• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.718-723
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• 2016

The temperature changes are very important in improving the accuracy of the load forecasting during the summer. It is because the cooling load in summer contribute to the increasing of the load. This paper proposes a weekend load forecasting algorithm using the temperature change characteristic in a summer of Jeju. The days before and after weekends in Jeju, when the load curves are quite different from those of normal weekdays. The temperature change characteristic are obtained by using weekends peak load and high temperature data. And load forecasted based on the sensitivity between unit temperature changes and load variations. Load forecast data with better accuracy are obtained by using the proposed temperature changes than by using the ordinary daily peak load forecasting. The method can be used to reduce the error rate of load forecast.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1144-1150
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• 2016

Short-term load forecasting is essential to the electricity pricing and stable power system operations. The conventional weekday 24-hour load forecasting algorithms consider the temperature model to forecast maximum load and minimum load. But 24-hour load pattern forecasting models do not consider temperature effects, because hourly temperature forecasts were not present until the latest date. Recently, 3 hour temperature forecast is announced, therefore hourly temperature forecasts can be produced by mathematical techniques such as various interpolation methods. In this paper, a new 24-hour load pattern forecasting method is proposed by using similar day search considering the hourly temperature. The proposed method searches similar day input data based on the anomalous weather features such as continuous temperature drop or rise, which can enhance 24-hour load pattern forecasting performance, because it uses the past days having similar hourly temperature features as input data. In order to verify the effectiveness of the proposed method, it was applied to the case study. The case study results show high accuracy of 24-hour load pattern forecasting.

• Journal of Industrial Technology
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• v.15
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• pp.187-193
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• 1995

In this paper, a short-term load forecasting expert system considering the load variation due to the change in temperature is presented. The change in temperature is an important load variation factor that varies the normal load pattern. The conventional load forecasting methods by artificial neural networks have used the technique where the temperature variables were included in the input neurons of artificial neural networks. However, simply adding the input units of temperature data may make the forecasting accuracy worse, since the accuracy of the load forecasting in this method depends on the accuracy of weather forecasting. In this paper, the fuzzy expert system that modifies the forecasted load using fuzzy rules representing the relations of load and temperature is presented and compared with a conventional load forecasting technique. In the test case of 1991, the proposed model provided a more accurate forecast than the conventional technique.

• Tribology and Lubricants
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• v.8 no.1
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• pp.48-55
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• 1992

The friction characteristic of plastics (PTFE, Nylon, Acetal and phenolic) was studied on the lubricated condition with a pin on disk machine. Mineral oil without additive (base oil) and water were used as liquid lubricants at the controlled temperature. From the experimental work, it was found out that the coefficient of friction of plastics was controlled by the mechanical properities of plastic more than that of liquid for various load and temperature. Viscosity of liquid has affected on the friction only at low temperature under lighb load. Among the tested plastics, the coefficient of friction of PTFE was the lowest under light load and at low temperature while Nylon at medium load and temperature, and Acetal at heavy load and high temperature. The coefficient of friction of soft plastics like PTFE and Nylon were increased as the load and temperature were increased, while that of hard plastic (Acetal) was decreased and that of thermo setting plastic (phenolic) was mixed. Also for soft plastics, the coefficient of friction under heavy load was always higher than that under light load, while hard plastic was vice versa.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.590-591
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• 2011

This paper proposes a short-term load forecasting by using a temperature and load pattern. The forecasting model that represents the relations between load and temperature which get a numeral expected temperature based on the past temperature was constructed. Case studies were applied to load forecasting for 2009 data, and the results show its appropriate accuracy.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.9
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• pp.399-399
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• 2001

This paper proposes TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. In the scatter diagram of daily peak load versus daily high or low temperature, we can find out that the load-temperature relationship has a negative slope in the lower regime and a positive slope in the upper regime due to the heating and cooling load, respectively. TAR model is adequate for analyzing these phenomena since TAR model is a piecewise linear autoregressive model. In this paper, we estimated and forecasted one day-ahead daily peak load by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.9
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• pp.309-405
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• 2001

This paper proposes TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. In the scatter diagram of daily peak load versus daily high or low temperature, we can find out that the load-temperature relationship has a negative slope in the lower regime and a positive slope in the upper regime due to the heating and cooling load, respectively. TAR model is adequate for analyzing these phenomena since TAR model is a piecewise linear autoregressive model. In this paper, we estimated and forecasted one day-ahead daily peak load by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.194.2-194.2
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• 2011

Heating and cooling performance has been analyzed for the water-source heat pump system using raw water from Daechung reservoir. During heating operation from March to May, water temperature is not good condition for a heat source due to the higher atmospheric temperature. Avearged heating load ratio is only 14.3%, and the averaged unit COP and system COP are estimated to be 2.46 and 2.15 respectively. The COP is affected considerably by the water temperature, and the unit COP is increased from 2.16 at $5^{\circ}C$ to 2.95 at $11^{\circ}C$. Cooling performance is analyzed with the measured data from June to August. During cooling operation, raw water has lower temperature by 4. $5^{\circ}C{\sim}4.7^{\circ}C$ than the atmosphere. The load ratio is 39.2%, and the averaged unit COP and system COP are estimated to be 7.25 and 6.13 respectively. The heating COP is affected by the load ratio rather than water temperature. The COP is increased for 20%~40% load ratio, while is decreased for 40%~60% load ratio. It is estimated that the compressor operation combination for 3 (two constant speed and one inverter) compressors is changed for the load ratio.

• International Journal of Highway Engineering
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• v.3 no.3 s.9
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• pp.147-158
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• 2001

One of the most important factors accounting for the structural capacity of concrete pavement is load transfer efficiency. Load transfer efficiency is affected by slab temperature gradient, construction of dowel bars, degree of aging, and crack width. The purpose of this study is to determine the patterns of load transfer efficiency of concrete pavement; to determine the factors that affect the load transfer efficiency; and to present the proper measuring method of load transfer efficiency. As a result from this study, load transfer efficiency was affected primarily by the average temperature in concrete slab. Load transfer efficiency decreased with decreasing temperature and increasing crack width. For the sections with dowel bars, there were little differences in load transfer efficiency regardless of temperature changes. For the sections without dowel bars, however, there on great losses of load transfer efficiency at low temperatures. For the old pavement, even in the sections with dowel bars, the load transfer efficiency reduced as the temperature dropped For the sections in this study, 1.4% reduction of the load transfer efficiency was observed for each $1^{\circ}C$ drop in the slab temperature.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.184-186
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• 2000

This paper proposes the introduction of TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. TAR model is a piecewise linear autoregressive model. In the scatter diagram of daily peak load versus daily maximum or minimum temperature, we can find out that the load-temperature relationship has a negative slope in lower regime and a positive slope in upper regime due to the heating and cooling load, respectively. In this paper, daily peak load was forecasted by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.