An Analysis and Prediction Model of Power Consumption in Korean Homes Using Kalman Filter

Safdar Ali, DoHyeun Kim


Electricity power efficiency has become a major concern to the research community due to the increase in power consumption, energy prices and alarms regarding the environmental changes. There are some major organizations such as Google and Microsoft who started projects to present electric energy consumption data to the consumers. However the two organizations’ backing, these two efforts have not received the expected level of attention, and are thus being discontinued. We believe that electric power consumption data can become more beneficial if it is presented to the occupants of the buildings along with prediction of power consumption. This will help the residents change their power consumption behavior, and thus have a positive impact on the electricity utilization and generation, distribution network and electricity grid operation. In this paper, we present power consumption and prediction using Kalman filter in Korean homes. We have analyzed the power consumption data on the basis of daily, weekly, monthly and yearly consumption. The aggregated data based on daily, weekly, monthly and yearly consumption are considered. Then we predict the maximum and average power consumption for each of the daily, weekly, monthly and yearly power consumption. At the end we also show the accuracy results of the prediction. We have used the actual data. The data is obtained from Korean homes in apartment buildings. Each building consists of 100 homes. Each apartment has ten floors and each floor has ten homes.


Kalman filter, power consumption, prediction.


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