• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.322-323
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• 2022

The most necessary thing for the optimal operation of a water purification plant is to accurately predict the pattern and amount of tap water used by consumers. The required amount of tap water should be delivered to the drain using a pump and stored, and the required flow rate should be supplied in a timely manner using the minimum amount of electrical energy. The short-term demand forecasting required from the point of view of energy optimization operation among water purification plant volume predictions has been made in consideration of seasons, major periods, and regional characteristics using time series analysis, regression analysis, and neural network algorithms. In this paper, we analyzed energy management methods through AI-based complex sensor applicability analysis such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units), which are types of cyclic neural networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1005-1012
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• 2021

Recently, the use of various location-based services-based location information systems using maps on the web has been expanding, and there is a need for a monitoring system that can check power demand in real time as an alternative to energy saving. In this study, we developed a deep learning real-time virtual power demand prediction web system using open source-based mapping service to analyze and predict the characteristics of power demand data using deep learning. In particular, the proposed system uses the LSTM(Long Short-Term Memory) deep learning model to enable power demand and predictive analysis locally, and provides visualization of analyzed information. Future proposed systems will not only be utilized to identify and analyze the supply and demand and forecast status of energy by region, but also apply to other industrial energies.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.419-430
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• 2020

This paper investigated methods to improve the forecasting accuracy of the electricity consumption prediction model. Currently, the demand for electricity has continuously been rising more than ever. Since the industrial sector uses more electricity than any other sectors, the importance of a more precise forecasting model for manufacturing sites has been highlighted to lower the excess energy production. We propose a double encoder-decoder model, which uses two separate encoders and one decoder, in order to adapt both long-term and short-term data for better forecasts. We evaluated our proposed model on our electricity power consumption dataset, which was collected in a manufacturing site of Sehong from January 1st, 2019 to June 30th, 2019 with 1 minute time interval. From the experiment, the double encoder-decoder model marked about 10% reduction in mean absolute error percentage compared to a conventional encoder-decoder model. This result indicates that the proposed model forecasts electricity consumption more accurately on manufacturing sites compared to an encoder-decoder model.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.38-39
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• 2006

KEPCO is executing a project in Cambodia consisting of two components, "Power Development Master Plan and Institutional Strengthening" and "Greater Mekong Sub-region (GMS) Power Project Preparation" upon request by the government of the Ministry of Industry, Mines and Energy of Cambodia, contracted on $29^{th}$ September 2005. This paper includes a basic design and review of economical efficiency for constructing the two (2) cross border 115 kV transmission lines, which run from the border of Vietnam and Laos to Cambodia, and four (4) 115kV substations. The main contents of the paper include the process of design and results of a review of line route selection, tower and hardware design regarding transmission line design, as well as the type of substation, and arrangement and specifications of equipment with expects to substation design. Also, long-term demand forecasting, and an economic analysis of the project area are included.

• Environmental and Resource Economics Review
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• v.23 no.4
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• pp.583-615
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• 2014

Including the rolling black out in 2011, Korea has suffered from rapid increase of electricity consumption and demand forecasting failure for last five years. In addition, because of the Fukushima disaster, high fuel prices, and introduction of new generation sources such as renewables, the uncertainty on a power supply strategy increases. Consequently, a stable power supply becomes the new agenda and a revisino of strategy for new power generation sources is needed. In the light of this, we appraises the sixth basic plan for long term electricity demand and supply considering the changes of foreign and domestic conditions. We also simulate a strategy for the new power generation sources using a portfolio analysis method. As results, a diversity of power generation sources will increase and the share of renewable power generation will be surged on the assumptions of a cost reduction of renewable power sources and an increase of fuel costs. Particularly, on the range of a risk level(standard deviation) from 0.06 and 0.09, the efficient frontier has the most various power sources. Besides, the existing power plan is not efficient so that an improvement is needed. Lastly, the development of an electricity storage system and energy management system is necessary to make a stable and efficient power supply condition.

• 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.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.66-73
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• 2023

The construction industry suffers losses due to failures in demand forecasting due to price fluctuations in construction raw materials, increased user costs due to project cost changes, and lack of forecasting system. Accordingly, it is necessary to improve the accuracy of construction raw material price forecasting. This study aims to predict the price of construction raw materials and verify applicability through the improvement of the Data Refactor technique. In order to improve the accuracy of price prediction of construction raw materials, the existing data refactor classification of low and high frequency and ARIMAX utilization method was improved to frequency-oriented and ARIMA method utilization, so that short-term (3 months in the future) six items such as construction raw materials lumber and cement were improved. ), mid-term (6 months in the future), and long-term (12 months in the future) price forecasts. As a result of the analysis, the predicted value based on the improved Data Refactor technique reduced the error and expanded the variability. Therefore, it is expected that the budget can be managed effectively by predicting the price of construction raw materials more accurately through the Data Refactor technique proposed in this study.

• Journal of the Korean Society for Marine Environment & Energy
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• v.1 no.2
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• pp.96-108
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• 1998

The management of water quality and fishery resources with a major environmental problem in eutrophic coastal sea is studied. The numerical experiments using the water-sediment quality model (WSQM) were carried out for the management of water quality at the Seto Inland Sea in Japan. The results of long-term water quality simulation showed responses of seawater quality to input loads to vary in different localities. A formula roughly forecasting water qualify to estimate the effect of loading abatement was proposed. The simulation for the improvement of seawater quality showed the abatements of nutrient loads such as total phosphorus (TP) and total nitrogen (TN) as well as organic loads such as chemical oxygen demand (COD) to be peformed in the eastern Seto Inland Sea from Bisan Seto to Osaka Bay. On the other hand, it is indicated that the increase of loading leads to the increase of primary production. while not straightly to the increase of fish production for the catch of fisheries.

• Journal of IKEEE
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• v.22 no.3
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• pp.822-828
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• 2018

Recently, energy issues such as massive blackout due to increase in power consumption have been emerged, and it is necessary to improve the accuracy of prediction of power consumption as a solution for these problems. In this study, we investigate the difference between the actual power consumption and the predicted power consumption through the deep learning- based power consumption forecasting experiment, and the possibility of adjusting the power reserve ratio. In this paper, the prediction of the power consumption based on the deep learning can be used as a basis to reduce the power reserve ratio so as not to excessively produce extra power. The deep learning method used in this paper uses a learning model of long-short-term-memory (LSTM) structure that processes time series data. In the computer simulation, the generated power consumption data was learned, and the power consumption was predicted based on the learned model. We calculate the error between the actual and predicted power consumption amount, resulting in an error rate of 21.37%. Considering the recent power reserve ratio of 45.9%, it is possible to reduce the reserve ratio by 20% when applying the power consumption prediction algorithm proposed in this study.