Design of a Dynamic Demand Response Model Through Intelligent Clustering Algorithm Based on Load Forecasting in Smart Grid
Keywords:Agglomerative clustering, ARIMA, Demand response, Forecast, Smart grid
The development of smart metering technology empowers power reforms, which allows effective implementation of demand response programs to effectively operate the power grid. The systematic analysis of smart meter data plays a vital role for both consumers and utilities to reduce their costs and improve the efficiency of power management. In this paper, a machine learning algorithm is proposed to recommend the appropriate Demand Response (DR) program for the consumer in a real-time environment, tailored with dynamic pricing. The systematic recommendation can be made by integrating time series forecasting, consumer clustering, and DR analysis. The smart meter data of the 28 consumers for 108 weeks are recorded and applied to the ARIMA time series forecast algorithm. The smart meter data and ARIMA time series forecast data are combined and fed to the Agglomerative Hierarchical clustering algorithm to cluster consumers based on their usage and demand pattern. Clusters are analysed to identify a suitable DR program for the consumer. The results show that the proposed machine learning method effectively clusters consumers and implements the DR program in the smart grid environment.
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