• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1304-1310
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• 2015

In remote islands, due to expense of existing generation systems, installation of photovoltaic cells (PVs) and wind turbines has a chance of reducing generation costs. However, in island power systems, even short-term power fluctuations change the frequency of grids because of their small inertia constant. In order to compensate power fluctuations, the authors proposed the power consumption control of pumps which send water to tanks. The power control doesn’t affect water users’ convenience as long as tanks hold water. Based on experimental characteristics of a pump system, this paper shows methods to determine reference power consumption of the system with compensation for short-term PV fluctuations while satisfying water demand. One method uses a PI controller and the other method calculates reference power consumption from water flow reference. Simulations with a PV and a pump system are carried out to find optimum parameters and to compare the methods. Results show that both PI control method and water flow calculation method are useful for satisfying the water demand constraint. The water demand constraint has a little impact to suppression of the short-term power fluctuation in this condition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.621-628
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• 2017

This paper presents an artificial neural network (ANN) based model with a back-propagation algorithm for short-term load forecasting in microgrid power systems. Owing to the significant weather factors for such purpose, relevant input variables were selected in order to improve the forecasting accuracy. As remarked above, forecasting is more complex in a microgrid because of the increased variability of disaggregated load curves. Accurate forecasting in a microgrid will depend on the variables employed and the way they are presented to the ANN. This study also shows numerically that there is a close relationship between forecast errors and the number of training patterns used, and so it is necessary to carefully select the training data to be employed with the system. Finally, this work demonstrates that the concept of load forecasting and the ANN tools employed are also applicable to the microgrid domain with very good results, showing that small errors of Mean Absolute Percentage Error (MAPE) around 3% are achievable.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.10
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• pp.467-474
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• 2005

This paper presents a systematic method to compute a reliability measure for a short term electrical load forecasting system using neuro-fuzzy models. It has been realized that the reliability computation is essential for a load forecasting system to be applied practically. The proposed method employs a local reliability measure in order to exploit the local representation characteristic of the neuro-fuzzy models. It, hence, estimates the reliability of each fuzzy rule learned. The design procedure of the proposed short term load forecasting system is as follows: (1) construct initial structures of neuro-fuzzy models, (2) store them in the initial structure bank, (3) train the neuro-fuzzy model using an appropriate initial structure, and (4) compute load prediction and its reliability. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1996 and 1997 at KEPCO. Simulation results suggest that the proposed scheme extends the applicability of the load forecasting system with the reliably computed reliability measure.

• Fashion & Textile Research Journal
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• v.10 no.6
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• pp.873-881
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• 2008

The purpose of this study is to examine the effects of sales person's perception of ethical management on job performance in department stores. Sales person's perception of ethical management consists of such things as fairness, looking for short-term profits and observing the rules. Job performance consists of such things as sales person's organizational commitment, Sales person's service delivery level, rational operations, and participational attitude. For these purposes, the author developed several hypotheses. The data was collected from 435 sales person's in department stores. The results of this study are as follows: First, fairness, looking for short-term profits, and observing the rules had a significantly positive effect on sales person's organizational commitment. Second, fairness and observing the rules had significantly positive effect on sales person's service delivery level. Third, fairness had a significantly positive effect on rational operation. Fifth, looking for short-term profits and observing the rules had significantly positive effect on participational attitude. At the end of this paper, limitations, further research directions, and implications are suggested.

• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.19-28
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• 2013

Artificial neural networks were used for time series modelling of algal dynamics of whole year and by season at the Paldang dam station (confluence area). The modelling was based on comprehensive weekly water quality data from 1997 to 2004 at the Paldang dam station. The results of validation of seasonal models showed that the timing and magnitude of the observed chlorophyll a concentration was predicted better, compared with the ANN model for whole year. Internal weightings of the inputs in trained neural networks were obtained by sensitivity analysis for identification of the primary driving mechanisms in the system dynamics. pH, COD, TP determined most the dynamics of chlorophyll a, although these inputs were not the real driving variable for algal growth. Short-term prediction models that perform one or two weeks ahead predictions of chlorophyll a concentration were designed for the application of Harmful Algal Alert System in Lake Paldang. Short-term-ahead ANN models showed the possibilities of application of Harmful Algal Alert System after increasing ANN model's performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.8
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• pp.1137-1144
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• 2015

Accurate and robust load forecasting model is very important in power system operation. In case of short-term electric load forecasting, its result is offered as an standard to decide a price of electricity and also can be used shaving peak. For this reason, various models have been developed to improve forecasting accuracy. In order to achieve accurate forecasting result for summer season, this paper proposes a forecasting model using corrected effective temperature based on Heat Index and CDH data as inputs. To do so, we establish polynomial that expressing relationship among CDH, load, temperature. After that, we estimate parameters that is multiplied to each of the terms using PSO algorithm. The forecasting results are compared to Holt-Winters and Artificial Neural Network. Proposing method shows more accurate by 1.018%, 0.269%, 0.132% than comparison groups, respectively.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.8-14
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• 1999

This paper presents the methods of short-term load forecasting Kohonen neural networks and back-propagation neural networks. First, historical load data is divided into 5 patterns for the each seasonal data using Kohonen neural networks and using these results, load forecasting neural network is used for next day hourly load forecasting. Next day hourly load of weekdays and weekend except holidays are forecasted. For load forecasting in summer, max-temperature and min-temperature data as well as historical hourly load date are used as inputs of load forecasting neural networks for a better forecasting accuracy. To show the possibility of the proposed method, it was tested with hourly load data of Korea Electric Power Corporation(1994-95).

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1530-1537
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• 2016

Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.329-338
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• 2021

This study predicts the amount of wind power generation for rational operation plan of wind power generation and capacity calculation of ESS. For forecasting, we present a method of predicting wind power generation by combining a physical approach and a statistical approach. The factors of wind power generation are analyzed and variables are selected. By collecting historical data of the selected variables, the amount of wind power generation is predicted using deep learning. The model used is a hybrid model that combines a bidirectional long short term memory (LSTM) and a convolution neural network (CNN) algorithm. To compare the prediction performance, this model is compared with the model and the error which consist of the MLP(:Multi Layer Perceptron) algorithm, The results is presented to evaluate the prediction performance.

• Journal of Climate Change Research
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• v.7 no.1
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• pp.1-8
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• 2016

In this study, we investigated the applicability of a short-term carbon dioxide ($CO_2$) passive sampler using turbidity change in a solution containing barium hydroxide ($Ba(OH)_2$). The mass of $CO_2$ introduced into the $Ba(OH)_2$ aqueous solution was strongly correlated ($r^2=0.9565$) to the change in turbidity caused by its reaction with the solution. The sampling rates calculated for 1 h and 24 h were $42.4{\pm}5.4mL\;min^{-1}$ and $2.3{\pm}0.3mL\;min^{-1}$, respectively. Both unexposed (blank) and exposed samplers remained stable during the storage period of at least two weeks. The detection limits of the passive sampler for $CO_2$ were 81.5 ppm for 1 h and 61.5 ppm for 24 h. Based on the results, the passive sampler using the change of turbidity in the $Ba(OH)_2$ aqueous solution appears to be a suitable tool for measuring short-term atmospheric concentrations of $CO_2$.