based prior for salient object detection. Both the vision transformer network and the energy-based prior model are jointly trained via Markov chain Monte Carlo-based maximum likelihood estimation. With the generative vision transformer, we can easily obtain a pixel-wise uncertainty map from an image, which indicates the model confidence in predicting saliency from the image.
  arXiv  Detail & Related papers  (2021-12-27T06:04:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.