Seasonal self-evolving neural networks based short-term wind farm generation forecast

Yunchuan Liu, Amir Ghasemkhani, Lei Yang, Jun Zhao, Junshan Zhang, Vijay Vittal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper studies short-term wind farm generation forecast. From the real wind generation data, we observe that wind farm generation exhibits the non-stationarity and the seasonality and that the dynamics of non-ramp, ramp-up, and ramp-down events are different across different classes of wind turbines. To deal with such heterogeneous dynamics of wind farm generation, we propose seasonal self-evolving neural networks based short-term wind farm generation forecast. The proposed approach first classifies the historical data into ramp-up and ramp-down datasets and non-ramp datasets for different seasons, and then trains different neural networks for each dataset to capture different wind farm power dynamics. To account for the non-stationarity as well as reduce the burden of hyperparameter tuning, we leverage NeuroEvolution of Augmenting Topologies (NEAT) to train neural networks, which evolves the neural networks using a genetic algorithm to find the best weighting parameters and network topology. Based on the proposed seasonal self-evolving neural networks, we develop algorithms for both point forecasts and distributional forecasts. Experimental results, using the real wind generation data, demonstrate the significantly improved accuracy of the proposed forecast approach, compared with other forecast approaches.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728161273
DOIs
StatePublished - Nov 11 2020
Event2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020 - Tempe, United States
Duration: Nov 11 2020Nov 13 2020

Publication series

Name2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020

Conference

Conference2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
Country/TerritoryUnited States
CityTempe
Period11/11/2011/13/20

Keywords

  • Distributional forecast
  • Point forecast
  • Self-evolving neural networks
  • Short-term wind power forecast

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization

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