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A Methodology for Energy Load Profile Forecasting Based on Intelligent Clustering and Smoothing Techniques

Author

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  • Jamer Jiménez Mares

    (Department of Electrical and Electronics Engineering; Universidad del Norte, Barranquilla 081007, Colombia)

  • Loraine Navarro

    (Department of Electrical and Electronics Engineering; Universidad del Norte, Barranquilla 081007, Colombia)

  • Christian G. Quintero M.

    (Department of Electrical and Electronics Engineering; Universidad del Norte, Barranquilla 081007, Colombia)

  • Mauricio Pardo

    (Department of Electrical and Electronics Engineering; Universidad del Norte, Barranquilla 081007, Colombia)

Abstract

The electrical sector needs to study how energy demand changes to plan the maintenance and purchase of energy assets properly. Prediction studies for energy demand require a high level of reliability since a deviation in the forecasting demand could affect operation costs. This paper proposed a short-term forecasting energy demand methodology based on hierarchical clustering using Dynamic Time Warp as a similarity measure integrated with Artificial Neural Networks. Clustering was used to build the typical curve for each type of day, while Artificial Neural Networks handled the weather sensibility to correct a preliminary forecasting curve obtained in the clustering stage. A statistical analysis was carried out to identify those significant factors in the prediction model of energy demand. The performance of this proposed model was measured through the Mean Absolute Percentage Error (MAPE). The experimental results show that the three-stage methodology was able to improve the MAPE, reaching values as good as 2%.

Suggested Citation

  • Jamer Jiménez Mares & Loraine Navarro & Christian G. Quintero M. & Mauricio Pardo, 2020. "A Methodology for Energy Load Profile Forecasting Based on Intelligent Clustering and Smoothing Techniques," Energies, MDPI, vol. 13(16), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4040-:d:394570
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    References listed on IDEAS

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    1. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
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