Forecasting Electricity Market Signals via Generative AI
- URL: http://arxiv.org/abs/2403.05743v4
- Date: Fri, 28 Jun 2024 03:17:12 GMT
- Title: Forecasting Electricity Market Signals via Generative AI
- Authors: Xinyi Wang, Qing Zhao, Lang Tong,
- Abstract summary: This paper presents a generative artificial intelligence approach to probabilistic forecasting of electricity market signals.
Inspired by the Wiener-Kallianpur innovation representation of nonparametric time series, we propose a weak innovation autoencoder architecture and a novel deep learning algorithm.
The validity of the proposed approach is established by proving that, under ideal training conditions, the generated samples have the same conditional probability distribution as that of the ground truth.
- Score: 41.99446024585741
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents a generative artificial intelligence approach to probabilistic forecasting of electricity market signals, such as real-time locational marginal prices and area control error signals. Inspired by the Wiener-Kallianpur innovation representation of nonparametric time series, we propose a weak innovation autoencoder architecture and a novel deep learning algorithm that extracts the canonical independent and identically distributed innovation sequence of the time series, from which samples of future time series are generated. The validity of the proposed approach is established by proving that, under ideal training conditions, the generated samples have the same conditional probability distribution as that of the ground truth. Three applications involving highly dynamic and volatile time series in real-time market operations are considered: (i) locational marginal price forecasting for self-scheduled resources such as battery storage participants, (ii) interregional price spread forecasting for virtual bidders in interchange markets, and (iii) area control error forecasting for frequency regulations. Numerical studies based on market data from multiple independent system operators demonstrate the superior performance of the proposed generative forecaster over leading classical and modern machine learning techniques under both probabilistic and point forecasting metrics.
Related papers
- ROLCH: Regularized Online Learning for Conditional Heteroskedasticity [0.0]
Large-scale streaming data are common in modern machine learning applications.
We present a methodology for online estimation of regularized linear distributional models for conditional heteroskedasticity.
Our algorithms are implemented in a computationally efficient Python package.
arXiv Detail & Related papers (2024-06-26T16:04:49Z) - Generative Probabilistic Time Series Forecasting and Applications in
Grid Operations [47.19756484695248]
Generative probabilistic forecasting produces future time series samples according to the conditional probability distribution given past time series observations.
We propose a weak innovation autoencoder architecture and a learning algorithm to extract independent and identically distributed innovation sequences.
We show that the weak innovation sequence is Bayesian sufficient, which makes the proposed weak innovation autoencoder a canonical architecture for generative probabilistic forecasting.
arXiv Detail & Related papers (2024-02-21T15:23:21Z) - Non-parametric Probabilistic Time Series Forecasting via Innovations
Representation [29.255644836978956]
Probabilistic time series forecasting predicts the conditional probability distributions of the time series at a future time given past realizations.
Existing approaches are primarily based on parametric or semi-parametric time-series models that are restrictive, difficult to validate, and challenging to adapt to varying conditions.
This paper proposes a nonparametric method based on the classic notion of em innovations pioneered by Norbert Wiener and Gopinath Kallianpur.
arXiv Detail & Related papers (2023-06-05T02:24:59Z) - Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics
in Limit-Order Book Markets [84.90242084523565]
Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics.
By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention.
By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
arXiv Detail & Related papers (2022-03-07T18:59:54Z) - Probabilistic Forecasting with Generative Networks via Scoring Rule
Minimization [5.5643498845134545]
We use generative neural networks to parametrize distributions on high-dimensional spaces by transforming draws from a latent variable.
We train generative networks to minimize a predictive-sequential (or prequential) scoring rule on a recorded temporal sequence of the phenomenon of interest.
Our method outperforms state-of-the-art adversarial approaches, especially in probabilistic calibration.
arXiv Detail & Related papers (2021-12-15T15:51:12Z) - 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) - Learning the Gap in the Day-Ahead and Real-Time Locational Marginal
Prices in the Electricity Market [0.0]
Machine learning algorithms and deep neural networks are used to predict the values of the price gap between day-ahead and real-time electricity markets.
The proposed methods are evaluated and neural networks showed promising results in predicting the exact values of the gap.
arXiv Detail & Related papers (2020-12-23T16:49:24Z) - Generative Temporal Difference Learning for Infinite-Horizon Prediction [101.59882753763888]
We introduce the $gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon.
We discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors.
arXiv Detail & Related papers (2020-10-27T17:54:12Z) - Stochastically forced ensemble dynamic mode decomposition for
forecasting and analysis of near-periodic systems [65.44033635330604]
We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system.
We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony.
Results are presented for a test case using load data from an electrical grid.
arXiv Detail & Related papers (2020-10-08T20:25:52Z)
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.