Prediction Of Time Series Analysis Of Power Usage Based On Rstudio

In a competitive retail market,power boards are looking to analyze their customer each and every power usage data from a smart meters that will produce lots of opportunity for them to realize additional knowledge on customer's electricity consumption demand in a very massive volumes of smart grids meters data.Generally there are a great number of analytical solutions to present the facility usage of households but these types of solutions don't provide exact information.So we attempt to perform a comprehensive analysis of individual household energy consumption patterns,and design a household-level prediction model that utilize historical energy consumption data to predict future value activation and associated demand response.This paper proposes a completely unique approach for prediction of time series analysis of transmit electricity in electrical distribution system which shows the proportion of different consumption actions,and consumption levels in different time periods of the week,in adjacent periods.The proposed methods predict customer's eligibility to manager their household electricity data collection using smart meters and help customer system operator to detect and control load demand.This model finds the various power trends in different periods in a large data set.Assessment has been performed by using prediction of time series data methods and forecasting models.The result shows that the prediction with good accuracy help companies and end-users to control their load demand by shifting power consumption from peak hours to off-peak hours.Knowledge discovery regressions model improve clearly power trend consumption over a week and help users to improve customer demand such a saving energy,low price and management.