Encored Technologies, Inc.

Hyo Seop Lee

Jae Ryun Yim

Chang Ho Shin

A power consumption disaggregation system according to an embodiment of the present invention comprises: a DB for receiving power data from a smart meter installed in a house, receiving data according to external factors affecting the power data from an external server, and storing the data in time series; an extraction unit for extracting a characteristic variable of a reference resolution time interval from the power data and the data according to external factors stored in time series; and an analysis unit for disaggregating total power consumption in the house by inferring a hidden power consumption state by inputting the characteristic variable into a power consumption disaggregation model based on a Hidden Markov Model (HMM) considering dependency of external factors.

System for disaggregating power consumption

A method of predicting power generation using meta-learning includes: (a) receiving, by a main server, power generation data and weather data of one or more power plants; (b) scaling, by the main server, the power generation data using respective capacities of the one or more power plants; (c) scaling, by the main server, the weather data using maximum and minimum weather values during a predetermined period; (d) generating, by the main server, a weather-power generation input sequence using weather scaling data corresponding to a first past period and a prediction period that are continuous to each other, and power generation scaling data corresponding to a second past period that is longer than the first past period; and (e) inputting, by the main server, the weather-power generation input sequence into a power generation prediction model to acquire a power generation output sequence corresponding to the prediction period.

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Seoul, South Korea

Hyo Seop Lee