KPF-AE-LSTM: A Deep Probabilistic Model for Net-Load Forecasting in High Solar Scenarios

• URL: http://arxiv.org/abs/2203.04401v1
• Date: Sat, 5 Mar 2022 10:54:54 GMT
• Title: KPF-AE-LSTM: A Deep Probabilistic Model for Net-Load Forecasting in High Solar Scenarios
• Authors: Deepthi Sen, Indrasis Chakraborty, Soumya Kundu, Andrew P. Reiman, Ian Beil, Andy Eiden
• Abstract summary: This paper presents a deep learning method to generate probabilistic forecasts of day-ahead net-load at 15-min resolution, at various solar penetration levels. The models are shown to deliver superior forecast performance, as well as maintain superior training efficiency, in comparison to existing benchmark models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the expected rise in behind-the-meter solar penetration within the distribution networks, there is a need to develop time-series forecasting methods that can reliably predict the net-load, accurately quantifying its uncertainty and variability. This paper presents a deep learning method to generate probabilistic forecasts of day-ahead net-load at 15-min resolution, at various solar penetration levels. Our proposed deep-learning based architecture utilizes the dimensional reduction, from a higher-dimensional input to a lower-dimensional latent space, via a convolutional Autoencoder (AE). The extracted features from AE are then utilized to generate probability distributions across the latent space, by passing the features through a kernel-embedded Perron-Frobenius (kPF) operator. Finally, long short-term memory (LSTM) layers are used to synthesize time-series probability distributions of the forecasted net-load, from the latent space distributions. The models are shown to deliver superior forecast performance (as per several metrics), as well as maintain superior training efficiency, in comparison to existing benchmark models. Detailed analysis is carried out to evaluate the model performance across various solar penetration levels (up to 50\%), prediction horizons (e.g., 15\,min and 24\,hr ahead), and aggregation level of houses, as well as its robustness against missing measurements.

Related papers

• Hierarchical Forecasting at Scale [55.658563862299495]
  Existing hierarchical forecasting techniques scale poorly when the number of time series increases. We propose to learn a coherent forecast for millions of time series with a single bottom-level forecast model. We implement our sparse hierarchical loss function within an existing forecasting model at bol, a large European e-commerce platform.
  arXiv  Detail & Related papers  (2023-10-19T15:06:31Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• Probabilistic Forecasting with Coherent Aggregation [42.215158938066054]
  We augment an MQForecaster neural network architecture with a novel deep Gaussian factor forecasting model that achieves coherence by construction. In a comparison to state-of-the-art coherent forecasting methods, DeepCoFactor achieves significant improvements in scaled CRPS forecast accuracy, with average gains of 15%.
  arXiv  Detail & Related papers  (2023-07-19T07:31:37Z)
• Deep Learning for Day Forecasts from Sparse Observations [60.041805328514876]
  Deep neural networks offer an alternative paradigm for modeling weather conditions. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 has a high temporal and spatial resolution, respectively, up to 2 minutes and 1 km as well as a low operational latency.
  arXiv  Detail & Related papers  (2023-06-06T07:07:54Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)
• Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope [9.963669010212012]
  We leverage state-of-the-art machine learning methods to predict observatory image quality (IQ) from environmental conditions and observatory operating parameters. We develop accurate and interpretable models of the complex dependence between data features and observed IQ for CFHT's wide field camera, MegaCam.
  arXiv  Detail & Related papers  (2021-06-30T18:10:20Z)
• Deep learning-based multi-output quantile forecasting of PV generation [34.51430520593065]
  This paper develops probabilistic PV forecasters by taking advantage of recent breakthroughs in deep learning. It tailored forecasting tool, named encoder-decoder, is implemented to compute intraday multi-output PV quantiles forecasts. The models are trained using quantile regression, a non-parametric approach.
  arXiv  Detail & Related papers  (2021-06-02T16:28:10Z)
• 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)
• Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques [2.3436632098950456]
  This paper presents a review of state-of-the-art forecasting methods for smart grid (SG) systems. Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated. A comparative case study using the Victorian electricity consumption and American electric power (AEP) is conducted.
  arXiv  Detail & Related papers  (2020-11-25T09:17:07Z)
• An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning [1.52292571922932]
  Short-term solar irradiance forecasting is difficult due to the non-stationary characteristic of solar power. We propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance. Our proposed method results in a 71.5% reduction in the mean RMSE averaged across all the test sites.
  arXiv  Detail & Related papers  (2019-05-07T14:40:32Z)

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.