• 에너지공학
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• 제28권4호
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• pp.82-93
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• 2019

본 연구에서는 기존 열수요 예측 시스템이 공휴일과 같은 특정 일자의 열수요 예측율이 저하되는 문제점을 개선하기 위해 새로운 모델을 제안한다. 제안된 모델은 사계절 혼합형 신경망 모델(Four Season Mixed Heat Demand Prediction Neural Network Model)로서 열수요 예측율 상승하였고, 특히 예측일 유형별(평일/주말/공휴일) 열수요 예측율이 크게 증가하였다. 제안된 모델은 다음과 같은 과정을 통해 선정되었다. 특정 계절에 예측일 유형별로 고른 오차를 갖는 모델을 선정하여 전체 예측 모델을 구성한다. 학습 시간의 단축과 과도학습을 방지하기 위해 구조적으로 단순화된 서로 다른 4개의 모델을 각각 학습한 후에 다양한 조합을 통해 최적의 예측 오차를 보여주는 모델을 선정하였다. 모델의 출력은 예측일의 24시간의 시간대별 열수요이며 총합은 일일 총열수요이다. 이 예측값을 통해 효율적인 열공급 계획을 수립 할 수 있으며, 목적에 따라 출력값을 선택하여 활용할 수 있다. 제안된 모델의 일일 열 총수요 예측의 경우, 전체 MAPE(Mean Absolute Percentage Error, 평균 절대 비율 오차)가 개별 모델의 5.3~6.1%에서 5.2%로 향상되었고, 공휴일 열수요예측은 4.9~7.9%에서 2.9%로 크게 개선되었다. 본 연구에서는 한국 지역난방공사에서 제공한 특정 아파트 단지의 34개월 분량의(2015년 1월~ 2017년10월) 시간단위 열수요 데이터를 활용하였다.

• 조명전기설비학회논문지
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• 제23권9호
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• pp.47-54
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• 2009

'08.12$\sim$'09. 2월 동절기에 세계경제위축 심화에 따른 수출 급감으로 제조업은 마이너스 성장을 기록함에 따라 우리나라 전력소비의 53[%]를 차지하는 산업용 전력이 약 7[%]의 감소율을 나타내고 있다. 또한 국내에는 내수경기침체에 따른 전력소비 감소와 평년 대비 기온 상승으로 인한 난방수요 감소로 일일 전력수요 패턴에 많은 변화를 보이고 있다. 본 연구에서는 동절기의 최대전력, 평균전력, 상대계수에 의한 전력수요 패턴, 시간대별 온도민감도 분석을 통하여 최대전력은 GDP 성장률 보다는 기온변화에 민감한 반면, 평균전력은 GDP 성장률에 비례하여 감소하는 추세를 보이고 있는 것으로 분석되었다. 이 자료를 근거로 동 하절기의 최대전력과 평균전력의 정확한 전력 수요 예측으로 전력계통을 경제적이고 안정적으로 운영할 수 있다고 여겨진다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.243-243
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• 2015

Low-flow simulation and forecasting is one of the emerging issues in hydrology due to the increasing demand of water in dry periods. Even though low-flow simulation and forecasting remains a difficult issue for hydrologists better simulation and earlier prediction of low flows are crucial for efficient water management. The UN has never stated that South Korea is in a water shortage. However, a recent study by MOLIT indicates that Korea will probably lack water by 4.3 billion m3 in 2020 due to several factors, including land cover and climate change impacts. The two main situations that generate low-flow events are an extended dry period (summer low-flow) and an extended period of low temperature (winter low-flow). This situation demands the hydrologists to concentrate more on low-flow hydrology. Korea's annual average precipitation is about 127.6 billion m3 where runoff into rivers and losses accounts 57% and 43% respectively and from 57% runoff discharge to the ocean is accounts 31% and total water use is about 26%. So, saving 6% of the runoff will solve the water shortage problem mentioned above. The main objective of this study is to present the hydrological modelling approach for low-flow simulation and forecasting using a model that have a capacity to represent the real hydrological behavior of the catchment and to address the water management of summer as well as winter low-flow. Two lumped hydrological models (GR4J and CAT) will be applied to calibrate and simulate the streamflow. The models will be applied to Seolmacheon catchment using daily streamflow data at Jeonjeokbigyo station, and the Nash-Sutcliffe efficiencies will be calculated to check the model performance. The expected result will be summarized in a different ways so as to provide decision makers with the probabilistic forecasts and the associated risks of low flows. Finally, the results will be presented and the capacity of the models to provide useful information for efficient water management practice will be discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3923-3942
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• 2022

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for small-scale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation.

• 전기학회논문지
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• 제65권3호
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• pp.404-410
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• 2016

This paper proposed a program of an energy storage system(ESS) for peak shaving of high-speed railway substations The peak shaving saves cost of equipment and demand cost of the substation. To reduce the peak load, it is very important to know when the peak load appears. The past data based load profile forecasting method is easy and applicable to customers which have relatively fixed load profiles. And an optimal scheduling method of the ESS is helpful in reducing the electricity tariff and shaving the peak load efficiently. Based on these techniques, MS. NET based peak shaving program is developed. In case study, a specific daily load profile of the local substation was applied and simulated to verify performance of the proposed program.

• 대한영양사협회학술지
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• 제25권1호
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• pp.44-58
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• 2019

Predicting the number of meals in a foodservice organization is an important decision-making process that is essential for successful food production, such as reducing the amount of residue, preventing menu quality deterioration, and preventing rising costs. Compared to other demand forecasts, the menu of dietary personnel includes diverse menus, and various dietary supplements include a range of side dishes. In addition to the menus, diverse subjects for prediction are very difficult problems. Therefore, the purpose of this study was to establish a method for predicting the number of meals including predictive modeling and considering various factors in addition to menus which are actually used in the field. For this purpose, 63 variables in eight categories such as the daily available number of people for the meals, the number of people in the time series, daily menu details, weekdays or seasons, days before or after holidays, weather and temperature, holidays or year-end, and events were identified as decision variables. An ensemble model using six prediction models was then constructed to predict the number of meals. As a result, the prediction error rate was reduced from 10%~11% to approximately 6~7%, which was expected to reduce the residual amount by approximately 40%.

• Journal of Electrical Engineering and Technology
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• 제12권5호
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• pp.1709-1718
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• 2017

This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The prime objective of the DASO is the minimization of the daily production cost in power systems with high penetration scenarios of variable generation. Furthermore, energy storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO). DASO simulation results are used as the base starting-point values in the hour-ahead online rolling RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid flexibility, to balance out the deviation between predicted and actual net load demand values. Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind forecasting.