Predict. Optimize. Revise. On Forecast and Policy Stability in Energy Management Systems

• URL: http://arxiv.org/abs/2407.03368v2
• Date: Thu, 11 Jul 2024 13:58:47 GMT
• Title: Predict. Optimize. Revise. On Forecast and Policy Stability in Energy Management Systems
• Authors: Evgenii Genov, Julian Ruddick, Christoph Bergmeir, Majid Vafaeipour, Thierry Coosemans, Salvador Garcia, Maarten Messagie,
• Abstract summary: This research addresses the challenge of integrating forecasting and optimization in energy management systems. It proposes a novel framework for analyzing online optimization problems with switching costs and enabled by deterministic and probabilistic forecasts.
• Score: 3.3295510777293837
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This research addresses the challenge of integrating forecasting and optimization in energy management systems, focusing on the impacts of switching costs, forecast accuracy, and stability. It proposes a novel framework for analyzing online optimization problems with switching costs and enabled by deterministic and probabilistic forecasts. Through empirical evaluation and theoretical analysis, the research reveals the balance between forecast accuracy, stability, and switching costs in shaping policy performance. Conducted in the context of battery scheduling within energy management applications, it introduces a metric for evaluating probabilistic forecast stability and examines the effects of forecast accuracy and stability on optimization outcomes using the real-world case of the Citylearn 2022 competition. Findings indicate that switching costs significantly influence the trade-off between forecast accuracy and stability, highlighting the importance of integrated systems that enable collaboration between forecasting and operational units for improved decision-making. The study shows that committing to a policy for longer periods can be advantageous over frequent updates. Results also show a correlation between forecast stability and policy performance, suggesting that stable forecasts can mitigate switching costs. The proposed framework provides valuable insights for energy sector decision-makers and forecast practitioners when designing the operation of an energy management system.

Related papers

• Calibrated Probabilistic Forecasts for Arbitrary Sequences [58.54729945445505]
  Real-world data streams can change unpredictably due to distribution shifts, feedback loops and adversarial actors. We present a forecasting framework ensuring valid uncertainty estimates regardless of how data evolves.
  arXiv  Detail & Related papers  (2024-09-27T21:46:42Z)
• Probabilistic forecasting of power system imbalance using neural network-based ensembles [4.573008040057806]
  We propose an ensemble of C-VSNs, which are our adaptation of variable selection networks (VSNs) Each minute, our model predicts the imbalance of the current and upcoming two quarter-hours, along with uncertainty estimations on these forecasts. For high imbalance magnitude situations, our model outperforms the state-of-the-art by 23.4%.
  arXiv  Detail & Related papers  (2024-04-23T08:42:35Z)
• 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)
• A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty [18.485617498705736]
  We propose a real-time uncertainty-aware energy dispatch framework, which is composed of two key elements. The proposed framework is capable to rapidly adapt to the real-time data distribution, as well as to target on uncertainties caused by data drift, model discrepancy and environment perturbations in the control process. The framework won the championship in CityLearn Challenge 2022, which provided an influential opportunity to investigate the potential of AI application in the energy domain.
  arXiv  Detail & Related papers  (2023-09-15T00:04:00Z)
• Probabilistic load forecasting with Reservoir Computing [10.214379018902914]
  This work focuses on reservoir computing as the core time series forecasting method. While the RC literature mostly focused on point forecasting, this work explores the compatibility of some popular uncertainty quantification methods with the reservoir setting.
  arXiv  Detail & Related papers  (2023-08-24T15:07:08Z)
• 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)
• Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts [73.33395097728128]
  We provide the first systematic finite-sample study of proper scoring rules for time-series forecasting evaluation. We carry out our analysis on a comprehensive synthetic benchmark, specifically designed to test several key discrepancies between ground-truth and forecast distributions.
  arXiv  Detail & Related papers  (2023-04-19T17:38:42Z)
• Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy [42.00952788334554]
  This paper presents the findings of the IEEE-CIS Technical Challenge on Predict+ for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition. The winning method predicted different scenarios and optimized over all scenarios using a sample average approximation method.
  arXiv  Detail & Related papers  (2022-12-21T02:34:12Z)
• How to predict and optimise with asymmetric error metrics [0.0]
  We examine the concept of the predict and optimise problem with specific reference to the third Technical Challenge of the IEEE Computational Intelligence Society. In this competition, entrants were asked to forecast building energy use and solar generation at six buildings and six solar installations, and then use their forecast to optimize energy cost while scheduling classes and batteries over a month. We explore the different nature of loss functions for the prediction and optimisation phase and propose to adjust the final forecasts for a better optimisation cost.
  arXiv  Detail & Related papers  (2022-11-24T13:16:45Z)
• 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)
• Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration [107.15813002403905]
  Decision makers often need to rely on imperfect probabilistic forecasts. We propose a compensation mechanism ensuring that the forecasted utility matches the actually accrued utility. We demonstrate an application showing how passengers could confidently optimize individual travel plans based on flight delay probabilities.
  arXiv  Detail & Related papers  (2020-11-15T08:22:39Z)

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.