How to predict and optimise with asymmetric error metrics

• URL: http://arxiv.org/abs/2211.13586v1
• Date: Thu, 24 Nov 2022 13:16:45 GMT
• Title: How to predict and optimise with asymmetric error metrics
• Authors: Mahdi Abolghasemi, Richard Bean
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, 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 examine the possible effect of underforecasting and overforecasting and asymmetric errors on the optimisation cost. 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. We report that while there is a positive correlation between these two, more appropriate loss functions can be used to optimise the costs associated with final decisions.

Related papers

• Predict. Optimize. Revise. On Forecast and Policy Stability in Energy Management Systems [3.3295510777293837]
  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.
  arXiv  Detail & Related papers  (2024-06-29T12:28:30Z)
• Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment [0.0]
  Prediction and optimisation are techniques that have found many applications in solving real-world problems. We review a digital twin that was developed and applied in wastewater treatment in Urban Utility to improve their operational efficiency.
  arXiv  Detail & Related papers  (2024-04-23T00:18:20Z)
• Loss Shaping Constraints for Long-Term Time Series Forecasting [79.3533114027664]
  We present a Constrained Learning approach for long-term time series forecasting that respects a user-defined upper bound on the loss at each time-step. We propose a practical Primal-Dual algorithm to tackle it, and aims to demonstrate that it exhibits competitive average performance in time series benchmarks, while shaping the errors across the predicted window.
  arXiv  Detail & Related papers  (2024-02-14T18:20:44Z)
• End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty [55.04219793298687]
  The Predict-Then-Forecast (PtO) paradigm in machine learning aims to maximize downstream decision quality. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives. It shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
  arXiv  Detail & Related papers  (2024-02-12T16:33:35Z)
• 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)
• Prediction-Oriented Bayesian Active Learning [51.426960808684655]
  Expected predictive information gain (EPIG) is an acquisition function that measures information gain in the space of predictions rather than parameters. EPIG leads to stronger predictive performance compared with BALD across a range of datasets and models.
  arXiv  Detail & Related papers  (2023-04-17T10:59:57Z)
• 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)
• Multistep Multiappliance Load Prediction [0.0]
  We develop a robust and accurate model for the appliance-level load prediction based on four datasets from four different regions. The empirical results show that cyclical encoding of time features and weather indicators alongside a long-short term memory (LSTM) model offer the optimal performance.
  arXiv  Detail & Related papers  (2022-12-19T13:01:51Z)
• The Perils of Learning Before Optimizing [16.97597806975415]
  We show how prediction models can be learned end-to-end by differentiating through the optimization task. We show that the performance gap between a two-stage and end-to-end approach is closely related to the emphprice of correlation concept in optimization.
  arXiv  Detail & Related papers  (2021-06-18T20:43:47Z)
• Regret-Optimal Filtering [57.51328978669528]
  We consider the problem of filtering in linear state-space models through the lens of regret optimization. We formulate a novel criterion for filter design based on the concept of regret between the estimation error energy of a clairvoyant estimator. We show that the regret-optimal estimator can be easily implemented by solving three Riccati equations and a single Lyapunov equation.
  arXiv  Detail & Related papers  (2021-01-25T19:06:52Z)
• 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.