• 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.

• Asia pacific journal of information systems
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• v.7 no.1
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• pp.67-83
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• 1997

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.9
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• pp.399-399
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• 2001

This paper proposes TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. In the scatter diagram of daily peak load versus daily high or low temperature, we can find out that the load-temperature relationship has a negative slope in the lower regime and a positive slope in the upper regime due to the heating and cooling load, respectively. TAR model is adequate for analyzing these phenomena since TAR model is a piecewise linear autoregressive model. In this paper, we estimated and forecasted one day-ahead daily peak load by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.9
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• pp.309-405
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• 2001

This paper proposes TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. In the scatter diagram of daily peak load versus daily high or low temperature, we can find out that the load-temperature relationship has a negative slope in the lower regime and a positive slope in the upper regime due to the heating and cooling load, respectively. TAR model is adequate for analyzing these phenomena since TAR model is a piecewise linear autoregressive model. In this paper, we estimated and forecasted one day-ahead daily peak load by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.3
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• pp.107-117
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• 2000

This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models. The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed 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. 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 model. 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 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptrons, radial basis function networks, and neuro-fuzzy models without the structure learning.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.131-131
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• 2021

Deep learning models, especially those based on long short-term memory (LSTM), have presented their superiority in addressing time series data issues recently. This study aims to comprehensively evaluate the performance of deep learning models that belong to the supervised learning category in streamflow prediction. Therefore, six deep learning models-standard LSTM, standard gated recurrent unit (GRU), stacked LSTM, bidirectional LSTM (BiLSTM), feed-forward neural network (FFNN), and convolutional neural network (CNN) models-were of interest in this study. The Red River system, one of the largest river basins in Vietnam, was adopted as a case study. In addition, deep learning models were designed to forecast flowrate for one- and two-day ahead at Son Tay hydrological station on the Red River using a series of observed flowrate data at seven hydrological stations on three major river branches of the Red River system-Thao River, Da River, and Lo River-as the input data for training, validation, and testing. The comparison results have indicated that the four LSTM-based models exhibit significantly better performance and maintain stability than the FFNN and CNN models. Moreover, LSTM-based models may reach impressive predictions even in the presence of upstream reservoirs and dams. In the case of the stacked LSTM and BiLSTM models, the complexity of these models is not accompanied by performance improvement because their respective performance is not higher than the two standard models (LSTM and GRU). As a result, we realized that in the context of hydrological forecasting problems, simple architectural models such as LSTM and GRU (with one hidden layer) are sufficient to produce highly reliable forecasts while minimizing computation time because of the sequential data nature.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.12
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• pp.2202-2211
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• 2010

The wind power prediction system is composed of a meteorological forecasting module, calculation module of wind power output and HMI(Human Machine Interface) visualization system. The final information from this system is a short-term (6hr ahead) and mid-term (48hr ahead) wind power prediction value. The meteorological forecasting module for wind speed and direction forecasting is a combination of physical and statistical model. In this system, the WRF(Weather Research and Forecasting) model, which is a three-dimensional numerical weather model, is used as the physical model and the GFS(Global Forecasting System) models is used for initial condition forecasting. The 100m resolution terrain data is used to improve the accuracy of this system. In addition, optimization of the physical model carried out using historic weather data in Jeju. The mid-term prediction value from the physical model is used in the statistical method for a short-term prediction. The final power prediction is calculated using an optimal adjustment between the currently observed data and data predicted from the power curve model. The final wind power prediction value is provided to customs using a HMI visualization system. The aim of this study is to further improve the accuracy of this prediction system and develop a practical system for power system operation and the energy market in the Smart-Grid.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1373-1383
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• 2016

The wind energy industry and wind power generation have increased; consequently, the stable supply of the wind power has become an important issue. It is important to accurately predict the wind power with short-term basis in order to make a reliable planning for the power supply and demand of wind power. In this paper, we first analyzed the speed, power and the directions of the wind. The neural network and the time series models (ARMA, ARMAX, ARMA-GARCH, Holt Winters) for wind power generation forecasting were compared based on mean absolute error (MAE). For one to three hour-ahead forecast, ARMA-GARCH model was outperformed, and the neural network method showed a better performance in the six hour-ahead forecast.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.