• Convergence Security Journal
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• v.22 no.2
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• pp.99-104
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• 2022

This study provided a prediction model for solar energy production in Yeongam province, Jeollanam-do. The model was derived from the correlation between climate changes and solar power production in Yeongam province, Jeollanam-do, and presented a prediction of solar power generation through the regression analysis of 6 parameters related to weather and solar power generation. The data used in this study were the weather and photovoltaic production data from January in 2016 to December in 2019 provided by public data. Based on the data, the machine learning technique was used to analyzed the correlation between weather change and solar energy production and derived to the prediction model. The model showed that the photovoltaic production can be categorized by the three-stage production index and will be used as an important barometer in the agriculture activity and the use of photovoltaic electricity.

• The Journal of Sustainable Design and Educational Environment Research
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• v.11 no.2
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• pp.19-27
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• 2012

This study was carried out in order to establish the estimation equation for school power consumption using regression analysis based on collected power consumption for two years of weather data and schools are located in Central Changwon and Masan district in Changwon city. (1) The power consumption estimation equation for Heating and cooling is calculated using power consumption per unit volume, the difference between actual power consumption and results of estimation equations is 4.1%. (2) The power consumption estimation equation for heating load is showed 2.6% difference compared to actual power consumption in Central Changwon and is expressed 2.9% difference compared to that in Masan district. Therefore, the power consumption prediction for each school using the power consumption estimation equation is possible. (3) The power consumption estimation equation for cooling load is showed 8.0% difference compared to actual power consumption in Central Changwon and is expressed 2.9% compared to that in Masan district. As the power consumption estimation equation for cooling load is expressed difference compared to heating load, it needs to investigate influence for cooling load.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.6 s.99
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• pp.635-644
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• 2005

This paper is. a study on relation between road traffic noise(RTN) and sound power level(PWL). At present, many experimental formulae and prediction formulae are used for prediction of RTN. But these formulae are difficult to appiy to the metropolitan area because these formulae are inaccurate in the different condition from reference condition. This paper calculate RTN and PWL of each prediction formula, choose the best one and make a noise map of the subject area. Procedure is as follows. First, calculate $L_{eq}$ of RTN using experimental formulae and prediction formulae. Second, calculate PWL using $L_{eq}$ of RTN and distance attenuation for point source at semi-free field. Third, choose the most accurate formula. And finally, make a noise map of the subject area at present and future. The result using noise map will be able to apply to application field. Noise mapping tool used on this paper is Raynoise program using Ray Tracing Method(RTM), Mirror Image Source Method(MISM) and Hybrid Method(HM).

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.103-104
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• 2006

Since the first proposition of IEC61850 object model at 1993, many questions about making a seamless model have been issued. the reason which they have worry about is that the functions of the equipment are supposed to be changed properly and new equipment and scheme are need to be introduced according to user's application. To handle those issues, TC57 which is a IEC committee for power control and communication has continuously updated the object model. Nowadays along with the new object model involving power quality, distribution resource and wind power, the committee has a plan to announce the revision of IEC61850-7-4. In the study, authors will present the prediction and diagnosis object models for transformer. Transformer models for protection and control have already been dealt with in the international standard but the models for prediction and diagnosis have never mentioned until now. Designing the prediction and diagnosis functions with the existing IEC61850-7-4, it'll be shown what is a proper object model for prediction and diagnosis.

• Wind and Structures
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• v.9 no.5
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• pp.387-396
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• 2006

This paper presents the wind characteristics of Typhoon Dujuan as measured at a 50 m guyed mast in Hong Kong. The basic wind speed, wind direction and turbulent intensity are studied at two measurement levels of the structure. The power spectral density of the typhoon is compared with the von Karman prediction, and the coherence between wind speeds at the two measurement levels is found to This paper presents the wind characteristics of Typhoon Dujuan as measured at a 50 m guyed mast in Hong Kong. The basic wind speed, wind direction and turbulent intensity are studied at two measurement levels of the structure. The power spectral density of the typhoon is compared with the von Karman prediction, and the coherence between wind speeds at the two measurement levels is found to compare with Davenport's prediction. The effect of typhoon Dujuan on the response of the structure will be discussed in a companion paper (Law, et al. 2006).with Davenport's prediction. The effect of typhoon Dujuan on the response of the structure will be discussed in a companion paper (Law, et al. 2006).

• Computers and Concrete
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• v.16 no.3
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• pp.343-356
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• 2015

1078 sets of mixtures in total that include fly ash, slag, and/or silica fume have been collected for prediction on concrete properties. A new database platform (Compos) has been developed, by which the stepwise multiple linear regression (SMLR) and BP artificial neural networks (BP ANNs) programs have been applied respectively to identify correlations between the concrete properties (strength, workability, and durability) and the dosage and/or quality of raw materials'. The results showed obvious nonlinear relations so that forecasting by using nonlinear method has clearly higher accuracy than using linear method. The forecasting accuracy rises along with the increasing of age and the prediction on cubic compressive strength have the best results, because the minimum average relative error (MARE) for 60-day cubic compressive strength was less than 8%. The precision for forecasting of concrete workability takes the second place in which the MARE is less than 15%. Forecasting on concrete durability has the lowest accuracy as its MARE has even reached 30%. These conclusions have been certified in a ready-mixed concrete plant that the synthesized MARE of 7-day/28-day strength and initial slump is less than 8%. The parameters of BP ANNs and its conformation have been discussed as well in this study.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.82-91
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• 2022

In this paper, time series prediction method of photovoltaic power is introduced using seasonal autoregressive integrated moving average (SARIMA). In order to obtain the best fitting model by a time series method in the absence of an environmental sensor, this research was used data below 50% of cloud cover. Three samples were extracted by time intervals from the raw data. After that, the best fitting models were derived from mean absolute percentage error (MAPE) with the minimum akaike information criterion (AIC) or beysian information criterion (BIC). They are SARIMA (1,0,0)(0,2,2)14, SARIMA (1,0,0)(0,2,2)28, SARIMA (2,0,3)(1,2,2)55. Generally parameter of model derived from BIC was lower than AIC. SARIMA (2,0,3)(1,2,2)55, unlike other models, was drawn by AIC. And the performance of models obtained by SARIMA was compared. MAPE value was affected by the seasonal period of the sample. It is estimated that long seasonal period samples include atmosphere irregularity. Consequently using 1 hour or 30 minutes interval sample is able to be helpful for prediction accuracy improvement.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1119-1126
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• 2002

In this paper, we propose two predictive spatial data mining based on spatial statistics and apply for predicting the forest fire hazardous area. These are conditional probability and likelihood ratio methods. In these approaches, the prediction models and estimation procedures are depending un the basic quantitative relationships of spatial data sets relevant forest fire with respect to selected the past forest fire ignition areas. To make forest fire hazardous area prediction map using the two proposed methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. In comparison of the prediction power of the two proposed prediction model, the likelihood ratio method is mort powerful than conditional probability method. The proposed model for prediction of forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrence and effective placement of forest fire monitoring equipment and manpower.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.3086-3101
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• 2021

To supply precise marketing and differentiated service for the electric power service department, it is very important to predict the customers with high sensitivity of electric power failure. To solve this problem, we propose a novel grouped 𝑙_1/2 sparsity constrained logistic regression method for sensitivity assessment of electric power failure. Different from the 𝑙₁ norm and k-support norm, the proposed grouped 𝑙_1/2 sparsity constrained logistic regression method simultaneously imposes the inter-class information and tighter approximation to the nonconvex 𝑙₀ sparsity to exploit multiple correlated attributions for prediction. Firstly, the attributes or factors for predicting the customer sensitivity of power failure are selected from customer sheets, such as customer information, electric consuming information, electrical bill, 95598 work sheet, power failure events, etc. Secondly, all these samples with attributes are clustered into several categories, and samples in the same category are assumed to be sharing similar properties. Then, 𝑙_1/2 norm constrained logistic regression model is built to predict the customer's sensitivity of power failure. Alternating direction of multipliers (ADMM) algorithm is finally employed to solve the problem by splitting it into several sub-problems effectively. Experimental results on power electrical dataset with about one million customer data from a province validate that the proposed method has a good prediction accuracy.

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.971-972
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• 2005