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Predict Electric Power Demand with Extended Goal Graph and Heterogeneous Mixture Modeling

Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4, Kawazu, Iizuka, Fukuoka 820-8502, Japan
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Information 2019, 10(4), 134; https://doi.org/10.3390/info10040134
Received: 29 January 2019 / Revised: 4 April 2019 / Accepted: 8 April 2019 / Published: 10 April 2019
(This article belongs to the Special Issue MoDAT: Designing the Market of Data)
In this study, methods for predicting energy demand on hourly consumption data are established for realizing an energy management system for buildings. The methods consist of an energy prediction algorithm that automatically separates the datasets to partitions (gate) and creates a linear regression model (local expert) for each partition on the heterogeneous mixture modeling, and an extended goal graph that extracts candidates of variables both for data partitioning and for linear regression for the energy prediction algorithm. These methods were implemented as tools and applied to create the energy prediction model on two years' hourly consumption data for a building. We validated the methods by comparing accuracies with those of different machine learning algorithms applied to the same datasets. View Full-Text
Keywords: prediction method for electric power demand in building; extended goal graph; heterogeneous mixture modeling prediction method for electric power demand in building; extended goal graph; heterogeneous mixture modeling
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Kushiro, N.; Fukuda, A.; Kawatsu, M.; Mega, T. Predict Electric Power Demand with Extended Goal Graph and Heterogeneous Mixture Modeling. Information 2019, 10, 134.

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