• Journal of Fisheries and Marine Sciences Education
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• v.25 no.1
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• pp.192-197
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• 2013

To overcome the mismatch of heat demand and heat supply is important as considering point on heat utilizing aspects in Urban area. At this point, It need to know the plan of heat networks on the heat balance aspects. The purpose of this study is to know the method of heat evaluation on heat network around costal area. It is need to building uses to calculate the amounts of heat demand. 25 different types of building uses were supplied, but it was reclassified 10 types and calculated the amounts of heat demand in the costal area. The results was described on the area with GIS mapping.

• IE interfaces
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• v.24 no.4
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• pp.373-378
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• 2011

High level of accuracy in forecasting heat demand of each district is required for operating and managing the district heating efficiently. Heat demand has a close connection with the demands of the previous days and the temperature, general demand forecasting methods may be used forecast. However, there are some exceptional situations to apply general methods such as the exceptional low demand in weekends or vacation period. We introduce a new method to forecast the heat demand to overcome these situations, using the linearities between the demand and some other factors. Our method uses the temperature and the past 7 days' demands as the factors which determine the future demand. The model consists of daily and hourly models which are multiple linear regression models. Appling these two models to historical data, we confirmed that our method can forecast the heat demand correctly with reasonable errors.

• Environmental and Resource Economics Review
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• v.11 no.3
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• pp.447-463
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• 2002

The heat demand prediction is an essential issue in management of district heating system. Without an accurate prediction through the lead-time period, it might be impossible to make a rational decision on many issues such as heat production scheduling and heat exchange among the plants which are very critical for the district heating company. The heat demand varies with the temperature as well as the time nonlinearly. And the parametric specification of the heat demand model would cause a misspecification bias in prediction. A nonparametric model for the short-term heat demand prediction has been developed as an alternative to avoiding the misspecification error and tested with the actual data. The prediction errors are reasonably small enough to use the model to predict a few hour ahead heat demand.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.82-93
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• 2019

In this study, a new model is proposed to improve the problem of the decline of predict rate of heat demand on a particular date, such as a public holiday for the conventional heat demand forecasting system. The proposed model was the Four Season Mixed Heat Demand Prediction Neural Network Model, which showed an increase in the forecast rate of heat demand, especially for each type of forecast date (weekday/weekend/holiday). The proposed model was selected through the following process. A model with an even error for each type of forecast date in a particular season is selected to form the entire forecast model. To avoid shortening learning time and excessive learning, after each of the four different models that were structurally simplified were learning and a model that showed optimal prediction error was selected through various combinations. The output of the model is the hourly 24-hour heat demand at the forecast date and the total is the daily total heat demand. These forecasts enable efficient heat supply planning and allow the selection and utilization of output values according to their purpose. For daily heat demand forecasts for the proposed model, the overall MAPE improved from 5.3~6.1% for individual models to 5.2% and the forecast for holiday heat demand greatly improved from 4.9~7.9% to 2.9%. The data in this study utilized 34 months of heat demand data from a specific apartment complex provided by the Korea District Heating Corp. (January 2015 to October 2017).

• The Journal of Bigdata
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• v.5 no.2
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• pp.17-27
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• 2020

District heating system supplies heat from low-cost high-efficiency heat production facilities to heat demand areas through a heat pipe network. For efficient heat supply system operation, it is important to accurately predict the heat demand within the region and optimize the heat production plan accordingly. In this study, a heat demand forecasting model is proposed considering real-time calorimeter information from local heat demands. Previous models considered ambient temperature and heat demand history data to predict future heat demands. To improve forecast accuracy, the proposed heat demand forecast model added big data from real-time calorimeters installed in the heat demands within the target region. By employing calorimeter information directly in the model, it is expected that the proposed forecast model is to reflect heat use pattern of each demand. Computational experiemtns based on the actual heat demand data shows that the forecast accuracy of the proposed model improved when the calorimeter big data is reflected.

• Plant Journal
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• v.15 no.1
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• pp.31-37
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• 2019

In this study, to improve the accuracy of forecast of heat demand in the district heating system, this study applied heat demand performance among the main factors of district heating demand forecast in Pankyo area as the heat sales information of the user facility instead of existing heat source facility heat supply information, and compared the existing method with the accuracy based on the actual value. As a result of comparing the difference of the forecasts values of the existing and changed methods based on the performance values over the one week (2018.01.08 ~ 01.14) during the hot water peak, the relative error decreased from 7% to 3% The relative error between the existing and revised forecasts was 9% and 4%, respectively, for the five-month cumulative heat demand from February to February 2018, Also, in case of the weekend where the demand of heat is differentiated, the relative error of the forecasts value is consistently reduced from 10% to 5%.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.12
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• pp.570-575
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• 2006

This paper develops methodology in order to consider CHP(Combined Heat and Power) capacity in the Basic Plan of Long Term Electricity Supply & Demand. We develop generating cost of CHP considering electric and heat. Also we develop mixed load duration curve which includes the electric load and heat load and then apply CHP capacity to SCM(Screening Curve Method) considering CHP feature. Accordingly, it decide the optimal CHP capacity in the Basic Plan of Long Term Electricity Supply & Demand. Also, We perform the sensitivity analysis according to cost variation.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• Journal of Energy Engineering
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• v.10 no.4
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• pp.327-334
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• 2001

Heat is wasted by unconcern in industrial complex. This paper presented for using waste heat, which investigated step by step from searching waste heat to starting construction before and directly applied for the using waste heat in the actual site. Especially, using heat is assessed by investigation of heat supply and demand. Design of heat transportation system was made base on analysis of heat balance between demand and supply, which was analyzed by economical efficiency and property. Payback-period on investment was 1,909 years that was comparatively a short period of time in assessment.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2000.05a
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• pp.146-152
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• 2000

As an improvement in the standard of living and economic growth the demand for air conditioning equipment is increasing rapidly. Nowadays air conditioning equipments are being used for industry large building house and car. Thess equipments was concentrated on improving heat efficiency of economic aspects while they design heat exchanger for cooling and heating,. These air conditioning equipments using heat exchanger cause a discomfort to user due to generating mist at the beginning of operating. Therefore the user demand air of high class and quality. In this experimental study to acquire elementary data for development of heat exchanger which be able to supply air of high quality that is to say possess a restraint effect of mist generation. We estimate an effect on cooling plate kind supply air velocity supply air temperature cooled plate temperature and supply air relative humidity which have an influence on outlet air condition of heat exchanger.