• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1144-1150
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• 2016

Short-term load forecasting is essential to the electricity pricing and stable power system operations. The conventional weekday 24-hour load forecasting algorithms consider the temperature model to forecast maximum load and minimum load. But 24-hour load pattern forecasting models do not consider temperature effects, because hourly temperature forecasts were not present until the latest date. Recently, 3 hour temperature forecast is announced, therefore hourly temperature forecasts can be produced by mathematical techniques such as various interpolation methods. In this paper, a new 24-hour load pattern forecasting method is proposed by using similar day search considering the hourly temperature. The proposed method searches similar day input data based on the anomalous weather features such as continuous temperature drop or rise, which can enhance 24-hour load pattern forecasting performance, because it uses the past days having similar hourly temperature features as input data. In order to verify the effectiveness of the proposed method, it was applied to the case study. The case study results show high accuracy of 24-hour load pattern forecasting.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.239-241
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• 1999

This paper proposes a novel wavelet transform and Kohonen neural network based technique for short-time load forecasting of power systems. Firstly. Kohonen Self-organizing map(KSOM) is applied to classify the loads and then the Daubechies D2, D4 and D10 wavelet transforms are adopted in order to forecast the short-term loads. The wavelet coefficients associated with certain frequency and time localisation are adjusted using the conventional multiple regression method and then reconstructed in order to forecast the final loads through a four-scale synthesis technique. The outcome of the study clearly indicates that the proposed composite model of Kohonen neural network and wavelet transform approach can be used as an attractive and effective means for short-term load forecasting.

• 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
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• v.43 no.5
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• pp.730-738
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• 1994

The major advantage of the short-term load forecasting technique using general exponential smoothing is high accuracy and operational simplicity, but it makes large forecasting error when the load changes repidly. The paper has presented new technique to improve those shortcomings, and according to forecasted the technique proved to be valid for two years. The structure of load model is time function which consists of daily-and temperature-deviation component. The average of standard percentage erro in daily forecasting for two years was 2.02%, and this forecasting technique has improved standard erro by 0.46%. As relative coefficient for daily and seasonal forecasting is 0.95 or more, this technique proved to be valid.

• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.230-232
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• 2001

This paper presents a model for short-term load forecasting using multiple time-series. We made one-hour ahead load forecasting without classifying load data according to daily load patterns(e.g. weekday. weekend and holiday) To verify its effectiveness. the results are compared with those of neuro-fuzzy forecasting model(5). The results show that the proposed model has more accurate estimate in forecasting.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.77-87
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• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.363-370
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• 2010

Load forecasting has always been essential to the operation and planning of power systems in deregulated electricity markets. Various methods have been proposed for load forecasting, and the neural network is one of the most widely accepted and used techniques. However, to obtain more accurate results, more information is needed as input variables, resulting in huge computational costs in the learning process. In this paper, to reduce training time in multi-layer perceptron-based short-term load forecasting, a graphics processing unit (GPU)-based computing method is introduced. The proposed approach is tested using the Korea electricity market historical demand data set. Results show that GPU-based computing greatly reduces computational costs.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.591-598
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• 2018

Short term load forecasts complexly affected by socioeconomic factors and weather variables have non-linear characteristics. Thus far, researchers have improved load forecast technologies through diverse techniques such as artificial neural networks, fuzzy theories, and statistical methods in order to enhance the accuracy of load forecasts. Short term load forecast errors for special days are relatively much higher than that of weekdays. The errors are mainly caused by the irregularity of social activities and insufficient similar past data required for constructing load forecast models. In this study, the load characteristics of Lunar New Year's Day holidays well known for the highest error occurrence holiday period are analyzed to propose a load forecast technique for Lunar New Year's Day holidays. To solve the insufficient input data problem, the similarity of the load patterns of past Lunar New Year's Day holidays having similar patterns was judged by Euclid distance. Lunar New Year's Day holidays periods for 2011-2012 were forecasted by the proposed method which shows that the proposed algorithm yields better results than the comprehensive analysis method or the knowledge-based method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.8
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• pp.450-456
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• 2003

This paper proposes a multiple time-series model with dummy variables for one-hour ahead load forecasting. We used 11 dummy variables that were classified by day characteristics such as day of the week, holiday, and special holiday. Also, model specification and selection of input variables including dummy variables were made by test statistics such as AIC(Akaike Information Criterion) and t-test statistics of each coefficient. OLS (Ordinary Least Squares) method was used for estimation and forecasting. We found out that model specifications for each hour are not identical usually at 30％ of optimal significance level, and dummy variables reduce the forecasting error if they are classified properly. The proposed model has much more accurate estimates in forecasting with less MAPE (Mean Absolute Percentage Error).

• Journal of IKEEE
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• v.22 no.3
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• pp.672-679
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• 2018

Recently, microgrid (MG) has been proposed as one of the most critical solutions for various energy problems. For the optimal and economic operation of MGs, it is very important to forecast the load profile. However, it is not easy to predict the load accurately since the load in a MG is small and highly variable. In this paper, we propose an artificial neural network (ANN) based method to predict the energy use in campus buildings in short-term time series from one hour up to one week. The proposed method analyzes and extracts the features from the historical data of load and temperature to generate the prediction of future energy consumption in the building based on sparsified K-means. To evaluate the performance of the proposed approach, historical load data in hourly resolution collected from the campus buildings were used. The experimental results show that the proposed approach outperforms the conventional forecasting methods.