• Title/Summary/Keyword: Demand forecasting

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A Study on the Building Energy Analysis and Algorithm of Energy Management System (건물 에너지 분석 및 에너지 관리 시스템 알고리즘에 관한 연구)

  • Han, Byung-Jo;Park, Ki-Kwang;Koo, Kyung-Wan;Yang, Hai-Won
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.58 no.4
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    • pp.505-510
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    • 2009
  • In this paper, building energy analysis and energy cost of power stand up and demand control over the power proposed to reduce power demand. Through analysis of the load power demand special day were able to apply the pattern. In addition, the existing rate of change of load forecasting to reduce the large errors were not previously available data. And daily schedules and special day for considering the exponential smoothing methods were used. Previous year's special day and the previous day due to the uncertainty of the load and the model components were considered. The maximum demand power control simulation using the fuzzy control of power does not exceed the contract. Through simulation, the benefits of the proposed energy-saving techniques were demonstrated.

Optimal Electric Energy Subscription Policy for Multiple Plants with Uncertain Demand

  • Nilrangsee, Puvarin;Bohez, Erik L.J.
    • Industrial Engineering and Management Systems
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    • v.6 no.2
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    • pp.106-118
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    • 2007
  • This paper present a new optimization model to generate aggregate production planning by considering electric cost. The new Time Of Switching (TOS) electric type is introduced by switching over Time Of Day (TOD) and Time Of Use (TOU) electric types to minimize the electric cost. The fuzzy demand and Dynamic inventory tracking with multiple plant capacity are modeled to cover the uncertain demand of customer. The constraint for minimum hour limitation of plant running per one start up event is introduced to minimize plants idle time. Furthermore; the Optimal Weight Moving Average Factor for customer demand forecasting is introduced by monthly factors to reduce forecasting error. Application is illustrated for multiple cement mill plants. The mathematical model was formulated in spreadsheet format. Then the spreadsheet-solver technique was used as a tool to solve the model. A simulation running on part of the system in a test for six months shows the optimal solution could save 60% of the actual cost.

A Study on the Demand Forecasting Control using A Composite Fuzzy Model (복합 퍼지모델을 이용한 디맨드 예측 제어에 관한 연구)

  • Kim, Chang-Il;Seong, Gi-Cheol;Yu, In-Geun
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.51 no.9
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    • pp.417-424
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    • 2002
  • This paper presents an industrial peak load management system for the peak demand control. Kohonen neural network and wavelet transform based techniques are adopted for industrial peak load forecasting that will be used as input data of the peak demand control. Firstly, one year of historical load data of a steel company were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are applied with Biorthogonal 1.3 mother wavelet in order to forecast the peak load of one minute ahead. In addition, for the peak demand control, composite fuzzy model is proposed and implemented in this work. The results are compared with those of conventional model, fuzzy model and composite model, respectively. The outcome of the study clearly indicates that the composite fuzzy model approach can be used as an attractive and effective means of the peak demand control.

A Study on the Future Air Traffic Demand in Busan Metropolitan Area (부산권 항공수요예측 연구)

  • Kim, Byung-Jong;Lee, Min-Hee
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.16 no.1
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    • pp.46-57
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    • 2008
  • Since the 90's, Korean Air transport market has been more expanded because of economic growth, the construction of airport infrastructure, and the advent of low cost carrier. Especially, the air traffic demand in Busan metropolitan area has been increasing steadily. Therefore, in this paper, we developed a new forecasting model which could expect the future air traffic demand in Busan area. This model is developed by regression analysis using social-economic variables such as GRDP, income, and the number of people, and dummy variables, for instance, KTX opening, Japan economic depression, SARS and so on. Result from demand forecasting by this new model suggests that the new airport system is needed in order to sustain the increasing air traffic demand in Busan area.

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Scenario Analysis of Natural Gas Demand for Electricity Generation in Korea (전력수급기본계획의 불확실성과 CO2 배출 목표를 고려한 발전용 천연가스 장기전망과 대책)

  • Park, Jong-Bae;Roh, Jea Hyung
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.63 no.11
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    • pp.1503-1510
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    • 2014
  • This study organizes scenarios on the power supply plans and electricity load forecasts considering their uncertainties and estimates natural gas quantity for electricity generation, total electricity supply cost and air pollutant emission of each scenario. Also the analysis is performed to check the properness of government's natural gas demand forecast and the possibility of achieving the government's CO2 emission target with the current plan and other scenarios. In result, no scenario satisfies the government's CO2 emission target and the natural gas demand could be doubled to the government's forecast. As under-forecast of natural gas demand has caused the increased natural gas procurement cost, it is required to consider uncertainties of power plant construction plan and electricity demand forecast in forecasting the natural gas demand. In addition, it is found that CO2 emission target could be achieved by enlarging natural gas use and demand-side management without big increase of total costs.

Studies on the Forecasting of Demand for Natural Recreation Forest (자연휴양림의 수요예측에 관한 연구)

  • 김태진;안성노;변우혁
    • Journal of the Korean Institute of Landscape Architecture
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    • v.21 no.3
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    • pp.51-64
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    • 1993
  • Meeting the rapidly increasing demand for natural outdoor recreation, Korea Forestry Administration established 26 places of $\ulcorner$Natural Recreation Forest$\lrcorner$ zones. By 2000 year, 100 zones were planned to cover the entire country. But there was no accurate information about demand of $\ulcorner$Natural Recreation Forest$\lrcorner$. Therefore, this study was carried out to forecast the quantitive demand of $\ulcorner$Natural Recreation Forest$\lrcorner$. To forecast the 'demand of 2001 year, forecasting unit was determined to $\ulcorner$Visitor. Day$\lrcorner$, and three quantifing methods were applied. The results of demand by each forecating method were as follows: 1) Questionnaire survey method for willingness to participate was 16,651,000(visitor. day). 2) application of similiar situation threshold method was 14,540,000(visitor. day). 3) Demand partition method by secondary data was 10,775,000(visitor, day). Comprised of these results. The scope estimate of $\ulcorner$Natural Recreation Forest$\lrcorner$ demand was proposed as 8,110,000(Minimum) - 27,088,000(Miximum). The point estimate of demand which were proposed as strategic guidelines was 16,651,000(visitor. day). These results implied that recently announced 111 predetermined $\ulcorner$Natural Recreation Forest$\lrcorner$ zones supposed to be overcrowded meeting the forcasted demand level of 2001 year.

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A Development of Time-Series Model for City Gas Demand Forecasting (도시가스 수요량 예측을 위한 시계열 모형 개발)

  • Choi, Bo-Seung;Kang, Hyun-Cheol;Lee, Kyung-Yun;Han, Sang-Tae
    • The Korean Journal of Applied Statistics
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    • v.22 no.5
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    • pp.1019-1032
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    • 2009
  • The city gas demand data has strong seasonality. Thus, the seasonality factor is the majority for the development of forecasting model for city gas supply amounts. Also, real city gas demand amounts can be affected by other factors; weekday effect, holiday effect, the number of validity day, and the number of consumptions. We examined the degree of effective power of these factors for the city gas demand and proposed a time-series model for efficient forecasting of city gas supply. We utilize the liner regression model with autoregressive regression errors and we have excellent forecasting results using real data.

Secure power demand forecasting using regression analysis on Intel SGX (회귀 분석을 이용한 Intel SGX 상의 안전한 전력 수요 예측)

  • Yoon, Yejin;Im, Jong-Hyuk;Lee, Mun-Kyu
    • The Journal of Korean Institute of Next Generation Computing
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    • v.13 no.4
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    • pp.7-18
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    • 2017
  • Electrical energy is one of the most important energy sources in modern society. Therefore, it is very important to control the supply and demand of electric power. However, the power consumption data needed to predict power demand may include the information about the private behavior of an individual, the analysis of which may raise privacy issues. In this paper, we propose a secure power demand forecasting method where regression analyses on power consumption data are conducted in a trusted execution environment provided by Intel SGX, keeping the power usage pattern of users private. We performed experiments using various regression equations and selected an equation which has the least error rate. We show that the average error rate of the proposed method is lower than those of the previous forecasting methods with privacy protection functionality.

An Study of Demand Forecasting Methodology Based on Hype Cycle: The Case Study on Hybrid Cars (기대주기 분석을 활용한 수요예측 연구: 하이브리드 자동차의 사례를 중심으로)

  • Jun, Seung-Pyo
    • Journal of Korea Technology Innovation Society
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    • v.14 no.spc
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    • pp.1232-1255
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    • 2011
  • This paper proposes a model for demand forecasting that will require less effort in the process of utilizing the new product diffusion model while also allowing for more objective and timely application. Drawing upon the theoretical foundation provided by the hype cycle model and the consumer adoption model, this proposed model makes it possible to estimate the maximum market potential based solely on bibliometrics and the scale of the early market, thereby presenting a method for supplying the major parameters required for the Bass model. Upon analyzing the forecasting ability of this model by applying it to the case of the hybrid car market, the model was confirmed to be capable of successfully forecasting results similar in scale to the market potential deduced through various other objective sources of information, thus underscoring the potentials of utilizing this model. Moreover, even the hype cycle or the life cycle can be estimated through direct linkage with bibliometrics and the Bass model. In cases where the hype cycles of other models have been observed, the forecasting ability of this model was demonstrated through simple case studies. Since this proposed model yields a maximum market potential that can also be applied directly to other growth curve models, the model presented in the following paper provides new directions in the endeavor to forecast technology diffusion and identify promising technologies through bibliometrics.

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A Study for Sales and Demand Forecasting Model Using Wavelet Neural Networks (웨이블렛 신경회로망을 이용한 상품 수요 예측 모형에 관한 연구)

  • Lee, Jae-Hyun
    • The Journal of the Korea institute of electronic communication sciences
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    • v.9 no.1
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    • pp.131-136
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    • 2014
  • In this paper, we develop a fashion products demand forecasting algorithm using ARIMA model and Wavelet Neural Networks model. To show effectiveness of the proposed method, we analyzed characteristics of time-series data collected in "H" company during 2008-2012 and then performed the proposed method through various analyses. As noted in experimental results, the performance of three types model such as ARIMA, Wavelet Neural Networks and ARIMA + Wavelet Neural Networks show 5.179%, 4.553%, and 4.448.% with respect to MAPE(Mean Absolute Percentage Error), respectively. Thus, it is noted that the proposed method can be used to predict fashion products demand for efficient of operation.