• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Journal of the HCI Society of Korea
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• v.12 no.4
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• pp.65-73
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• 2017

While electricity demand is generally increasing, stably controlling supply is becoming a serious challenge because renewable energies are becoming popular and often their productions are dependent on the weather. The 'demand response' programs can be used to complement the problems of renewable energies, and therefore their role is becoming increasingly important. This study provides an analysis of a demand response pilot that was conducted in Korea. The study first focused on questionnaire surveys and in-depth interviews, and the data was used to perform a Technology Acceptance Model (TAM) analysis. The goal of the pilot was to have the residential users reduce their power consumptions when an energy reduction mission is issued during peak load hours. The experimental subjects consisted of two groups with different characteristics. Subjects in group A obtained smart meters as an optional function of IoT platform service provided by a mobile service company, and received a charge deduction as their compensation. Subjects in group B either voluntarily purchased smart meters as individuals or received them by participating in an energy self-sufficient village program that was run by a local government, and were entitled to a donation as their compensation. With the analysis, group A was found to fit the extended technology acceptance model that includes perceived playfulness in addition to perceived ease of use and perceived usefulness. On the contrary, group B failed to fit the model well, but perceived usefulness was found to be relatively more important compared to group A. The results indicate that the residential energy groups' behavior changes are dependent on each group's characteristics, and group-specific DR design should be considered to improve the effectiveness of DR.

• Environmental and Resource Economics Review
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• v.27 no.3
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• pp.463-493
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• 2018

As the population structure of Korea changes with the increase of single households and elderly households, this may have effect on domestic energy consumption pattern. Our study analyzes whether the energy consumption of single and elderly households are distinguishable from those of general households. For empirical analysis, Household Energy Standing Survey panel data and regional fixed effect model are employed. The result strongly shows that single households consume more energy than other households. The consumption of single households from 40s to 60s was the highest. On the other hand, the effect of aging was different from energy sources. Electricity consumption of elderly household was more than other age groups, while oil consumption of elderly household was less than others. Gas and total energy consumptions turned out to be not much different among different age groups.