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Electricity cost plays a vital role due to the immense increase in power utilization, rise in energy rates and alarms about the variations and impact on the environment which ultimately affects electricity cost. We claim that electrical power utilization data became more beneficial if it is presented to the customers along with the prediction of power consumption, prediction of energy prices and prediction of its expected electricity cost. It will assist the residents to alter their power utilization behavior, and thus will have an optimistic influence on the electricity production companies, dissemination network and electricity grid. In this study, we present a residential area power cost prediction by applying the Autoregressive Integrated Moving Average (ARIMA) technique in Korean apartments. We have investigated the energy utilization data on the foundation of daily, weekly and monthly power utilization. The accumulated data constructed on daily, weekly and monthly utilization are selected. Then we predict the maximum and average power consumption cost for each of the predicted daily, weekly and monthly power consumption. The power consumption and general price (General Electricity Price in Korea) data of Korea are used to analyze the efficiency of the prediction algorithm. The accuracy of the power cost prediction using the ARIMA model is verified using the absolute error.

Electricity Cost Prediction using Autoregressive Integrated Moving Average (ARIMA) in Korea