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초록
영어
In this paper, the problem of short-term load forecasting is divided into load classification and forecasting. Load classification is needed to obtain meaningful load data as input to train forecasting models. To this end, k-NN and K-mean algorithms are presented. K-mean and k-NN algorithms can handle seasonal load classification and daily load classification, respectively. The classified load data are used to train forecasting models, which are Artificial Neural Networks, Simple Exponential Smoothing, and ARIMA models. As a real case study, we tried to forecast the electric power load of the Republic of Korea. A comparison between the classified and non-classified load forecasts demonstrates the efficiency of the proposed method.
목차
Abstract
1. Introduction
2. Load Classification
2.1. K-mean Clustering
2.2. k-Nearest Neighbor Classification
3. Load Forecasting
3.1. Simple Exponential Smoothing
3.2. Auto Regression Integrated Moving Average Model(ARIMA Model)
3.3. Artificial Neural Network(ANN)
4. Case Study
4.1. Load Classification
4.2. Load Forecasting
5. Conclusion
Acknowledgements
References
1. Introduction
2. Load Classification
2.1. K-mean Clustering
2.2. k-Nearest Neighbor Classification
3. Load Forecasting
3.1. Simple Exponential Smoothing
3.2. Auto Regression Integrated Moving Average Model(ARIMA Model)
3.3. Artificial Neural Network(ANN)
4. Case Study
4.1. Load Classification
4.2. Load Forecasting
5. Conclusion
Acknowledgements
References
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자료제공 : 네이버학술정보