• 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.

• Environmental and Resource Economics Review
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• v.16 no.4
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• pp.803-832
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• 2007

Electricity and heat load profiles by use types on an hourly basis at the least are essential for assessing economic viability of new cogeneration and CES projects and for optimally operating existing cogeneration and CES facilities. We adopt a multilevel model to specify heat load profiles so as to utilize in a flexible manner the panel nature of our data on weather and heating/cooling use. Converting the multilevel model to the linear mixed-effects model, we estimate the model by panel FGLS. The estimated load profile model for each distinct use type accounts for the effects of temperature, humidity, each hour over the year, each day of the week, each type of legal holidays, and heating/cooling area on energy use. To save space, we feature in detail the heating profile of the household.

• Journal of the Korea Convergence Society
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• v.6 no.6
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• pp.241-246
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• 2015

In cold winter season, the apparatuses of heating and heater which warm up the interior of a room are necessary element and the used amount of these apparatuses from the year 2000 has been increased abruptly. But, the fire accident and the danger of fire are also increased. Therefore, 3D modelling is done by referring three kinds of the electric heaters as the heaters of ceramic, carbon and near infrared ray sold in the city for the design of more safe heating apparatuses in this study. The thermal analyses with these models are carried out and the durabilities due to the thermal deformation and stress are studied. By the background of the study results derived in this study ultimately, the durabilities of electric heater models due to each shape can be anticipated and contributed to the development of new heating apparatus with more safe resistance to fire. And it is possible to be grafted onto the convergence technique at design and show the esthetic sense.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.6
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• pp.283-292
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• 2015

A model of apartment heating load in a district heating system could be useful in the management and utilization of energy resources, since it could predict energy usage and so could assist in the efficient use of energy resources. The heating load in a district heating system varies in a highly nonlinear manner and is subject to many different factors, such as heating area, number of people living in that complex, and ambient temperature. Thus there are few published papers with accurate models of heating load, especially in domestic literature. This work is concerned with the modeling of apartment heating load in a district heating system in winter, using the reduced least square support vector machine (LS-SVM), and with the purpose of using the model to predict heating energy usage in domestic city area. We collected 23,856 pieces of data on heating energy usage over a 12-week period in winter, from 12 heat exchangers in five apartments. Half of the collected data were used to construct the heating load model, and the other half were used to test the model's accuracy. The model was able to predict the heating energy usage pattern rather accurately. It could also estimate the usage of heating energy within of mean absolute percentage error. This implies that the model prediction accuracy needs to be improved further, but it still could be considered as an acceptable model if we consider the nonlinearity and uncertainty of apartment heating energy usage in a district heating system.