A Markov Chain Load Modeling Approach through a Stream Clustering Algorithm
| Autor: | Gabriele Mosaico, Stefano Massucco, Federico Silvestro, M. Saviozzi, Enrico Ragaini, Antonio Fidigatti |
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| Rok vydání: | 2020 |
| Předmět: |
Data stream
Markov chain Computer science 020209 energy 020208 electrical & electronic engineering 02 engineering and technology computer.software_genre Load profile Electric power system 0202 electrical engineering electronic engineering information engineering Metering mode Data mining Cluster analysis computer Streaming algorithm Selection (genetic algorithm) |
| Zdroj: | 2020 AEIT International Annual Conference (AEIT). |
| DOI: | 10.23919/aeit50178.2020.9241159 |
| Popis: | Advanced Metering Infrastructure (AMI) is improving the quality and quantity of information within power systems. Thus, these data should be wisely used for efficient management and control. For these reasons, advanced functionalities have to be implemented in order to deal with the massive data stream. In this work, a stream clustering algorithm is used to model any load with a Markov Chain (MC). This algorithm is able to describe the typical load profile in real-time, thanks to a design and an implementation that minimizes the computational burden. The proposed procedure has been tested on an IEEE industrial machines dataset. In addition, a discussion on the parameter selection is provided. |
| Databáze: | OpenAIRE |
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