Energy Prediction Using Spatiotemporal Pattern Networks

Gregor Henze, Zhanhong Jiang, Chao Liu, Adedotun Akintayo, Soumik Sarkar

Research output: Contribution to journalArticlepeer-review

13 Scopus Citations


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.
Original languageAmerican English
Pages (from-to)1022-1039
Number of pages18
JournalApplied Energy
StatePublished - 2017

NREL Publication Number

  • NREL/JA-5500-70286


  • NILM
  • probabilistic finite state automata
  • spatiotemporal pattern network
  • symbolic dynamical filtering
  • wind power


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