Energy Consumption Load Forecasting Using a Level-Based Random Forest Classifier

被引:15
|
作者
Chen, Yu-Tung [1 ]
Piedad, Eduardo, Jr. [2 ]
Kuo, Cheng-Chien [1 ]
机构
[1] Natl Taiwan Univ Sci & Technol, Dept Elect Engn, Taipei 10607, Taiwan
[2] Univ San Jose Recoletos, Dept Elect Engn, Cebu 6000, Philippines
来源
SYMMETRY-BASEL | 2019年 / 11卷 / 08期
关键词
energy level consumption; pattern recognition; random forest; machine learning; load forecasting; level classification; RESIDENTIAL BUILDINGS; WAVELET TRANSFORM; PREDICTION; MODELS; PERFORMANCE; REGRESSION; APPLIANCES; DEMAND;
D O I
10.3390/sym11080956
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Energy consumers may not know whether their next-hour forecasted load is either high or low based on the actual value predicted from their historical data. A conventional method of level prediction with a pattern recognition approach was performed by first predicting the actual numerical values using typical pattern-based regression models, hen classifying them into pattern levels (e.g., low, average, and high). A proposed prediction with pattern recognition scheme was developed to directly predict the desired levels using simpler classifier models without undergoing regression. The proposed pattern recognition classifier was compared to its regression method using a similar algorithm applied to a real-world energy dataset. A random forest (RF) algorithm which outperformed other widely used machine learning (ML) techniques in previous research was used in both methods. Both schemes used similar parameters for training and testing simulations. After 10-time cross training validation and five averaged repeated runs with random permutation per data splitting, the proposed classifier shows better computation speed and higher classification accuracy than the conventional method. However, when the number of its desired levels increases, its prediction accuracy seems to decrease and approaches the accuracy of the conventional method. The developed energy level prediction, which is computationally inexpensive and has a good classification performance, can serve as an alternative forecasting scheme.
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页数:9
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