2017/02/03 by Zhanhong Jiang, Chao Liu, Jiang, Zhanhong +8
Computer Science · Engineering · Mathematics · #60-04 #Building Energy and Comfort Optimization #Energy Load and Power Forecasting #FOS: Computer and information sciences #Machine Learning (stat.ML) #Solar Radiation and Photovoltaics #msc:60-04 #stat.ML
paper · pdf · doi:10.48550/arxiv.1702.01125
31 Pages, 24 Figures Preprint Submitted to Journal of Applied Energy
arxiv created 2017/02/03 · openalex publication_date 2017/02/03 · arxiv updated 2017/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 dynamic 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. For quantifying the causal dependency, a mutual information based metric is presented. An energy prediction approach is subsequently proposed based on the STPN framework. For validating 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 the spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring the temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.