Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows

• URL: http://arxiv.org/abs/2204.13939v3
• Date: Thu, 15 Jun 2023 13:23:30 GMT
• Title: Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows
• Authors: Marcel Arpogaus, Marcus Voss, Beate Sick, Mark Nigge-Uricher, Oliver D\"urr
• Abstract summary: High fluctuations and increasing electrification cause huge forecast variability, not reflected in traditional point estimates. We propose an approach for flexible conditional density forecasting of short-term load based on normalizing flows.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing electrification cause huge forecast variability, not reflected in traditional point estimates. Probabilistic load forecasts take future uncertainties into account and thus allow more informed decision-making for the planning and operation of low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein polynomial normalizing flows, where a neural network controls the parameters of the flow. In an empirical study with 363 smart meter customers, our density predictions compare favorably against Gaussian and Gaussian mixture densities. Also, they outperform a non-parametric approach based on the pinball loss for 24h-ahead load forecasting for two different neural network architectures.

Related papers

• Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand [8.068451210598678]
  Power systems operate under uncertainty originating from multiple factors that are impossible to account for deterministically. Recent progress in deep learning has helped to significantly improve the accuracy of point forecasts. We propose a novel general approach for distributional forecasting capable of predicting arbitrary quantiles.
  arXiv  Detail & Related papers  (2024-04-26T14:43:19Z)
• Short-Term Load Forecasting Using A Particle-Swarm Optimized Multi-Head Attention-Augmented CNN-LSTM Network [0.0]
  Short-term load forecasting is of paramount importance in the efficient operation and planning of power systems. Recent strides in deep learning have shown promise in addressing this challenge. I propose a novel solution that surmounts these obstacles.
  arXiv  Detail & Related papers  (2023-09-07T13:06:52Z)
• Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing Uncertainty [125.41260574344933]
  We consider an encoder-decoder based deep ensemble network for capturing both perception and predictive uncertainty simultaneously. Overall, deep ensembles provided more robust predictions and the consideration of upstream uncertainty further increased the estimation accuracy for the model.
  arXiv  Detail & Related papers  (2023-05-26T04:27:48Z)
• Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach [5.718294641082287]
  We propose a forecasting scheme that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting. This paper uses the deep deterministic policy gradient (DDPG) algorithm as the basic forecasting model to improve prediction accuracy. The proposed FedDRL can obtain an accurate prediction model in a decentralized way by sharing model parameters instead of sharing private data.
  arXiv  Detail & Related papers  (2022-11-02T08:36:32Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• Dense Uncertainty Estimation [62.23555922631451]
  In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation. We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
  arXiv  Detail & Related papers  (2021-10-13T01:23:48Z)
• 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)
• Probabilistic electric load forecasting through Bayesian Mixture Density Networks [70.50488907591463]
  Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids. We propose a novel PLF approach, framed on Bayesian Mixture Density Networks. To achieve reliable and computationally scalable estimators of the posterior distributions, both Mean Field variational inference and deep ensembles are integrated.
  arXiv  Detail & Related papers  (2020-12-23T16:21:34Z)
• 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)
• N-BEATS neural network for mid-term electricity load forecasting [8.430502131775722]
  We show that our proposed deep neural network modeling approach is effective at solving the mid-term electricity load forecasting problem. It is simple to implement and train, it does not require signal preprocessing, and it is equipped with a forecast bias reduction mechanism. The empirical study shows that proposed neural network clearly outperforms all competitors in terms of both accuracy and forecast bias.
  arXiv  Detail & Related papers  (2020-09-24T21:48:08Z)

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.