Learning the Gap in the Day-Ahead and Real-Time Locational Marginal
Prices in the Electricity Market
- URL: http://arxiv.org/abs/2012.12792v1
- Date: Wed, 23 Dec 2020 16:49:24 GMT
- Title: Learning the Gap in the Day-Ahead and Real-Time Locational Marginal
Prices in the Electricity Market
- Authors: Nika Nizharadze, Arash Farokhi Soofi, Saeed D. Manshadi
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In this paper, statistical machine learning algorithms, as well as deep
neural networks, are used to predict the values of the price gap between
day-ahead and real-time electricity markets. Several exogenous features are
collected and impacts of these features are examined to capture the best
relations between the features and the target variable. Ensemble learning
algorithm namely the Random Forest issued to calculate the probability
distribution of the predicted electricity prices for day-ahead and real-time
markets. Long-Short-Term-Memory (LSTM) is utilized to capture long term
dependencies in predicting direct gap values between mentioned markets and the
benefits of directly predicting the gap price rather than subtracting the
predictions of day-ahead and real-time markets are illustrated. Case studies
are implemented on the California Independent System Operator (CAISO)
electricity market data for a two years period. The proposed methods are
evaluated and neural networks showed promising results in predicting the exact
values of the gap.
Related papers
- Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market [0.0]
integration of renewable energy into electricity markets poses significant challenges to price stability.
This study explores the enhancement of probabilistic price prediction using Conformal Prediction (CP) techniques.
We propose an ensemble approach that combines the efficiency of quantile regression models with the robust coverage properties of time series adapted CP techniques.
arXiv Detail & Related papers (2025-02-07T13:57:47Z) - Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market [3.130428666578115]
This study presents a Transformer-based deep learning model to forecast the price spread between real-time and day-ahead electricity prices in the ERCOT (Electric Reliability Council of Texas) market.
The proposed model is trained under realistic constraints and validated using a walk-forward approach by updating the model every week.
The results show that the strategy of trading only at the peak hour with a precision score of over 50% produces nearly consistent profit over the test period.
arXiv Detail & Related papers (2024-11-25T20:04:16Z) - Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Prices [0.0]
This study presents the first Bayesian forecasting of electricity prices traded on the German intraday market.
The target variable is the IDFull price index, with forecasts given as posterior predictive distributions.
We observe significant improvements in point measures and probability scores, including an average reduction of $5.9,%$ in absolute errors.
arXiv Detail & Related papers (2024-03-08T16:51:27Z) - Diffusion Variational Autoencoder for Tackling Stochasticity in
Multi-Step Regression Stock Price Prediction [54.21695754082441]
Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility.
Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions.
We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction.
Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
arXiv Detail & Related papers (2023-08-18T16:21:15Z) - Price-Aware Deep Learning for Electricity Markets [58.3214356145985]
We propose to embed electricity market-clearing optimization as a deep learning layer.
Differentiating through this layer allows for balancing between prediction and pricing errors.
We showcase the price-aware deep learning in the nexus of wind power forecasting and short-term electricity market clearing.
arXiv Detail & Related papers (2023-08-02T21:16:05Z) - Multivariate Probabilistic Forecasting of Intraday Electricity Prices
using Normalizing Flows [62.997667081978825]
In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the EPEX spot markets in a distinct hourly pattern.
This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts.
arXiv Detail & Related papers (2022-05-27T08:38:20Z) - 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) - ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without
Periodogram and Gaussianity Assumptions [91.3755431537592]
We present the ApeRI-miodic (ARISE) process for investigating efficient markets.
The ARISE process is formulated as an infinite-sum of some known processes and employs the aperiodic spectrum estimation.
In practice, we apply the ARISE function to identify the efficiency of real-world markets.
arXiv Detail & Related papers (2021-11-08T03:36:06Z) - The impact of online machine-learning methods on long-term investment
decisions and generator utilization in electricity markets [69.68068088508505]
We investigate the impact of eleven offline and five online learning algorithms to predict the electricity demand profile over the next 24h.
We show we can reduce the mean absolute error by 30% using an online algorithm when compared to the best offline algorithm.
We also show that large errors in prediction accuracy have a disproportionate error on investments made over a 17-year time frame.
arXiv Detail & Related papers (2021-03-07T11:28:54Z) - Transfer Learning for Electricity Price Forecasting [0.0]
We propose to use transfer learning as a tool for utilizing information from other electricity price markets for forecasting.
Our experiments on five different day-ahead markets indicate that transfer learning improves the performance of electricity price forecasting in a statistically significant manner.
arXiv Detail & Related papers (2020-07-05T17:24:36Z) - Ensemble Forecasting for Intraday Electricity Prices: Simulating
Trajectories [0.0]
Recent studies have shown that the hourly German Intraday Continuous Market is weak-form efficient.
A probabilistic forecasting of the hourly intraday electricity prices is performed by simulating trajectories in every trading window.
The study aims to forecast the price distribution in the German Intraday Continuous Market in the last 3 hours of trading, but the approach allows for application to other continuous markets, especially in Europe.
arXiv Detail & Related papers (2020-05-04T10:21:20Z)
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.