DeepAI AI Chat
Log In Sign Up

Energy Prediction using Spatiotemporal Pattern Networks

by   Zhanhong Jiang, et al.
Iowa State University of Science and Technology

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.


page 12

page 15

page 18

page 21

page 24

page 26

page 27

page 28


Wind ramp event prediction with parallelized Gradient Boosted Regression Trees

Accurate prediction of wind ramp events is critical for ensuring the rel...

Spatiotemporal Attention Networks for Wind Power Forecasting

Wind power is one of the most important renewable energy sources and acc...

E^3: Visual Exploration of Spatiotemporal Energy Demand

Understanding demand-side energy behaviour is critical for making effici...

Predicting the Energy Output of Wind Farms Based on Weather Data: Important Variables and their Correlation

Wind energy plays an increasing role in the supply of energy world-wide....

Data-driven Thermal Model Inference with ARMAX, in Smart Environments, based on Normalized Mutual Information

Understanding the models that characterize the thermal dynamics in a sma...

Deep Belief Networks Based Feature Generation and Regression for Predicting Wind Power

Wind energy forecasting helps to manage power production, and hence, red...

Data-driven Models to Anticipate Critical Voltage Events in Power Systems

This paper explores the effectiveness of data-driven models to predict v...