An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction

• URL: http://arxiv.org/abs/2302.10889v1
• Date: Sun, 5 Feb 2023 17:16:15 GMT
• Title: An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction
• Authors: Jihan Ghanim, Maha Issa, Mariette Awad
• Abstract summary: Power consumption patterns of the residential sector contain fluctuations and anomalies making them challenging to predict. We propose multiple Long Short-Term Memory (LSTM) frameworks with different asymmetric loss functions to impose a higher penalty on underpredictions. Considering the effect of weather and social factors, seasonality splitting is performed on the three considered datasets from France, Germany, and Hungary.
• Score: 1.6156983514505385
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Building an accurate load forecasting model with minimal underpredictions is vital to prevent any undesired power outages due to underproduction of electricity. However, the power consumption patterns of the residential sector contain fluctuations and anomalies making them challenging to predict. In this paper, we propose multiple Long Short-Term Memory (LSTM) frameworks with different asymmetric loss functions to impose a higher penalty on underpredictions. We also apply a density-based spatial clustering of applications with noise (DBSCAN) anomaly detection approach, prior to the load forecasting task, to remove any present oultiers. Considering the effect of weather and social factors, seasonality splitting is performed on the three considered datasets from France, Germany, and Hungary containing hourly power consumption, weather, and calendar features. Root-mean-square error (RMSE) results show that removing the anomalies efficiently reduces the underestimation and overestimation errors in all the seasonal datasets. Additionally, asymmetric loss functions and seasonality splitting effectively minimize underestimations despite increasing the overestimation error to some degree. Reducing underpredictions of electricity consumption is essential to prevent power outages that can be damaging to the community.

Related papers

• Efficient mid-term forecasting of hourly electricity load using generalized additive models [0.0]
  We propose a novel forecasting method using Generalized Additive Models (GAMs) built from interpretable P-splines and enhanced with autoregressive post-processing. The proposed model is evaluated on load data from 24 European countries.
  arXiv  Detail & Related papers  (2024-05-27T11:41:41Z)
• 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)
• Kunyu: A High-Performing Global Weather Model Beyond Regression Losses [0.0]
  I present Kunyu, a global data-driven weather forecasting model which delivers accurate predictions across a comprehensive array of atmospheric variables at 0.35deg resolution. With both regression and adversarial losses integrated in its training framework, Kunyu generates forecasts with enhanced clarity and realism.
  arXiv  Detail & Related papers  (2023-12-04T17:30:41Z)
• Hierarchical Forecasting at Scale [55.658563862299495]
  Existing hierarchical forecasting techniques scale poorly when the number of time series increases. We propose to learn a coherent forecast for millions of time series with a single bottom-level forecast model. We implement our sparse hierarchical loss function within an existing forecasting model at bol, a large European e-commerce platform.
  arXiv  Detail & Related papers  (2023-10-19T15:06:31Z)
• Residual Diffusion Modeling for Km-scale Atmospheric Downscaling [51.061954281398116]
  A cost-effective downscaling model is trained from a high-resolution 2-km weather model over Taiwan. textitCorrDiff exhibits skillful RMSE and CRPS and faithfully recovers spectra and distributions even for extremes. Downscaling global forecasts successfully retains many of these benefits, foreshadowing the potential of end-to-end, global-to-km-scales machine learning weather predictions.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events [25.325870546140788]
  We propose a novel forecasting framework, named Self-adaptive Decomposed Interpretable framework(SaDI) Experiments on both Central China electric load and public energy meters from buildings show that the proposed SaDI framework achieves average 22.14% improvement.
  arXiv  Detail & Related papers  (2023-06-14T07:11:30Z)
• DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions [78.6363825307044]
  We propose DeepVol, a model based on Dilated Causal Convolutions to forecast day-ahead volatility by using high-frequency data. We show that the dilated convolutional filters are ideally suited to extract relevant information from intraday financial data.
  arXiv  Detail & Related papers  (2022-09-23T16:13:47Z)
• Global Extreme Heat Forecasting Using Neural Weather Models [0.0]
  Heat waves are projected to increase in frequency and severity with global warming. In this work, we explore the potential for deep learning systems trained on historical data to forecast extreme heat on short, medium and subseasonal timescales.
  arXiv  Detail & Related papers  (2022-05-23T00:35:23Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Deep Particulate Matter Forecasting Model Using Correntropy-Induced Loss [1.7797683504485504]
  The maximum correntropy criterion for regression (MCCR) loss is used in an analysis of the statistical characteristics of air pollution and weather data. The MCCR loss is more appropriate than the conventional mean squared error loss for forecasting extreme values.
  arXiv  Detail & Related papers  (2021-06-06T05:17:24Z)

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.