• New & Renewable Energy
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• v.2 no.2 s.6
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• pp.37-43
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• 2006

In this paper, the first forecasting system of wind power generation, KIER Forecaster is presented. KIER Forecaster has been constructed based on statistical models and was trained with wind speed data observed at Gosan Weather Station nearby Walryong Site. Due to short period of measurements at Walryong Site for training the model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict(MCP) technique. The results of One to Three-hour advanced forecasting models are consistent with the measurement at Walryong site. In particular, the multiple regression model by classification of wind speed pattern, which has been developed in this work, shows the best performance comparing with neural network and auto-regressive models.

• Current Photovoltaic Research
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• v.12 no.3
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• pp.49-54
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• 2024

This study proposed a forecasting model that combines ANNs and RNNs to address the intermittency and fluidity of solar power generation. Four prediction models were trained separately based on sky conditions provided by the Korea Meteorological Administration, and insolation was estimated using the ASHRAE Clear-Sky model. The proposed model showed an error rate of 6.5-7.7% based on NMAE, which meets the requirements of power generation prediction. As a result, this study can improve the accuracy of solar power generation forecasting, which can contribute to the stability of power operation and the profitability of power operators.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.8
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• pp.380-389
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• 2000

This paper presents a method for the regional long-term load forecasting in metropolitan area considering econimic indicator with the assumption that energy demands propoprtionally increases under the economic indicators. For the accurate load forecasting, it is very important to scrutinize the correlation among the regional electric power demands, economic indicator and other characteristics because load forecasting results may vary depending on many different factors such as electric power demands, gross products, social trend and so on. Three steps for the regional long-term load forecasting are microscopically and macroscopically used for the regional long -term load forecasting in order to increase the accuracy and practicality of the results.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1419-1424
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• 2018

Very short-term electric load forecasting is essential for real-time power system operation. In this paper, a very short-term electric load forecasting technique applying the Kalman filter algorithm is proposed. In order to apply the Kalman filter algorithm to electric load forecasting, an electrical load forecasting algorithm is defined as an observation model and a state space model in a time domain. In addition, in order to precisely reflect the noise characteristics of the Kalman filter algorithm, the optimal error covariance matrixes Q and R are selected from several experiments. The proposed algorithm is expected to contribute to stable real-time power system operation by providing a precise electric load forecasting result in the next six hours.

• Economic and Environmental Geology
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• v.23 no.3
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• pp.323-328
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• 1990

This study explores that learning model that has been employed for many years in the description of and projection of system or process performance promises to be very useful in long-term forecasting, especially of technology or related productivity measures, in mineral and energy industries. This study also provides some empirical results on the measurement of the learning curve in U. S. petroleum resource assessment and demonstrates how the learning system can be used as an economic forecasting tool.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.247-253
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• 2011

A sustainability of electricity supply has emerged as a critical issue for low carbon green growth in South Korea. Wind power is the fastest growing source of renewable energy. However, due to its own intermittency and volatility, the power supply generated from wind energy has variability in nature. Hence, accurate forecasting of wind speed and power plays a key role in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. This paper presents a short-term wind speed prediction method based on support vector regression. Moreover, particle swarm optimization is adopted to find an optimum setting of hyper-parameters in support vector regression. An illustration is given by real-world data and the effect of model regularization by particle swarm optimization is discussed as well.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.929-931
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• 1998

This paper describes the peak load forecasting technique of distribution transformers with correlation equation. While customers are demanding safe energy supply, conventional correlation equation that is used for load management of distribution transformers in domestic has some problems. To get accurate correlation equation, se-correlation equation were examined using new collected using the measuring instrument dev for this study. It was recognized that the qua equation was the most accurate for peak forecasting from working electrical energy.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.292-294
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• 1998

It is very important for electric utility to expand generating facilities and transmission equipments in accordance with the increase of electricity demand. Regional electricity demand forecasting is among the most important step for long-term investment and power supply planning. The main objectives of this paper are to develop the methodologies for forecasting regional load demand. The Model consists of four models, regional economy, regional electricity energy demand, areal electricity energy demand. and areal peak load demand. This paper mainly suggests regional electricity energy demand model and areal peak load demand. A case study is also presented.

• Journal of the Korean Solar Energy Society
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• v.39 no.4
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• pp.41-54
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• 2019

The proportion of solar photovoltaic power generation has steadily increased in the power trade market. Solar energy forecast is highly important for the stable trade of volatile solar energy in the existing power trade market, and it is necessary to identify accurately any forecast error according to the forecast lead time. This paper analyzes the latest study trend in solar energy forecast overseas and presents a consistent comparative assessment by adopting a single statistical variable (nRMSE) for forecast errors according to lead time and forecast technology.

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.1063-1070
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• 2017

This paper presents a method for forecasting future uranium prices that is used as input data to calculate the uranium cost, which is a rational key cost driver of the nuclear fuel cycle cost. In other words, the statistical autoregressive integrated moving average (ARIMA) model and existing engineering cost estimation method, the so-called escalation rate model, were subjected to a comparative analysis. When the uranium price was forecasted in 2015, the margin of error of the ARIMA model forecasting was calculated and found to be 5.4%, whereas the escalation rate model was found to have a margin of error of 7.32%. Thus, it was verified that the ARIMA model is more suitable than the escalation rate model at decreasing uncertainty in nuclear fuel cycle cost calculation.