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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10306)

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.

Keywords

Smart grid Sequence-based forecasting Time series models K-means Hierarchical clustering 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Fachgebiet Multimedia KommunikationTechnische Universität DarmstadtDarmstadtGermany

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