Generating Long-term Continuous Multi-type Generation Profiles using Generative Adversarial Network

• URL: http://arxiv.org/abs/2012.13344v2
• Date: Wed, 24 Feb 2021 14:18:13 GMT
• Title: Generating Long-term Continuous Multi-type Generation Profiles using Generative Adversarial Network
• Authors: Ming Dong, Kaigui Xie, Wenyuan Li
• Abstract summary: The adoption of new technologies has increased power system dynamics significantly. Traditional long-term planning studies cannot reflect system dynamics and often fail to accurately predict system reliability deficiencies. This paper proposes a completely novel approach to generate such profiles for multiple generation types using Generative Adversarial Networks (GAN)
• Score: 7.234117485816439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Today, the adoption of new technologies has increased power system dynamics significantly. Traditional long-term planning studies that most utility companies perform based on discrete power levels such as peak or average values cannot reflect system dynamics and often fail to accurately predict system reliability deficiencies. As a result, long-term future continuous profiles such as the 8760 hourly profiles are required to enable time-series based long-term planning studies. However, unlike short-term profiles used for operation studies, generating long-term continuous profiles that can reflect both historical time-varying characteristics and future expected power magnitude is very challenging. Current methods such as average profiling have major drawbacks. To solve this challenge, this paper proposes a completely novel approach to generate such profiles for multiple generation types using Generative Adversarial Networks (GAN). A multi-level profile synthesis process is proposed to capture time-varying characteristics at different time levels. Both Single-type GAN and a modified Conditional GAN systems are developed. Unique profile evaluation metrics are proposed. The proposed approach was evaluated based on a public dataset and demonstrated great performance and application value for generating long-term continuous multi-type generation profiles.

Related papers

• Recurrent Neural Goodness-of-Fit Test for Time Series [8.22915954499148]
  Time series data are crucial across diverse domains such as finance and healthcare. Traditional evaluation metrics fall short due to the temporal dependencies and potential high dimensionality of the features. We propose the REcurrent NeurAL (RENAL) Goodness-of-Fit test, a novel and statistically rigorous framework for evaluating generative time series models.
  arXiv  Detail & Related papers  (2024-10-17T19:32:25Z)
• Timer-XL: Long-Context Transformers for Unified Time Series Forecasting [67.83502953961505]
  We present Timer-XL, a generative Transformer for unified time series forecasting. Timer-XL achieves state-of-the-art performance across challenging forecasting benchmarks through a unified approach.
  arXiv  Detail & Related papers  (2024-10-07T07:27:39Z)
• Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting [19.426131129034115]
  This paper proposes a novel framework leveraging self-supervised multiscale representation learning to capture both long-term and near-term workload patterns. The long-term history is encoded through multiscale representations while the near-term observations are modeled via temporal flow fusion.
  arXiv  Detail & Related papers  (2024-07-29T04:42:18Z)
• 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)
• Multi-Scale Dilated Convolution Network for Long-Term Time Series Forecasting [17.132063819650355]
  We propose Multi Scale Dilated Convolution Network (MSDCN) to capture the period and trend characteristics of long time series. We design different convolution blocks with exponentially growing dilations and varying kernel sizes to sample time series data at different scales. To validate the effectiveness of the proposed approach, we conduct experiments on eight challenging long-term time series forecasting benchmark datasets.
  arXiv  Detail & Related papers  (2024-05-09T02:11:01Z)
• PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations [49.80959046861793]
  We present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers. Our experimentation across seven diversetemporal real-world LMTF datasets reveals that PDETime adapts effectively to the intrinsic nature of the data.
  arXiv  Detail & Related papers  (2024-02-25T17:39:44Z)
• TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting [24.834846119163885]
  We propose a novel framework, TEMPO, that can effectively learn time series representations. TEMPO expands the capability for dynamically modeling real-world temporal phenomena from data within diverse domains.
  arXiv  Detail & Related papers  (2023-10-08T00:02:25Z)
• MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting [13.790498420659636]
  Time series forecasting has received wide interest from existing research due to its broad applications inherent challenging. This paper proposes a forecasting model, MPR-Net. It first adaptively decomposes multi-scale historical series patterns using convolution operation, then constructs a pattern extension forecasting method based on the prior knowledge of pattern reproduction, and finally reconstructs future patterns into future series using deconvolution operation. By leveraging the temporal dependencies present in the time series, MPR-Net not only achieves linear time complexity, but also makes the forecasting process interpretable.
  arXiv  Detail & Related papers  (2023-07-13T13:16:01Z)
• Validation Methods for Energy Time Series Scenarios from Deep Generative Models [55.41644538483948]
  A popular scenario generation approach uses deep generative models (DGM) that allow scenario generation without prior assumptions about the data distribution. We provide a critical assessment of the currently used validation methods in the energy scenario generation literature. We apply the four validation methods to both the historical and the generated data and discuss the interpretation of validation results as well as common mistakes, pitfalls, and limitations of the validation methods.
  arXiv  Detail & Related papers  (2021-10-27T14:14:25Z)
• Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series Modeling [68.8204255655161]
  We propose an artificial neural network with a mechanism to implicitly learn the phase spaces properties. Our approach is either as competitive as or better than most state-of-the-art strategies.
  arXiv  Detail & Related papers  (2020-06-19T21:04:47Z)
• Transformer Hawkes Process [79.16290557505211]
  We propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies. THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.
  arXiv  Detail & Related papers  (2020-02-21T13:48:13Z)

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.