Sequence-to-Sequence Forecasting-aided State Estimation for Power Systems

• URL: http://arxiv.org/abs/2305.13215v1
• Date: Mon, 22 May 2023 16:46:37 GMT
• Title: Sequence-to-Sequence Forecasting-aided State Estimation for Power Systems
• Authors: Kamal Basulaiman, Masoud Barati
• Abstract summary: This paper proposes an end-to-end deep learning framework to accurately predict multi-step power system state estimations in real-time. Bidirectional gated recurrent units (BiGRUs) are incorporated into the model to achieve high prediction accuracy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Power system state forecasting has gained more attention in real-time operations recently. Unique challenges to energy systems are emerging with the massive deployment of renewable energy resources. As a result, power system state forecasting are becoming more crucial for monitoring, operating and securing modern power systems. This paper proposes an end-to-end deep learning framework to accurately predict multi-step power system state estimations in real-time. In our model, we employ a sequence-to-sequence framework to allow for multi-step forecasting. Bidirectional gated recurrent units (BiGRUs) are incorporated into the model to achieve high prediction accuracy. The dominant performance of our model is validated using real dataset. Experimental results show the superiority of our model in predictive power compared to existing alternatives.

Related papers

• Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamics [14.694200929205975]
  This paper introduces a novel methodology for trajectory forecasting based on state-space dynamic system modeling. To enhance the precision of state estimations within the dynamic system, the paper also presents a novel modeling technique for control variables. The proposed approach ingeniously integrates graph neural networks with state-space models, effectively capturing the complexities of multi-agent interactions.
  arXiv  Detail & Related papers  (2024-08-08T08:33:02Z)
• Weather Prediction with Diffusion Guided by Realistic Forecast Processes [49.07556359513563]
  We introduce a novel method that applies diffusion models (DM) for weather forecasting. Our method can achieve both direct and iterative forecasting with the same modeling framework. The flexibility and controllability of our model empowers a more trustworthy DL system for the general weather community.
  arXiv  Detail & Related papers  (2024-02-06T21:28:42Z)
• Deep Generative Methods for Producing Forecast Trajectories in Power Systems [0.0]
  Transport System Operators (TSOs) must conduct analyses to simulate the future functioning of power systems. These simulations are used as inputs in decision-making processes.
  arXiv  Detail & Related papers  (2023-09-26T14:43:01Z)
• Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets [74.51865676466056]
  We show how graph convolutional networks and hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning. Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency.
  arXiv  Detail & Related papers  (2022-11-14T01:29:09Z)
• 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)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)
• Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning [7.194382512848327]
  We propose a new parametrization to supervised learning on state-action data to stably predict at longer horizons. Our results in simulated and experimental robotic tasks show that our trajectory-based models yield significantly more accurate long term predictions.
  arXiv  Detail & Related papers  (2020-12-16T18:47:37Z)
• Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids [67.72249211312723]
  Real-time state estimation and forecasting is critical for efficient operation of power grids. PhI-GPR is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate both observed and unobserved states.
  arXiv  Detail & Related papers  (2020-10-09T14:18:31Z)
• 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)
• 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)

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.