Is your forecaster smarter than an energy engineer: a deep dive into
electricity price forecasting
- URL: http://arxiv.org/abs/2209.13411v1
- Date: Thu, 22 Sep 2022 10:53:24 GMT
- Title: Is your forecaster smarter than an energy engineer: a deep dive into
electricity price forecasting
- Authors: Maria Margarida Mascarenhas and Hussain Kazmi
- Abstract summary: We use data from the Belgian electricity markets to understand if a state-of-the-art forecasting model can be trusted in more general settings.
Our results show that, despite being largely accurate enough in general, even state of the art forecasts struggle to remain consistent with reality.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The field of electricity price forecasting has seen significant advances in
the last years, including the development of new, more accurate forecast
models. These models leverage statistical relationships in previously observed
data to predict the future; however, there is a lack of analysis explaining
these models, which limits their real world applicability in critical
infrastructure. In this paper, using data from the Belgian electricity markets,
we explore a state-of-the-art forecasting model to understand if its
predictions can be trusted in more general settings than the limited context it
is trained in. If the model produces poor predictions in extreme conditions or
if its predictions are inconsistent with reality, it cannot be relied upon in
real-world where these forecasts are used in downstream decision-making
activities. Our results show that, despite being largely accurate enough in
general, even state of the art forecasts struggle with remaining consistent
with reality.
Related papers
- Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes:
Functional and Augmented Data Structures in Financial Forecasting [0.0]
We explore the application of Gaussian Processes (GPs) for predicting mean-reverting time series with an underlying structure.
GPs offer the potential to forecast not just the average prediction but the entire probability distribution over a future trajectory.
This is particularly beneficial in financial contexts, where accurate predictions alone may not suffice if incorrect volatility assessments lead to capital losses.
arXiv Detail & Related papers (2024-02-23T06:09:45Z) - Predictive Churn with the Set of Good Models [64.05949860750235]
We study the effect of conflicting predictions over the set of near-optimal machine learning models.
We present theoretical results on the expected churn between models within the Rashomon set.
We show how our approach can be used to better anticipate, reduce, and avoid churn in consumer-facing applications.
arXiv Detail & Related papers (2024-02-12T16:15:25Z) - Weather Prediction with Diffusion Guided by Realistic Forecast Processes [49.07556359513563]
We introduce a novel method that applies diffusion models (DM) for weather forecasting.
Our method can achieve both direct and iterative forecasting with the same modeling framework.
The flexibility and controllability of our model empowers a more trustworthy DL system for the general weather community.
arXiv Detail & Related papers (2024-02-06T21:28:42Z) - ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast.
We also introduce a training-free extreme value enhancement strategy named ExEnsemble, which increases the variance of pixel values and improves the forecast robustness.
Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
arXiv Detail & Related papers (2024-02-02T10:34:13Z) - FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather
Forecasting [56.73502043159699]
This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution.
It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model.
The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
arXiv Detail & Related papers (2024-01-28T13:23:25Z) - On some limitations of data-driven weather forecasting models [0.0]
We examine some aspects of the forecasts produced by an exemplar of the current generation of ML models, Pangu-Weather.
The main conclusion is that Pangu-Weather forecasts, and possibly those of similar ML models, do not have the fidelity and physical consistency of physics-based models.
arXiv Detail & Related papers (2023-09-15T15:21:57Z) - SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models [13.331224394143117]
Uncertainty quantification is crucial to decision-making.
dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts.
We propose to amortize the computational cost by emulating these forecasts with deep generative diffusion models learned from historical data.
arXiv Detail & Related papers (2023-06-24T22:00:06Z) - 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) - Probabilistic forecasts of wind power generation in regions with complex
topography using deep learning methods: An Arctic case [3.3788638227700734]
This work presents concepts and approaches concerning probabilistic forecasts with deep learning.
Deep learning models are used to make probabilistic forecasts of day-ahead power generation from a wind power plant located in Northern Norway.
arXiv Detail & Related papers (2022-03-10T15:52:11Z) - A generative adversarial network approach to (ensemble) weather
prediction [91.3755431537592]
We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe.
The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
arXiv Detail & Related papers (2020-06-13T20:53:17Z)
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.