A hybrid model based on symbolic regression and neural networks for electricity load forecastingShow others and affiliations
2018 (English)In: International Conference on the European Energy Market, EEM, IEEE Computer Society, 2018, article id 8469901Conference paper, Published paper (Refereed)
Abstract [en]
This paper proposes a hybrid model for electricity load forecasting. Symbolic regression is initially used to automatically create a regression model of the load. Then the explanatory variables and their transformations that have been selected in the model are used as input in an artificial neural network that is trained to predict the electricity load at the output. Therefore symbolic regression operates as a feature selection-creation method and forecasting is done by the artificial neural network. The proposed hybrid model has been successfully used in an electricity load forecasting competition.
Place, publisher, year, edition, pages
IEEE Computer Society, 2018. article id 8469901
Keywords [en]
Forecasting competition, Load forecasting, Neural networks, Symbolic regression, Commerce, Electric power plant loads, Forecasting, Power markets, Regression analysis, Electricity load, Electricity load forecasting, Explanatory variables, Hybrid model, Regression model, Electric load forecasting
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-247176DOI: 10.1109/EEM.2018.8469901ISI: 000482771100114Scopus ID: 2-s2.0-85055476841ISBN: 9781538614884 (print)OAI: oai:DiVA.org:kth-247176DiVA, id: diva2:1313930
Conference
15th International Conference on the European Energy Market, EEM 2018; Lodz; Poland; 27 June 2018 through 29 June 2018
Note
QC 20190507
2019-05-072019-05-072024-03-18Bibliographically approved