Validation Methods for Energy Time Series Scenarios from Deep Generative Models

• URL: http://arxiv.org/abs/2110.14451v1
• Date: Wed, 27 Oct 2021 14:14:25 GMT
• Title: Validation Methods for Energy Time Series Scenarios from Deep Generative Models
• Authors: Eike Cramer, Leonardo Rydin Gorj\~ao, Alexander Mitsos, Benjamin Sch\"afer, Dirk Witthaut, Manuel Dahmen
• Abstract summary: 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.
• Score: 55.41644538483948
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The design and operation of modern energy systems are heavily influenced by time-dependent and uncertain parameters, e.g., renewable electricity generation, load-demand, and electricity prices. These are typically represented by a set of discrete realizations known as scenarios. A popular scenario generation approach uses deep generative models (DGM) that allow scenario generation without prior assumptions about the data distribution. However, the validation of generated scenarios is difficult, and a comprehensive discussion about appropriate validation methods is currently lacking. To start this discussion, we provide a critical assessment of the currently used validation methods in the energy scenario generation literature. In particular, we assess validation methods based on probability density, auto-correlation, and power spectral density. Furthermore, we propose using the multifractal detrended fluctuation analysis (MFDFA) as an additional validation method for non-trivial features like peaks, bursts, and plateaus. As representative examples, we train generative adversarial networks (GANs), Wasserstein GANs (WGANs), and variational autoencoders (VAEs) on two renewable power generation time series (photovoltaic and wind from Germany in 2013 to 2015) and an intra-day electricity price time series form the European Energy Exchange in 2017 to 2019. 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. Our assessment shows that no single method sufficiently characterizes a scenario but ideally validation should include multiple methods and be interpreted carefully in the context of scenarios over short time periods.

Related papers

• GM-DF: Generalized Multi-Scenario Deepfake Detection [49.072106087564144]
  Existing face forgery detection usually follows the paradigm of training models in a single domain. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets.
  arXiv  Detail & Related papers  (2024-06-28T17:42:08Z)
• Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift [28.73747033245012]
  We introduce a universal calibration methodology for the detection and adaptation of context-driven distribution shifts. A novel CDS detector, termed the "residual-based CDS detector" or "Reconditionor", quantifies the model's vulnerability to CDS. A high Reconditionor score indicates a severe susceptibility, thereby necessitating model adaptation.
  arXiv  Detail & Related papers  (2023-10-23T11:58:01Z)
• 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)
• Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning [71.24316734338501]
  We propose an effective temporally-conditional diffusion model coined Temporally-Composable diffuser (TCD) TCD extracts temporal information from interaction sequences and explicitly guides generation with temporal conditions. Our method reaches or matches the best performance compared with prior SOTA baselines.
  arXiv  Detail & Related papers  (2023-06-08T02:12:26Z)
• Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand [7.544120398993689]
  Masked Multi-Step Multi Probabilistic Forecasting (MMMPF) is a novel and general framework to train any neural network model. It combines both the temporal information from the past and the known information about the future to make probabilistic predictions. MMMPF can also generate desired quantiles to capture uncertainty and enable probabilistic planning for grid of the future.
  arXiv  Detail & Related papers  (2023-02-14T04:09:03Z)
• Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings [57.133639209759615]
  We interpret sequences as energy-based sequence models and propose two energy parametrizations derivable from traineds. We develop a tractable emph scheme based on the Metropolis-Hastings Monte Carlo algorithm. We validate the effectiveness of the proposed parametrizations by exploring the quality of samples drawn from these energy-based models.
  arXiv  Detail & Related papers  (2021-06-04T22:04:30Z)
• Principal Component Density Estimation for Scenario Generation Using Normalizing Flows [62.997667081978825]
  We propose a dimensionality-reducing flow layer based on the linear principal component analysis (PCA) that sets up the normalizing flow in a lower-dimensional space. We train the resulting principal component flow (PCF) on data of PV and wind power generation as well as load demand in Germany in the years 2013 to 2015.
  arXiv  Detail & Related papers  (2021-04-21T08:42:54Z)
• 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)
• Generating Long-term Continuous Multi-type Generation Profiles using Generative Adversarial Network [7.234117485816439]
  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)
  arXiv  Detail & Related papers  (2020-12-22T20:58:32Z)
• Probabilistic Load Forecasting Based on Adaptive Online Learning [7.373617024876726]
  This paper presents a method for probabilistic load forecasting based on the adaptive online learning of hidden Markov models. We propose learning and forecasting techniques with theoretical guarantees, and experimentally assess their performance in multiple scenarios. The results show that the proposed method can significantly improve the performance of existing techniques for a wide range of scenarios.
  arXiv  Detail & Related papers  (2020-11-30T12:02:26Z)

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.