JaeHwan Kim, SeMo Yang, KangYoon Lee, Journal of Internet Computing and Services, Vol. 24, No. 2, pp. 57-66, Apr. 2023
10.7472/jksii.2023.24.2.57, Full Text:
Keywords: Energy, Energy usage fee, LSTM, clustering, Time Series K-means, shift of demand

Abstract

Currently, a new energy system is emerging that implements consumption reduction by improving energy efficiency. Accordingly, as smart grids spread, the rate system by timing is expanding. The rate system by timing is a rate system that applies different rates by season/hour to pay according to usage. In this study, external factors such as temperature/day/time/season are considered and the time series prediction model, LSTM, is used to predict energy power usage data. Based on this energy usage prediction model, energy usage charges are reduced by analyzing usage patterns for each device and transferring power energy from the maximum load time to the light load time. In order to analyze the usage pattern for each device, a clustering technique is used to learn and classify the usage pattern of the device by time. In summary, this study predicts usage and usage fees based on the user's power data usage, analyzes usage patterns by device, and provides customized demand transfer services based on analysis, resulting in cost reduction for users.

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