MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV
Generation Forecasting
- URL: http://arxiv.org/abs/2306.10356v2
- Date: Sat, 2 Mar 2024 08:51:25 GMT
- Title: MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV
Generation Forecasting
- Authors: Matteo Tortora, Francesco Conte, Gianluca Natrella, Paolo Soda
- Abstract summary: 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.
- Score: 0.47518865271427785
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Accurate forecasting of renewable generation is crucial to facilitate the
integration of RES into the power system. Focusing on PV units, forecasting
methods can be divided into two main categories: physics-based and data-based
strategies, with AI-based models providing state-of-the-art performance.
However, while these AI-based models can capture complex patterns and
relationships in the data, they ignore the underlying physical prior knowledge
of the phenomenon. Therefore, in this paper we propose MATNet, a novel
self-attention transformer-based architecture for multivariate multi-step
day-ahead PV power generation forecasting. It consists of a hybrid approach
that combines the AI paradigm with the prior physical knowledge of PV power
generation of physics-based methods. The model is fed with historical PV data
and historical and forecast weather data through a multi-level joint fusion
approach. The effectiveness of the proposed model is evaluated using the
Ausgrid benchmark dataset with different regression performance metrics. The
results show that our proposed architecture significantly outperforms the
current state-of-the-art methods. These findings demonstrate the potential of
MATNet in improving forecasting accuracy and suggest that it could be a
promising solution to facilitate the integration of PV energy into the power
grid.
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