• International journal of advanced smart convergence
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• v.10 no.4
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• pp.59-65
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• 2021

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1843-1850
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• 2014

In this paper, we investigate a group building based power consumption scheduling to minimize the electricity cost. We consider the demand shift to reduce the peak load and suggest the compensation function reflecting the relationship between the change of the building demand and the occupants' comfort. Using that, the electricity cost minimization problem satisfied the convexity is formulated, and the optimal power consumption scheduling algorithm is proposed based on the iterative method. Extensive simulations show that the proposed algorithm achieves the group management gain compared to the individual building operation by increasing the degree of freedom for the operation.

• 한국신재생에너지학회:학술대회논문집
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• 2009.11a
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• pp.243-246
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• 2009

Fuel consumption measurement of Environmentally Friendly Vehicles is considerably different form internal combustion engine vehicle such as Carbon balance method. A practical method of fuel Consumption measurement has been developed for Hydrogen fuel cell vehicles and Electricity Vehicles. The purpose of this research is to measure the fuel consumption of hydrogen fuel cell vehicles and Electricity Vehicles on chassis-dynamometer and to give information when the research is intended to develop method to measure Energy consumption.

• Proceedings of the KIEE Conference
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• summer
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• pp.165-167
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• 2004

End-user electric power consumption trends shows various load curves dependant on industry, contract, season, day and time. Analysis of end-user electric power consumption trends has a key role to efficiently meet electricity demand. There are several factors of change in electricity demand such as the change of weather, international conflict, and industrial trends during summer. This paper has analyzed the analysis the end-user electric power consumption trends using the load curve during international conflict. We observed that international conflict decreased electric demand by $5.4\%$. This increase is not significant, and therefore we conclude that the international conflict has not greatly affected Korea's electricity demands. This paper provides useful information so as to mon: efficiently perform demand side management.

• Journal of Advanced Navigation Technology
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• v.16 no.4
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• pp.709-716
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• 2012

Recently, smartgrid has interested in enable to existing electrical grid to supplying stably and efficient energy management. Smartgrid environment using PLC is transmit PLC module collected electricity consumption information in each house from PLC module to server. This communication process can occurred security threats such as personal information leak of consumer, electrical grid paralysis. In this paper, we propose efficient electricity consumption information transmission protocol with ID-based key distribution method for respond to security threats.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.12
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• pp.103-107
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• 2014

Together with housings, general buildings and industrial facilities, multi-purpose complexes are equipped with various and special equipment. They are often used by many unspecified people, which causes an increase in annual electricity consumption. Because of this, a great amount of money has been spent for electric charge, far more in excess of the budget, so a reasonable electricity rate needs to be estimated. In this study, we surveyed the power consumption, average power use, and annual electricity bill of multi-purpose complexes in the past five years. To see the general tendency of the survey, we conducted a statistical analysis with such parameters as average, maximum, and minimum values. Through regression analysis, we could see the trend of the survey in linear way. Based on the survey, we have developed an electric-rate calculation program to estimate the next year's budget on electricity.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.319-330
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• 2019

Forecasting the electricity consumption through analyzing the past electricity consumption a advantageous for energy planing and policy. Machine learning is widely used as a method to predict electricity consumption. Among them, ensemble learning is a method to avoid the overfitting of models and reduce variance to improve prediction accuracy. However, ensemble learning applied to daily data shows the disadvantages of predicting a center value without showing a peak due to the characteristics of ensemble learning. In this study, we overcome the shortcomings of ensemble learning by considering the temperature trend. We compare nine models and propose a model using random forest with the linear trend of temperature.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.115-129
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• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

• Journal of Families and Better Life
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• v.26 no.4
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• pp.1-11
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• 2008

This study investigated consumers' energy saving behavior and perceptions concerning the effectiveness of their energy saving behavior. A nation wide survey was conducted involving 2000 households in urban areas and the data from 1767 households were used for the analysis. excluding cases with incomplete responses. Descriptive analysis, factor analysis, and regression analysis were applied. The results were as follows. First, electricity saving behavior was classified into three categories: Thrift (reducing energy consumption), Purchase (buying energy saving appliances), and Control (checking the energy consumption). Second, consumers rated Thrift as the best way to save energy. Third, education, age, and household income were significantly related to energy saving behavior and perceptions on the effectiveness of energy saving behavior. Consumers using above average levels of electricity tended not to practice energy saving behavior and not to positively evaluate effectiveness of the energy saving behavior. Lastly, the implications for public policies to promote energy saving behavior are suggested.

• 한국신재생에너지학회:학술대회논문집
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• 2006.06a
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• pp.343-346
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• 2006

Although renewable energy sources are more environmentally friendly than fossil energy sources, it is far more costly, considering current technological standards. It would not present many competitive advantages in the power market. If the renewable electricity is viable in the market, the government should take 'visible' actions to compensate production costs. Popular policies, such as Feed-In-Tariff and Renewable Portfolio Standards, can help to attract investors into generators of renewable electricity. But presently, they are mainly financed through a undifferentiated increase of electricity bills and occasionally confronted with the opposition of the electricity consumers. And most policies tend to focus on increasing the supply of renewable electricity with little consideration toward elevating the motivation of consumers. This study evaluates the potential of environmentally friendly energy consumption and examines the 'green pricing' program which realize the potential.