PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids

• URL: http://arxiv.org/abs/2110.06324v1
• Date: Tue, 12 Oct 2021 20:18:49 GMT
• Title: PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids
• Authors: Xiangtian Zheng, Nan Xu, Loc Trinh, Dongqi Wu, Tong Huang, S. Sivaranjani, Yan Liu, Le Xie
• Abstract summary: PSML is a first-of-its-kind open-access multi-scale time-series dataset. We present PSML to aid in the development of data-driven machine learning (ML) approaches towards reliable operation of future electric grids. We envision this dataset will enable advances for ML in dynamic systems, while simultaneously allowing ML researchers to contribute towards carbon-neutral electricity and mobility.
• Score: 11.03026038752202
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable energy resources and electrified transportation, the reliable and secure operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML) based approaches towards reliable operation of future electric grids. The dataset is generated through a novel transmission + distribution (T+D) co-simulation designed to capture the increasingly important interactions and uncertainties of the grid dynamics, containing electric load, renewable generation, weather, voltage and current measurements at multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML baselines on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbance events; (ii) robust hierarchical forecasting of load and renewable energy with the presence of uncertainties and extreme events; and (iii) realistic synthetic generation of physical-law-constrained measurement time series. We envision that this dataset will enable advances for ML in dynamic systems, while simultaneously allowing ML researchers to contribute towards carbon-neutral electricity and mobility.

Related papers

• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Foundation Models for the Electric Power Grid [53.02072064670517]
  Foundation models (FMs) currently dominate news headlines. We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities. We discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit.
  arXiv  Detail & Related papers  (2024-07-12T17:09:47Z)
• Multiscale Spatio-Temporal Enhanced Short-term Load Forecasting of Electric Vehicle Charging Stations [4.239428835958199]
  The rapid expansion of electric vehicles (EVs) has rendered the load forecasting of electric vehicle charging stations (EVCS) increasingly critical. The primary challenge in achieving precise load forecasting for EVCS lies in accounting for the nonlinear of charging behaviors, the spatial interactions among different stations, and the intricate temporal variations in usage patterns. We propose a Multiscale Spatio-Temporal Enhanced Model (MSTEM) for effective load forecasting at EVCS.
  arXiv  Detail & Related papers  (2024-05-29T12:54:22Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Towards Energy-Aware Federated Traffic Prediction for Cellular Networks [2.360352205004026]
  We propose a novel sustainability indicator that allows assessing the feasibility of machine learning (ML) models. We evaluate state-of-the-art deep learning (DL) architectures in a federated scenario using real-world measurements from base station (BS) sites in the area of Barcelona, Spain. Our findings indicate that larger ML models achieve marginally improved performance but have a significant environmental impact in terms of carbon footprint.
  arXiv  Detail & Related papers  (2023-09-19T14:28:09Z)
• 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)
• Leveraging the Potential of Novel Data in Power Line Communication of Electricity Grids [0.5399800035598186]
  Electricity grids have become an essential part of daily life, even if they are often not noticed in everyday life. We propose two first-of-its-kind datasets based on measurements in a broadband powerline communications infrastructure. Both datasets FiN-1 and FiN-2 were collected during real practical use in a part of the German low-voltage grid that supplies around 4.4 million people.
  arXiv  Detail & Related papers  (2022-09-23T11:37:02Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• Combining Embeddings and Fuzzy Time Series for High-Dimensional Time Series Forecasting in Internet of Energy Applications [0.0]
  Fuzzy Time Series (FTS) models stand out as data-driven non-parametric models of easy implementation and high accuracy. We present a new methodology for handling high-dimensional time series, by projecting the original high-dimensional data into a low dimensional embedding space.
  arXiv  Detail & Related papers  (2021-12-03T19:50:09Z)
• Data-Driven Time Series Reconstruction for Modern Power Systems Research [11.447394702830408]
  This paper proposes a data-driven framework for reconstructing high-fidelity time series using publicly-available grid snapshots and historical data published by transmission system operators. Thereby, synthetic but highly realistic time series data, spanning multiple years with a 5-minute granularity, is generated at the individual component level.
  arXiv  Detail & Related papers  (2021-10-26T15:26:38Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)

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.