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Related Experiment Video

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Additive Ensemble Neural Network with Constrained Weighted Quantile Loss for Probabilistic Electric-Load Forecasting.

Manuel Lopez-Martin1, Antonio Sanchez-Esguevillas1, Luis Hernandez-Callejo2

  • 1Department of TSyCeIT, ETSIT, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel constrained weighted quantile loss for probabilistic electric load forecasting. The method enhances neural networks for accurate short and medium-term predictions crucial for smart grids.

Keywords:
deep learningdeep learning additive ensemble modelmachine learningquantile forecastingshort and medium-term electric-load forecasting

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Operations Research

Background:

  • Accurate electric load forecasting is vital for smart grid operations.
  • Probabilistic forecasting offers a more comprehensive understanding of future load compared to point forecasts.
  • Existing methods may lack stability or efficiency in capturing complex load patterns.

Purpose of the Study:

  • To develop a novel quantile regression neural network for probabilistic short and medium-term electric load forecasting.
  • To introduce a constrained weighted quantile loss (CWQLoss) function for improved quantile prediction.
  • To enable existing point forecast models to perform probabilistic forecasting.

Main Methods:

  • A constrained weighted quantile loss (CWQLoss) function was developed, extending the pinball loss.
  • The CWQLoss function incorporates constraints on quantile values and weights, learned end-to-end.
  • The proposed method was applied to multivariate multi-output regression models for electric load forecasting.
  • Performance was evaluated using specific metrics for both point and probabilistic forecasts.

Main Results:

  • The proposed quantile regression neural network achieved superior performance in probabilistic electric load forecasting across various forecast horizons (1h to 1-month).
  • The CWQLoss function demonstrated stability and efficiency, outperforming traditional forecasting models.
  • The model showed optimal results when integrated with an additive ensemble neural network.
  • Experimental validation was conducted using real-world electric load data from a Spanish city.

Conclusions:

  • The proposed constrained weighted quantile loss neural network is effective for probabilistic electric load forecasting in smart grids.
  • The method provides both accurate point and probabilistic forecasts, enhancing operational decision-making.
  • This approach offers a significant advancement for managing electricity demand and supply efficiently.