A hybrid forecasting approach applied in the electrical power system based on data preprocessing, optimization and artificial intelligence algorithms

DOI10.1016/J.APM.2016.08.001zbMath1443.62301OpenAlexW2509426069MaRDI QIDQ2281817

Ping Jiang, Xuejiao Ma

Publication date: 6 January 2020

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2016.08.001

zbMATH Keywords

time series forecastingelectrical power systemmodified particle swarm optimizationfast ensemble empirical mode decompositionforecasting validity degreeimproved simulated annealing

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Economic models of real-world systems (e.g., electricity markets, etc.) (91B74)

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Cites Work

• Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms -- support vector regression forecast combinations
• Swarm-based translation-invariant morphological prediction method for financial time series forecasting
• Fuzzy aggregation of modular neural networks with ordered weighted averaging operators
• Induced aggregation operators.

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