Integrating the Expected Future: Schedule Based Energy Forecasting

• URL: http://arxiv.org/abs/2409.05884v1
• Date: Mon, 26 Aug 2024 12:32:40 GMT
• Title: Integrating the Expected Future: Schedule Based Energy Forecasting
• Authors: Raffael Theiler, Olga Fink,
• Abstract summary: Power grid operators depend on accurate and reliable energy forecasts, aiming to minimize cases of extreme errors. incorporating planning information has the potential to significantly enhance the accuracy and specificity of forecasts. Our proposed method significantly improved the average forecasting accuracy of nationwide railway energy consumption.
• Score: 6.675805308519987
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Power grid operators depend on accurate and reliable energy forecasts, aiming to minimize cases of extreme errors, as these outliers are particularly challenging to manage during operation. Incorporating planning information -- such as known data about users' future behavior or scheduled events -- has the potential to significantly enhance the accuracy and specificity of forecasts. Although there have been attempts to integrate such expected future behavior, these efforts consistently rely on conventional regression models to process this information. These models often lack the flexibility and capability to effectively incorporate both dynamic, forward-looking contextual inputs and historical data. To address this challenge, we conceptualize this combined forecasting and regression challenge as a sequence-to-sequence modeling problem and demonstrate, with three distinct models, that our contextually enhanced transformer models excel in this task. By leveraging schedule-based contextual information from the Swiss railway traction network, our proposed method significantly improved the average forecasting accuracy of nationwide railway energy consumption. Specifically, enhancing the transformer models with contextual information resulted in an average reduction of mean absolute error by 40.6\% , whereas other state-of-the-art methods did not demonstrate any significant improvement.

Related papers

• PowerMamba: A Deep State Space Model and Comprehensive Benchmark for Time Series Prediction in Electric Power Systems [6.516425351601512]
  Time series prediction models are needed for closing the gap between the forecasted and actual grid outcomes. We introduce a multivariate time series prediction model that combines traditional state space models with deep learning methods. We release an extended dataset spanning five years of load, electricity price, ancillary service price, and renewable generation.
  arXiv  Detail & Related papers  (2024-12-09T00:23:34Z)
• On autoregressive deep learning models for day-ahead wind power forecasting with irregular shutdowns due to redispatching [0.6001424997506751]
  Day-ahead forecasts are necessary to communicate Wind Power (WP) availability for redispatch planning. The irregular interventions into the WP generation capabilities due to redispatch shutdowns pose challenges in the design and operation of WP forecasting models. This paper analyzes state-of-the-art forecasting methods on data sets with both regular and irregular shutdowns.
  arXiv  Detail & Related papers  (2024-11-30T10:30:11Z)
• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Meta-learning and Data Augmentation for Stress Testing Forecasting Models [0.33554367023486936]
  A model is considered to be under stress if it shows a negative behaviour, such as higher-than-usual errors or increased uncertainty. This paper contributes with a novel framework called MAST (Meta-learning and data Augmentation for Stress Testing)
  arXiv  Detail & Related papers  (2024-06-24T17:59:33Z)
• TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks [31.10683149519954]
  We propose an innovative time series forecasting model TimeSieve. Our approach employs wavelet transforms to preprocess time series data, effectively capturing multi-scale features. Our results validate the effectiveness of our approach in addressing the key challenges in time series forecasting.
  arXiv  Detail & Related papers  (2024-06-07T15:58:12Z)
• A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation [35.46631415365955]
  We introduce a conditional diffusion framework called C$2$TSD, which incorporates disentangled temporal (trend and seasonality) representations as conditional information. Our experiments on three real-world datasets demonstrate the superior performance of our approach compared to a number of state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-18T11:59:04Z)
• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)

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.