• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.115-123
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• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

• International Journal of Air-Conditioning and Refrigeration
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• v.10 no.2
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• pp.65-71
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• 2002

The objective of this study is to evaluate the design parameters and to develop the cooling and heating load prediction equations of office buildings. The building load calculation simulation was carried out using the DOE-2.1E program. The results of the simulation were used as data for multiple regression analysis which could develop the load prediction equations.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.9
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• pp.914-921
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• 2001

The objective of this study is to evaluate the design parameters and to develop the cooling and heating load prediction equations of office buildings. The building load calculation simulation was carried out using the DOE-2.1E program. The results of the simulation was used as a data for ANOVA and multiple regression analysis which could develop the load prediction equations.

• Smart Structures and Systems
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• v.26 no.6
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• pp.781-796
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• 2020

Prediction of total sediment load is essential in an extensive range of problems such as the design of the dead volume of dams, design of stable channels, sediment transport in the rivers, calculation of bridge piers degradation, prediction of sand and gravel mining effects on river-bed equilibrium, determination of the environmental impacts and dredging necessities. This paper is aimed to investigate and predict the total sediment load of the Wadi Arbaat in Eastern Sudan. The study was estimated the sediment load by separate total sediment load into bedload and Suspended Load (SL), independently. Although the sediment records are not sufficient to construct the discharge-sediment yield relationship and Sediment Rating Curve (SRC), the total sediment loads were predicted based on the discharge and Suspended Sediment Concentration (SSC). The turbidity data NTU in water quality has been used for prediction of the SSC in the estimation of suspended Sediment Yield (SY) transport of Wadi Arbaat. The sediment curves can be used for the estimation of the suspended SYs from the watershed area. The amount of information available for Khor Arbaat case study on sediment is poor data. However, the total sediment load is essential for the optimal control of the sediment transport on Khor Arbaat sediment and the protection of the dams on the upper gate area. The results show that the proposed model is found to be considered adequate to predict the total sediment load.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.6
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• pp.441-446
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• 2000

This paper presents an approach for the prediction of optimal heating load using a diagonal recurrent neural networks(DRNN) and data base system of outdoor temperature. In the DRNN, a dynamic backpropagation(DBP) with delta-bar-delta teaming method is used to train an optimal heating load identifier. And the data base system is utilized for outdoor temperature prediction. Compared to other kinds of methods, the proposed method gives better prediction performance of heating load. Also a hardware for the controller is developed using a microprocessor. The experimental results show that prediction enhancement for heating load can be achieved with the proposed method regardless of the its inherent nonlinearity and large time constant.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.171-177
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• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.666-671
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• 1993

Fatigue life prediction under multi-axial variable load were performed for Aluminium 7075-T651 alloy using SAE Notched specimen & Torque tube shaft component specimen. When variable multiaxial load is applied to material, maximum damaged plane(critical plane) change. To clarify the situation, experiment is performed on two different changing load path. For multiaxial fatigue life prediction, miner rule is expanded to critical plane theory. Shear based parameter and Elliptical parameter give better correlation. This suggests that miner rule can be applicable on multi-axial variable load.

• Steel and Composite Structures
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• v.36 no.5
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• pp.493-506
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• 2020

The load-slip relationship of the shear connection is an important parameter in design and analysis of composite structures. In this paper, a load-slip curve prediction method of the shear connection based on the artificial neural networks (ANNs) is proposed. The factors which are significantly related to the structural and deformation performance of the connection are selected, and the shear stiffness of shear connections and the transverse coordinate slip value of the load-slip curve are taken as the input parameters of the network. Load values corresponding to the slip values are used as the output parameter. A twolayer hidden layer network with 15 nodes and 10 nodes is designed. The test data of two different forms of shear connections, the stud shear connection and the perforated shear connection with flange heads, are collected from the previous literatures, and the data of six specimens are selected as the two prediction data sets, while the data of other specimens are used to train the neural networks. Two trained networks are used to predict the load-slip curves of their corresponding prediction data sets, and the ratio method is used to study the proximity between the prediction loads and the test loads. Results show that the load-slip curves predicted by the networks agree well with the test curves.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.5
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• pp.193-198
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• 1986

In this paper, 1-168 hours ahead load prediction algorithm is developed for power system economic weekly operation. Total load is composed of three components, which are base load, week load and weather-sensitive load. Base load and week load are predicted by moving average and exponential smoothing method, respectively. The days of moving average and smoothing constant are optimally determined. Weather-sensitive load is modeled by linear form. The paramiters of weather load model are estimated by exponentially weighted recursive least square method. The load prediction of special day is very tedious, difficult and remains many problems which should be improved. Test results are given for the day of different types using the actual load data of KEPCO.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.730-735
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• 2006

This paper proposes a time series prediction method for the short term electrical load will) the fuzzy system and the genetic algorithm. At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction system may be obtained. We obtain good results for the time prediction of the short term electric load by the proposed algorithm. In addition we implement the graphic user interface for the proposed algorithms. Finally, we implement the regional prediction system for the electric load.