• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.292-294
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• 2000

This paper is presented the method peak load forecast based on multiple regression Model. Forecasting model was composed with the temperature-humidity and the discomfort index. Also the week periodicity was excluded from weekday change coefficient of two types. Forecasting result was good with about 3[%]. And, utility of presented forecast model using statistical tests has been proved. Therefore, This results establish appropriateness and fitness of forecast models using peak power demand forecasting.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.779-783
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• 2006

A The estimation of electrical power consumption is becoming more important to supply stabilized electrical power recently. In this paper, we propose a supplied forecasting system of electrical power using Fuzzy Compensative Algorithm to estimate electrical load accurately than the previous. We evaluate a time series of supplied electrical power have the chaotic character using quantitative and qualitative analysis, compose a forecasting system by the maximum change $rate(\alpha)$ of Fuzzy Algorithm and compensative parameter. Simulating it for obtained time series, we can obtain more accurate results than the previous proposed system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.533-538
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• 2004

This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models. The proposed system predicts the electrical loads with the lead times of 1 hour, 24 hour, and 168 hour. To do so, the load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. 96 initial structures are constructed for each prediction lead time. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized prediction modell. To improve the performance of the prediction system in terms of accuracy and reliability at the same time, the prediction model employs only two inputs. It makes possible to interpret the fuzzy rules to be learned. In order to demonstrate the viability of the proposed method, we develop a load forecasting system by using the real load data collected during 1996 and 1997 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability

• Journal of the Korean Solar Energy Society
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• v.38 no.1
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• pp.1-11
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• 2018

In this work, we propose weather prediction models to estimate hourly outdoor temperatures and solar irradiance in the next day using forecasting information. Hourly weather data predicted by the proposed models are useful for setting system operating strategies for the next day. The outside temperature prediction model considers 3-hourly temperatures forecasted by Korea Meteorological Administration. Hourly data are obtained by a simple interpolation scheme. The solar irradiance prediction is achieved by constructing a dataset with the observed cloudiness and correspondent solar irradiance during the last two weeks and then by matching the forecasted cloud factor for the next day with the solar irradiance values in the dataset. To verify the usefulness of the weather prediction models in predicting a short-term building load, the predicted data are inputted to a TRNSYS building model, and results are compared with a reference case. Results show that the test case can meet the acceptance error level defined by the ASHRAE guideline showing 8.8% in CVRMSE in spite of some inaccurate predictions for hourly weather data.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.5
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• pp.105-111
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• 2004

This paper shows that active and reactive power load have significant positive relationship and there exist two types of relationship between them using Test Statistics. In investigating the cross plots at every hour, we found out that from 0 to 8 hours, there relationships are linear, while from 9 to 23 hours, they are two piece-wise linear. Also, reactive power loads was estimated and forecasted using active power load as the explanary variable with OLS (Ordinary Least Squares) regression methods. MAPE (Mean Absolute Percentage Error) for each model is calculated for one-hour ahead forecasting.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.177-181
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• 2009

Developing u-Public facilities for application u-City is to combine both the state-of-the art of the construction and ubiquitous computing and must be flexibly comprised of the facilities for the basic service of the building such as air conditioning, heating, lighting and electric equipments to materialize a new format of spatial planning and the public facilities inside or outside. Accordingly, in this paper we suggested the time pattern system for predicting the most basic power system loads for the basic service. To application the tim e pattern we applied SOM algorithm and k-means method and then clustered the data each weekday and each time respectively. The performance evaluation results of suggestion system showed that the forecasting system better the ARIMA model than the exponential smoothing method. It has been assumed that the plan for power supply depending on demand and system operation could be performed efficiently by means of using such power load forecasting.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.323-326
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• 2001

본 논문은 뉴로-퍼지 모델의 구조 학습을 이용하여 단기 전력 수요 예측시스템을 개발하기 위한 체계적인 방법을 제안한다. 제안된 단기 수요 예측시스템은 1시간, 24시간, 168시간의 예측 리드 타임을 갖고 예측을 수행하기 위해서 요일 유형과 시간 별로 총 96개의 초기 구조를 미리 생성하고, 이를 초기 구조 뱅크에 저장한다. 예측이 수행되는 시점에 해당하는 초기 구조를 선택하여 뉴로-퍼지 모델을 초기화하고, 학습하고, 예측을 수행한다. 제안된 예측시스템은 단지 2개의 입력 변수만을 이용하기 때문에 간단한 모델 구조를 가질 뿐 아니라 학습된 퍼지 규칙을 해석하는 것이 매우 용이하다는 장점을 갖는다. 제안된 방법의 실효성을 검증하기 위해 1996년과 1997년의 한국전력의 실제 전력 수요 데이터를 이용하여 1시간, 24시간 168시간 앞의 전력 수요를 예측하는 모의 실험을 수행한다. 실험 결과 제안된 방법은 단지 2개의 입력 변수를 사용함에도 불구하고 기존의 예측 방법과 비교하여 예측의 정확도와 신뢰도 측면에서 우수한 성능을 얻는다.

• Journal of IKEEE
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• v.23 no.3
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• pp.852-857
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• 2019

In order to manage the demand resources of project participants and to provide appropriate strategies in the virtual power plant's power trading platform for consumers or operators who want to participate in the distributed resource collective trading market, it is very important to forecast the next day's demand of individual participants and the overall system's electricity demand. This paper developed a power demand forecasting model for the next day. For the model, we used LSTM algorithm of deep learning technique in consideration of time series characteristics of power demand forecasting data, and new scheme is applied by applying one-hot encoding method to input/output values such as power demand. In the performance evaluation for comparing the general DNN with our LSTM forecasting model, both model showed 4.50 and 1.89 of root mean square error, respectively, and our LSTM model showed high prediction accuracy.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.347-349
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• 2005

This paper presents an efficient algorithm to estimate the maximum load level for heavily loaded power systems with the load-generation variation vector obtained by ELD (Economic Load Dispatch) and/or short term load forecasting while utilizing the elliptic pattern of the P-e curve. It is well known the power flow equation in the rectangular coordinate is fully quadratic. However, the coupling between e and f makes it difficult to take advantage of this quadratic characteristic. In this paper, a simple technique is proposed to reflect the e-f coupling effects on the estimation of maximum loadability with theoretical analysis. An efficient estimation algorithm has been developed with the use of the elliptic properties of the P-e curve. The proposed algorithm is tested on IEEE 14 bus system, New England 39 bus system and IEEE 118 bus system, which shows that the maximum load level can be efficiently estimated with remarkable improvement in accuracy.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.22-30
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• 1999

This paper presents an efficient algorithm to estimate the maximum load level for heavily loaded power systems with the load-generation vector obtained by ELD (Economic Load Dispach) and/or short term load forecasting while utilizing the elliptic pattern of the P-e curve. It is well known the power flow equation in the rectangular corrdinate is jully quadratic. However, the coupling between e and f makes it difficult to take advantage of this quadratic characteristic. In this paper, the elliptic characteristics of P-e curve are illustrated and a simple technique is proposed to reflect the e-f coupling effects on the estimation of maximum loadability with theoretical analysis. An efficient estimation algorithm has been developed with the use of the elliptic properties of the P-e curve. The proposed algorithm is tested on IEEE 14 bus system, New England 39 bus system and IEEE 118 bus system, which shows that the maximum load level can be efficiently estimated with remarkable improvement in accuracy.