An ensemble model for day-ahead electricity demand time series forecasting

Wen Shen, Vahan Babushkin, Zeyar Aung, Wei Lee Woon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

48 Scopus citations

Abstract

In this work, we try to solve the problem of day-ahead prediction of electricity demand using an ensemble forecasting model. Based on the Pattern Sequence Similarity (PSF) algorithm, we implemented five forecasting models using different clustering techniques: K-means model (as in original PSF), Self-Organizing Map model, Hierarchical Clustering model, K-medoids model, and Fuzzy C-means model. By incorporating these five models, we then proposed an ensemble model, named Pattern Forecasting Ensemble Model (PFEM), with iterative prediction procedure. We evaluated its performance on three real-world electricity demand datasets and compared it with those of the five forecasting models individually. Experimental results show that PFEM outperforms all those five individual models in terms of Mean Error Relative and Mean Absolute Error.

Original languageBritish English
Title of host publicatione-Energy 2013 - Proceedings of the 4th ACM International Conference on Future Energy Systems
Pages51-62
Number of pages12
DOIs
StatePublished - 2013
Event4th ACM International Conference on Future Energy Systems, e-Energy 2013 - Berkeley, CA, United States
Duration: 21 May 201324 May 2013

Publication series

Namee-Energy 2013 - Proceedings of the 4th ACM International Conference on Future Energy Systems

Conference

Conference4th ACM International Conference on Future Energy Systems, e-Energy 2013
Country/TerritoryUnited States
CityBerkeley, CA
Period21/05/1324/05/13

Keywords

  • clustering
  • ensemble model
  • time series forecasting

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