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

Published: 21 May 2013 Publication History

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.

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      cover image ACM Conferences
      e-Energy '13: Proceedings of the fourth international conference on Future energy systems
      January 2013
      306 pages
      ISBN:9781450320528
      DOI:10.1145/2487166
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      Published: 21 May 2013

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      Author Tags

      1. clustering
      2. ensemble model
      3. time series forecasting

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      e-Energy '13 Paper Acceptance Rate 40 of 76 submissions, 53%;
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