Forecasting Intraday Power Output by a Set of PV Systems using Recurrent Neural Networks and Physical Covariates

• URL: http://arxiv.org/abs/2303.08459v3
• Date: Wed, 28 Aug 2024 12:11:46 GMT
• Title: Forecasting Intraday Power Output by a Set of PV Systems using Recurrent Neural Networks and Physical Covariates
• Authors: Pierrick Bruneau, David Fiorelli, Christian Braun, Daniel Koster,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate intraday 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. We build upon a physical, deterministic PV performance model, the output of which is used as covariates in the context of the neural model. In addition, our application data relates to a geographically distributed set of PV systems. We address all PV sites with a single neural model, which embeds the information about the PV site in specific covariates. We use a scale-free approach which relies on the explicit modeling of seasonal effects. Our proposal repurposes a model initially used in the retail sector and discloses a novel truncated Gaussian output distribution. An ablation study and a comparison to alternative architectures from the literature shows that the components in the best performing proposed model variant work synergistically to reach a skill score of 15.72% with respect to the physical model, used as a baseline.

Related papers

• Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models [54.132297393662654]
  We introduce a hybrid method that fine-tunes cutting-edge diffusion models by optimizing reward models through RL. We demonstrate the capability of our approach to outperform the best designs in offline data, leveraging the extrapolation capabilities of reward models.
  arXiv  Detail & Related papers  (2024-05-30T03:57:29Z)
• A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks [0.0]
  We employ Bayesian neural networks to capture uncertainties in a reacting flow model. We demonstrate that BNN models can provide unique insights about the structure of uncertainty of the data-driven closure models. The efficacy of the model is demonstrated by a priori evaluation on a dataset consisting of a variety of flame conditions and fuels.
  arXiv  Detail & Related papers  (2024-02-28T22:19:55Z)
• 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)
• 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 Energy-Based Prior for Generative Saliency [62.79775297611203]
  We propose a novel generative saliency prediction framework that adopts an informative energy-based model as a prior distribution. With the generative saliency model, we can obtain a pixel-wise uncertainty map from an image, indicating model confidence in the saliency prediction. Experimental results show that our generative saliency model with an energy-based prior can achieve not only accurate saliency predictions but also reliable uncertainty maps consistent with human perception.
  arXiv  Detail & Related papers  (2022-04-19T10:51:00Z)
• 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)
• Deep Variational Models for Collaborative Filtering-based Recommender Systems [63.995130144110156]
  Deep learning provides accurate collaborative filtering models to improve recommender system results. Our proposed models apply the variational concept to injectity in the latent space of the deep architecture. Results show the superiority of the proposed approach in scenarios where the variational enrichment exceeds the injected noise effect.
  arXiv  Detail & Related papers  (2021-07-27T08:59:39Z)
• 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)
• Forecasting Photovoltaic Power Production using a Deep Learning Sequence to Sequence Model with Attention [0.0]
  We propose a supervised deep learning model for end-to-end forecasting of PV power production. The proposed model is based on two seminal concepts that led to significant performance improvements in other sequence-related fields. The results show that the new design performs at or above the current state of the art of PV power forecasting.
  arXiv  Detail & Related papers  (2020-08-06T17:20:08Z)
• VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry [70.10343492784465]
  It is necessary to expose to the process engineer, not solely the model predictions, but also their interpretability. Model-agnostic local interpretability solutions based on LIME have recently emerged to improve the original method. We present in this paper a novel approach, VAE-LIME, for local interpretability of data-driven models forecasting the temperature of the hot metal produced by a blast furnace.
  arXiv  Detail & Related papers  (2020-07-15T07:07:07Z)
• Energy Predictive Models for Convolutional Neural Networks on Mobile Platforms [0.0]
  Energy use is a key concern when deploying deep learning models on mobile devices. We build layer-type predictive models for the fully-connected and pooling layers using 12 representative Convolutional NeuralNetworks (ConvNets) on the Jetson TX1 and the Snapdragon 820. We obtain an accuracy between 76% to 85% and a model complexity of 1 for the overall energy prediction of the test ConvNets across different hardware-software combinations.
  arXiv  Detail & Related papers  (2020-04-10T17:35:40Z)

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.