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.

Related papers

• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• Combining Physics-based and Data-driven Modeling for Building Energy Systems [5.437298646956505]
  Building energy modeling plays a vital role in optimizing the operation of building energy systems. Researchers are combining physics-based and data-driven models into hybrid approaches. We evaluate four predominant hybrid approaches in building energy modeling through a real-world case study.
  arXiv  Detail & Related papers  (2024-11-01T21:56:39Z)
• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• 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)
• Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
  We propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
  arXiv  Detail & Related papers  (2024-06-20T02:20:23Z)
• Forecasting Intraday Power Output by a Set of PV Systems using Recurrent Neural Networks and Physical Covariates [0.0]
  Accurate forecasts of the power output by PhotoVoltaic (PV) systems are critical to improve the operation of energy distribution grids. We describe a neural autoregressive model that aims to perform such intraday forecasts.
  arXiv  Detail & Related papers  (2023-03-15T09:03:58Z)
• Evaluating generative models in high energy physics [7.545095780512178]
  We present the first systematic review and investigation into evaluation metrics and their sensitivity to failure modes of generative models. We propose two new metrics, the Fr'echet and kernel physics distances (FPD and KPD, respectively), and perform a variety of experiments measuring their performance. We demonstrate the efficacy of these proposed metrics in evaluating and comparing a novel attention-based generative adversarial particle transformer to the state-of-the-art message-passing generative adversarial network jet simulation model.
  arXiv  Detail & Related papers  (2022-11-18T15:36:28Z)
• 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)
• An Adaptive Deep Learning Framework for Day-ahead Forecasting of Photovoltaic Power Generation [0.8702432681310401]
  This paper proposes an adaptive LSTM (AD-LSTM) model, which is a DL framework that can not only acquire general knowledge from historical data, but also dynamically learn specific knowledge from newly-arrived data. The developed AD-LSTM model demonstrates greater forecasting capability than the offline LSTM model, particularly in the presence of concept drift.
  arXiv  Detail & Related papers  (2021-09-28T02:39:56Z)
• Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization [60.73540999409032]
  We show that expressive autoregressive dynamics models generate different dimensions of the next state and reward sequentially conditioned on previous dimensions. We also show that autoregressive dynamics models are useful for offline policy optimization by serving as a way to enrich the replay buffer.
  arXiv  Detail & Related papers  (2021-04-28T16:48:44Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)

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.