A Markov Chain Load Modeling Approach through a Stream Clustering Algorithm

2020 ◽  
Author(s):  
Stefano Massucco ◽  
Gabriele Mosaico ◽  
Matteo Saviozzi ◽  
Federico Silvestro ◽  
Antonio Fidigatti ◽  
...  
Keyword(s):  
Markov Chain ◽  
Clustering Algorithm ◽  
Load Modeling ◽  
Stream Clustering ◽  
Modeling Approach

DGStream: High quality and efficiency stream clustering algorithm

2020 ◽  
Vol 141 ◽  
pp. 112947
Author(s):  
Rowanda Ahmed ◽  
Gökhan Dalkılıç ◽  
Yusuf Erten
Keyword(s):  
Clustering Algorithm ◽  
High Quality ◽  
Stream Clustering

A residual load modeling approach for household short-term load forecasting application

2019 ◽  
Vol 187 ◽  
pp. 132-143 ◽  
Author(s):  
Fatima Amara ◽  
Kodjo Agbossou ◽  
Yves Dubé ◽  
Sousso Kelouwani ◽  
Alben Cardenas ◽  
...  
Keyword(s):  
Load Forecasting ◽  
Load Modeling ◽  
Short Term ◽  
Modeling Approach ◽  
Short Term Load Forecasting

Modeling and Prediction of Human Behavior

1999 ◽  
Vol 11 (1) ◽  
pp. 229-242 ◽  
Author(s):  
Alex Pentland ◽  
Andrew Liu
Keyword(s):  
Markov Chain ◽  
Human Behavior ◽  
Kalman Filters ◽  
Dynamic Models ◽  
Markov Models ◽  
Human Behaviors ◽  
Sensory Data ◽  
Modeling Approach ◽  
Modeling And Prediction

We propose that many human behaviors can be accurately described as a set of dynamic models (e.g., Kalman filters) sequenced together by a Markov chain. We then use these dynamic Markov models to recognize human behaviors from sensory data and to predict human behaviors over a few seconds time. To test the power of this modeling approach, we report an experiment in which we were able to achieve 95% accuracy at predicting automobile drivers' subsequent actions from their initial preparatory movements.

Sign in / Sign up

Export Citation Format

Share Document