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

Jamer Jiménez Mares, Loraine Navarro, Christian G. Quintero M. and Mauricio Pardo
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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

Energies, 2020, vol. 13, issue 16, 1-16

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%.

Keywords: demand forecasting; artificial neural networks; clustering; time series analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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