Authors:
Seigo Haruta
1
;
Ken-ichi Tokoro
2
and
Takashi Onoda
1
Affiliations:
1
Aoyama Gakuin University School of Science and Engineering, Kanagawa, Japan
;
2
Central Research Institute of Electric Power Industry, Kanagawa, Japan
Keyword(s):
Support Vector Machine (SVM), Machine Learning, Discrimination Problem, Excess Forecast Alert, Large-Scale Power Consumers, Maximum Power Consumption, Imbalanced Data.
Abstract:
Large-scale power consumers, such as buildings and factories, make high-voltage power contracts with the Japanese electric power companies. The basic fee for high-voltage power contracts is based on the maximum power consumption in the past year. If the power consumption in the present month does not exceed the maximum power consumption in the past year, large-scale power consumers can suppress the basic fee. So, large-scale power consumers need the alert to prevent the maximum power consumption in the present month from exceeding the maximum power consumption in the past year. In this study, excess forecasting was performed considering the characteristics of power consumption in each industry. In addition, we proposed SVM improvements for imbalanced data. We applied this method to power consumption data, which is imbalanced data, to perform excess forecast. As a result, we have improved the accuracy of the excess forecast and contributed to effective alerts to many large-scale power
consumers.
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