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Hybrid Models for Short-Term Load Forecasting Using Clustering and Time Series

Key:AABR17
Author:Wael Alkhatib, Alaa Alhamoud, Doreen Böhnstedt, Ralf Steinmetz
Date:June 2017
Kind:In proceedings - use for conference & workshop papers
Publisher:Springer, Cham
Organization:Springer
Book title:International Work-Conference on Artificial Neural Networks
Editor:Ignacio RojasGonzalo JoyaAndreu Catala
Pages:104--115
Volume:10306
ISBN:Online: 978-3-319-59147-6, Print: 978-3-319-59146-9
Keywords:Smart grid; Sequence-based Forecasting; Time series models; K-means; Hierarchical clustering
Research Area(s):Knowledge Media
Abstract:Short-term forecasting models on the micro-grid level help guaranteeing the cost-effective dispatch of available resources and maintaining shortfalls and surpluses to a minimum in the spot market. In this paper, we introduce two time series models for forecasting the day-ahead total power consumption and the fine-granular 24-hour consumption pattern of individual buildings. The proposed model for predicting the consumption pattern outperforms the state-of-the-art algorithm of Pattern Sequence-based Forecasting (PSF). Our analysis reveals that the clustering of individual buildings based on their seasonal, weekly, and daily patterns of power consumption improves the prediction accuracy and increases the time efficiency by reducing the search space.
URL:https://link.springer.com/chapter/10.1007/978-3-319-59147-6_10

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