• Journal of Korean Academy of Nursing
• /
• v.7 no.1
• /
• pp.55-62
• /
• 1977

Varieties of literatures were reviewed in regard to the fundamental concept of day hospital, historical trends, the recipient of its care, facilities and personnel, therapeutic programmes and the follow-up care plans. Through the research the advantages of day hospital were highlighted in order to provide the reference for those who consider planning such health care institution. Since the introduction of the concept of day hospital and its implementation in 1930, many psychiatric patients world over are treated and cared. Patients with specific health problems ; alcoholism, acute or serious psychiatric disease, tendencies of humidor suicidal attempts, and with serious physical problems were excluded from the general recipient. Day hospital were annexed to the psychiatric hospitals in most in instances ; facilities, personnel, except nursing personnel, were shared. All therapeutic care were planned in daley, weekly programmes, and were focussed on socialization. The follow-up care were provided for those participating post- therapy club activities which were planned and introduced ahead. Many advantages of day-hospital care in contrast to the traditional hospitalization care were found: 1. The abrupt discontinuity of his family and other social role is prevented. 2. Therapeutic progress is faster. 3. Lessened economic burden to the family. 4. Behavioral regression is lessened and the lessened fear of hospitalization. 5. Less injury to the patients, self- respect, through lessened anxiety of hospitalization. 6. Incidents of secondary crisis believed to be existing in long term cases are decreased. 7. Therapeutic care implemented in freer atmosphere, better Patient-personnel relationships are created. 8. Varieties of group activities are Induced which enable faster recovery. 9. Patients could engage himself with social activities including getting job on part-time basis. 10. Rehabilitation of patient could be implemented.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.13 no.1
• /
• pp.39-49
• /
• 2013

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

• Advances in environmental research
• /
• v.4 no.2
• /
• pp.83-104
• /
• 2015

Air Quality Index (AQI) is a pointer to broadcast short term air quality. This paper presents one day ahead AQI forecasting on seasonal basis for three major cities in Maharashtra State, India by using Artificial Neural Networks (ANN) and Genetic Programming (GP). The meteorological observations & previous AQI from 2005-2008 are used to predict next day's AQI. It was observed that GP captures the phenomenon better than ANN and could also follow the peak values better than ANN. The overall performance of GP seems better as compared to ANN. Stochastic nature of the input parameters and the possibility of auto-correlation might have introduced time lag and subsequent errors in predictions. Spectral Analysis (SA) was used for characterization of the error introduced. Correlational dependency (serial dependency) was calculated for all 24 models prepared on seasonal basis. Particular lags (k) in all the models were removed by differencing the series, that is converting each i'th element of the series into its difference from the (i-k)"th element. New time series is generated for all seasonal models in synchronization with the original time line & evaluated using ANN and GP. The statistical analysis and comparison of GP and ANN models has been done. We have proposed a promising approach of use of GP coupled with SA for real time prediction of seasonal multicity AQI.

• Journal of Communications and Networks
• /
• v.17 no.6
• /
• pp.656-664
• /
• 2015

This study analyzes the performance of power-line communications for sending open automated demand response (OpenADR) signals. In particular, we study main channel disturbances that can affect end-to-end communications and which have not been previously studied in detail. Our analysis takes into account physical phenomena, such as background and impulsive noise sources, channel attenuation, and multipath effects, and considers the physical, network, and applications layers of the communications structure. The performance of the physical layer is the basis for computing the packet error rate. In analyzing application performance, we focus specifically on the latency in several communication environments. If a channel is impaired only by background noise, latencies are less than 40 seconds. With the addition of impulsive noise in the channel, this value increases as long as 68 seconds. Using these figures, we find that power-line technology is more suitable for "slow" demand programs, such as day-ahead or day-of curtailments, rather than ancillary services markets, which require near-real-time communication.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.68 no.3
• /
• pp.405-415
• /
• 2019

The considerations of the electrical device' characteristics and the customers' satisfaction have been important criteria for efficient smart grid systems. In general, an electrical device is classified into a non-interruptible device or an interruptible device. The consideration of the type is an essential information for the efficient smart grid scheduling. In addition, customers' scheduling preferences or satisfactions have to be considered simultaneously. However, the existing research studies failed to consider both criteria. This paper proposes a new and efficient smart grid scheduling framework considering both criteria. The framework consists of two modules - 1) A day-head smart grid scheduling algorithm and 2) Real-time sudden demand negotiation framework. The first method generates the smart grid schedule efficiently using an embedded genetic algorithm with the consideration of the device's characteristics. Then, in case of sudden electrical demands, the second method generates the more efficient real-time smart grid schedules considering both criteria. In order to show the effectiveness of the proposed framework, comparisons with the existing relevant research studies are provided under various electricity demand scenarios.

• Journal of the Korean Solar Energy Society
• /
• v.39 no.2
• /
• pp.71-80
• /
• 2019

Daily ahead forecast is necessary for the electricity balance between load and supply due to the variability renewable energy. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for more than 12 hours forecast horizon. UM-LDAPS model is the numerical weather prediction operated by Korea Meteorological Administration and it generates the 36 hours forecast of hourly total irradiance 4 times a day. This study attempts to evaluate the model performance against the in situ measurements at 37 ground stations from January to May, 2013. Relative mean bias error, mean absolute error and root mean square error of hourly total irradiance are averaged over all ground stations as being 8.2%, 21.2% and 29.6%, respectively. The behavior of mean bias error appears to be different; positively largest in Chupoongnyeong station but negatively largest in Daegu station. The distinct contrast might be attributed to the limitation of microphysics parameterization for thick and thin clouds in the model.

• Water Engineering Research
• /
• v.1 no.2
• /
• pp.129-136
• /
• 2000

Predicting water levels is a difficult task because a lot of uncertainties are included. Therefore the neural network which is appropriate to such a problem, is introduced. One day ahead forecasting of river stage in the Bocheong River is carried out by using the neural network model. Historical water levels at Snagye gauging point which is located at the downstream of the Bocheong River and average rainfall of the Bocheong River basin are selected as training data sets. With these data sets, the training process has been done by using back propagation algorithm. Then waters levels in 1997 and 1998 are predicted with the trained algorithm. To improve the accuracy, a filtering method is introduced as predicting scheme. It is shown that predicted results are in a good agreement with observed water levels and that a filtering method can overcome the lack of training patterns.

• ETRI Journal
• /
• v.37 no.2
• /
• pp.273-282
• /
• 2015

Vehicle-to-grid presents a mechanism to meet the key requirements of an electric power system, using electric vehicles (EVs) when they are parked. The most economic ancillary service is that of frequency regulation, which imposes some constraints regarding the period and duration of time the vehicles have to be connected to the grid. The majority of research explores the profitability of the aggregator, while the perspective of the EV fleet owner, in terms of their need for usage of their fleet, remains neglected. In this paper, the optimal allocation of available vehicles on a day-ahead basis using queuing theory and fuzzy multi-criteria methodology has been determined. The proposed methodology is illustrated on the daily scheduling of EVs in an electricity distribution company.

• ETRI Journal
• /
• v.37 no.1
• /
• pp.197-202
• /
• 2015

A smart grid is a modernized electrical grid that uses information about the behaviors of suppliers and consumers in an automated fashion to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity. In the operation of a smart grid, demand side management (DSM) plays an important role in allowing customers to make informed decisions regarding their energy consumption. In addition, it helps energy providers reduce peak load demand and reshapes the load profile. In this paper, we propose a new DSM scheduling scheme that makes use of the day-ahead pricing strategy. Based on the Rubinstein-Stahl bargaining model, our pricing strategy allows consumers to make informed decisions regarding their power consumption, while reducing the peak-to-average ratio. With a simulation study, it is demonstrated that the proposed scheme can increase the sustainability of a smart grid and reduce overall operational costs.

• Journal of Electrical Engineering and Technology
• /
• v.7 no.5
• /
• pp.681-688
• /
• 2012

This paper presents the application of Differential Evolution (DE) algorithm to obtain a solution for Bid Based Dynamic Economic Dispatch (BBDED) problem including the transmission losses and to maximize the social profit in a deregulated power system. The IEEE-30 bus test system with six generators, two customers and two trading periods are considered under various bidding strategies in a day-ahead electricity market. By matching the bids received from supplying and distributing entities, the Independent System Operator (ISO) maximize the social profit, (with the choices available). The simulation results of DE are compared with the results of Particle swarm optimization (PSO). The results demonstrate the potential of DE algorithm and show its effectiveness to solve BBDED.