Smart Meter Data Analytics Using Particle Swarm Optimization

Smart meter data are raw data. The pervasive recognition of smart meters generates an enormous quantity of electricity utilization data to be collected. The huge amount of data generated by smart meters are collected periodically and it will be analyzed for predicting the electricity demand which wi...

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Bibliographic Details
Published in:	2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN) pp. 1 - 5
Main Authors:	Suresh, M., Anbarasi, M. S., Jayasre, R., Shivani, C., Sowmiya, P.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-03-2019
Subjects:	Clustering Data analysis data analytics Feature extraction Forecasting Meters Particle swarm optimization PSO Signal processing algorithms Smart Meter Smart meters usage pattern
Online Access:	Get full text
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Summary:	Smart meter data are raw data. The pervasive recognition of smart meters generates an enormous quantity of electricity utilization data to be collected. The huge amount of data generated by smart meters are collected periodically and it will be analyzed for predicting the electricity demand which will be for convenience companies and inhabitants. Now our proposed work is to Forecasting the usage and price of smart meter data analytics using particle swarm optimization and k-means algorithm. The k-means algorithm is using for given best solution for prediction.
DOI:	10.1109/ICSCAN.2019.8878833