Combining Probability Density Forecasts for Power Electrical Loads

Researchers have proposed various probabilistic load forecasting models in the form of quantiles, densities, or intervals to describe the uncertainties of future energy demand. Density forecasts can provide more uncertainty information than can be expressed by just the quantile and interval. However...

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Bibliographic Details
Published in:IEEE transactions on smart grid Vol. 11; no. 2; pp. 1679 - 1690
Main Authors: Li, Tianyi, Wang, Yi, Zhang, Ning
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
continuous ranked probability score
Density
density forecasting
Electrical loads
Electricity consumption
ensemble learning
Estimation
Forecasting
Gaussian distribution
linearly constrained quadratic programming
Load forecasting
Load modeling
Mathematical models
Optimization
Performance enhancement
Predictive models
Probabilistic load forecasting
Probabilistic logic
Quadratic programming
Quantiles
Statistical analysis
Uncertainty
ISSN:1949-3053, 1949-3061
Online Access:Get full text
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Summary:Researchers have proposed various probabilistic load forecasting models in the form of quantiles, densities, or intervals to describe the uncertainties of future energy demand. Density forecasts can provide more uncertainty information than can be expressed by just the quantile and interval. However, the combining method for density forecasts is seldom investigated. This paper proposes a novel and easily implemented approach to combine density probabilistic load forecasts to further improve the performance of the final probabilistic forecasts. The combination problem is formulated as an optimization problem to minimize the continuous ranked probability score of the combined model by searching the weights of different individual methods. Under the Gaussian mixture distribution assumption of the density forecasts, the problem is cast to a linearly constrained quadratic programming problem and can be solved efficiently. Case studies on the electric load datasets of eight areas verify the effectiveness of the proposed method.
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ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2019.2942024