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Title: Energy prediction using spatiotemporal pattern networks

Abstract

This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated by the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.

Authors:
; ; ; ; ORCiD logo
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
National Science Foundation (NSF)
OSTI Identifier:
1399848
Report Number(s):
NREL/JA-5500-70286
Journal ID: ISSN 0306-2619
DOE Contract Number:
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: Applied Energy; Journal Volume: 206; Journal Issue: C
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 42 ENGINEERING; wind power; symbolic dynamical filtering; spatiotemporal pattern network; probabilistic finite state automata; NILM

Citation Formats

Jiang, Zhanhong, Liu, Chao, Akintayo, Adedotun, Henze, Gregor P., and Sarkar, Soumik. Energy prediction using spatiotemporal pattern networks. United States: N. p., 2017. Web. doi:10.1016/j.apenergy.2017.08.225.
Jiang, Zhanhong, Liu, Chao, Akintayo, Adedotun, Henze, Gregor P., & Sarkar, Soumik. Energy prediction using spatiotemporal pattern networks. United States. doi:10.1016/j.apenergy.2017.08.225.
Jiang, Zhanhong, Liu, Chao, Akintayo, Adedotun, Henze, Gregor P., and Sarkar, Soumik. Wed . "Energy prediction using spatiotemporal pattern networks". United States. doi:10.1016/j.apenergy.2017.08.225.
@article{osti_1399848,
title = {Energy prediction using spatiotemporal pattern networks},
author = {Jiang, Zhanhong and Liu, Chao and Akintayo, Adedotun and Henze, Gregor P. and Sarkar, Soumik},
abstractNote = {This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated by the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.},
doi = {10.1016/j.apenergy.2017.08.225},
journal = {Applied Energy},
number = C,
volume = 206,
place = {United States},
year = {Wed Nov 01 00:00:00 EDT 2017},
month = {Wed Nov 01 00:00:00 EDT 2017}
}