• Proceedings of the Korea Water Resources Association Conference
• /
• 한국수자원학회 2019년도 학술발표회
• /
• pp.3-3
• /
• 2019

최근 기후변화로 인한 집중호우, 가뭄 등 예측하기 어려운 사태가 발생하면서 깨끗하고 안정적인 용수공급 기술의 필요성이 대두되고 있다. 이에 IoT와 기존 물관리시스템을 결합한 스마트워터그리드 출범은 실시간으로 수요와 공급량의 정보를 취득하여 물 관리 효율성을 제고 할 수 있게 되었다. 실시간 수요량 자료를 이용하여 물 수요량 예측을 통한 최적의 물 공급량을 결정할 수 있다. 이 때 스마트워터그리드의 핵심 기술은 실시간으로 취득한 자료의 품질관리라 할 수 있다. 본 연구 대상지역인 영종도 112 블록에는 528개 AMI 스마트 미터를 이용하여 1시간 단위의 물 수요량 자료를 원격 검침하고 있다. 각 수용가에 설치된 AMI 센서를 통해 수집된 자료에는 오류를 포함할 수 있는데 통신 장애, 미터기 고장 및 교체 등으로 발생된다. 결측된 수요량 자료는 상수관망 수리해석에 사용되는 기본자료로서 비표본오차를 증가시켜 검정력과 정확성을 결여시키는 문제가 있다. 이에 본 연구에서는 수집된 자료를 가용할 수 있는 자료로 정제하고 대체하기 위해 완전히 관찰된 자료(complete data)만을 이용하여 각 시간에 따른 관경별, 용도별 그리고 요일별 수요패턴을 추정한다. 결측된 자료는 기존에 사용되는 평균대체법과 핫덱 대체(hot deck imputation) 등과 비교 검증한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• 제18권2호
• /
• pp.197-207
• /
• 2018

SMEs manufacturing Factory, which are small-scale production systems of various types, mass-produce and sell products in order to meet customer needs. This means that the company has an excessive amount of material supply to reduce the loss due to lack of inventory and high inventory maintenance cost. And the products that fail to respond to the demand are piled up in the management warehouse, which is the reality that the storage cost is incurred. To overcome this problem, this paper uses ARIMA model, a time series analysis technique, to predict demand in terms of seasonal factors. In this way, demand forecasting model based on economic order quantity model was developed to prevent stock shortage risk. Simulation is carried out to evaluate the effectiveness of the development model and to demonstrate the effectiveness of the development model as applied to SMEs in the future.

• Information and Communications Magazine
• /
• 제21권4호
• /
• pp.74-89
• /
• 2004

인터넷전화는 PC-to-Phone 혹은 Phone-to-Phone 형태로 제공되어 왔으나 품질, 사용의 불편함, 표준 착신번호의 부재 등으로 인해 활성화되지 못하였다. 그러나 올해를 기점으로 인터넷 전화역무가 도입되어 상기와 같은 문제가 일정부분 해결되면 인터넷전화시장은 급성장할 것으로 전망하는 전문가가 적지 않다. 본 고에서는 인터넷전화서비스의 시장 현황과 전망을 다룬다. 특히 인터넷전화시장의 전망을 가정고객의 가입 수요 확산 관점에서 살펴본다. 이를 위해 전문가 설문을 통해 인터넷전화의 활성화 이슈에 대해 살펴보고 이를 근거로 시나리오 기법과 Bass의 확산모형을 적용하여 수요예측을 실시한다. 즉 활성화 이슈를 중심으로 총 4개의 시나리오를 도출하고 각 시나리오에서 확산계수를 비교유추법으로 추정한다. 마지막으로 시나리오별로 도출된 확산과정은 전문가 설문의 결과와 비교하여 실현 가능성이 보다 높은 확산과정은 어떠한 것인지 살펴본다.(중략)

• The Journal of the Convergence on Culture Technology
• /
• 제10권4호
• /
• pp.693-700
• /
• 2024

As of the end of March 2022, the total area of domestic industrial complexes is 606 km2, which is only about 0.6% of the total land area. However, as of 2018, the annual energy consumption of domestic industrial complexes is 110,866.1 thousand TOE, accounting for 53.5% of the country's total energy consumption and 83.1% of the entire industrial sector energy consumption. In addition, industrial complexes have a significant impact on the environment, accounting for 45.1% of the country's total greenhouse gas emissions and 76.8% of industrial sector greenhouse gas emissions. Under this background, in this study, in order to contribute to the energy efficiency of industrial complexes, a prediction study on energy demand and supply for an industrial complex in Korea using machine learning was conducted. In addition, a simulator UI screen was designed to more efficiently convey information on energy demand/supply prediction results and energy consumption status. Among the machine learning algorithms, Multi-Layer Perceptron (MLP) was used, and Bayesian Optimization was applied as an optimization technique for the prediction model. The energy prediction model for the industrial complex built in this study showed a prediction accuracy of 87.90% for compressed air demand and 99.54% for the flow rate available for the public air compressor.

• KEPCO Journal on Electric Power and Energy
• /
• 제2권3호
• /
• pp.391-395
• /
• 2016

In the last few decades, global electricity consumption has dramatically increased and has become drastically fluctuating and uncertain causing blackout. Due to the unexpected peak electricity demand, we need significant electricity supply. The solutions to these problems are smart grid system which is envisioned as future power system. Smart grid system can reduce electricity peak demand and induce effective electricity consumption through various price policies, demand response (DR) control methodologies, and state-of-the-art smart equipments in order to optimize electricity resource usage in an intelligent fashion. Demand response (DR) is one of the key technologies to enable smart grid. In this paper, we propose greedy technique for demand response smart grid system. The proposed scheme focuses on minimizing electricity bills, preventing system blackout and sacrificing user convenience.

• Korean Journal of Construction Engineering and Management
• /
• 제24권6호
• /
• pp.66-73
• /
• 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.

• KIPS Transactions on Software and Data Engineering
• /
• 제3권7호
• /
• pp.263-270
• /
• 2014

This paper suggests a method for predicting a box-office success of the film. Lately, as the growth of the film industry, a variety of studies for the prediction of market demand is being performed. The product life cycle of film is relatively short cultural goods. Therefore, in order to produce stable profits, marketing costs before opening as well as the number of screen after opening need a plan. To fulfill this plan, the demand for the product and the calculation of economic profit scale should be preceded. The cases of existing researches, as a variable for predicting, primarily use the factors of competition of the market or the properties of the film. However, the proportion of the potential audiences who purchase the goods is relatively insufficient. Therefore, in this paper, in order to consider people's perception of a movie, Twitter was utilized as one of the survey samples. The existing variables and the information extracted from Twitter are defined as off-line and on-line element, and applied those two elements in machine learning by combining. Through the experiment, the proposed predictive techniques are validated, and the results of the experiment predicted the chance of successful film with about 95% of accuracy.

• Journal of IKEEE
• /
• 제23권1호
• /
• pp.120-126
• /
• 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 Energy Engineering
• /
• 제11권4호
• /
• pp.317-323
• /
• 2002

This paper proposed the calculation method of the generation operating avoided cost to cost-effectiveness evaluation of energy conservation programs that compounded the Proxy Plant Method and Load Decrement Method. This method introduced an operating index of the Energy Efficiency Demand-Side Management (EEDSM) resources based on the end-user's behaviors on the electricity power usage. The operation index is applied to calculate the hourly operating capacity of diffused high-efficiency appliances. And the operating capacity on the peak load hours for reference load is computed through the reduction of the peak load that contributes to that hour. Also, the proposed method evaluated the effect of EEDSM resources. The IEEE-RTS is adopted as a sample system to analyze impacts of an EEDSM. This paper, we have analyzed the effect of EEDSM upon the changes in the generation of generator, generation cost and the system marginal price (SMP). This method can be used to evaluate the impact of the diffused DSM resource and to estimate the impact in short-term EEDSM program. Further, result of the calculation can be utilized to pabulum for effect analysis of EEDSM resources.

• Journal of Korea Society of Industrial Information Systems
• /
• 제22권4호
• /
• pp.19-28
• /
• 2017

Companies and Governments have Mainly used the Delphi Technique to Understand Research or Technology Trends. Because this Technique has the Disadvantage of Consuming a Large Amount of Time and Money, this Study Attempted to Understand and Predict Science and Technology Trends using the Topic Modeling Technique Latent Dirichlet Allocation (LDA). To this end, 20 Specific Artificial Intelligence (AI) Technologies were Extracted From the Abstracts of the US Patent Documents on AI. With Regard to the Extracted Specific Technologies, Core Technologies were Identified, and then these were Divided into Hot and Cold Technologies though a Trend Analysis on their Annual Proportions. Text/Word Searching, Computer Management, Programming Syntax, Network Administration, Multimedia, and Wireless Network Technology were Derived From Hot Technologies. These Technologies are Key Technologies that are Actively Studied in the Field of AI in Recent Years. The Methodology Suggested in this Study may be used to Analyze Trends, Derive Policies, or Predict Technical Demands in Various Fields such as Social Issues, Regional Innovation, and Management.