A Machine Learning Approach for Real-time Battery Optimal Operation Mode Prediction and Control

被引：0
|
作者：
Henri, Gonzague [1 ,2 ]
Lu, Ning [2 ]
Carrejo, Carlos [1 ]
机构：
[1] Total SA, Raleigh, NC 27695 USA
[2] North Carolina State Univ, Raleigh, NC 27695 USA
来源：
2018 IEEE/PES TRANSMISSION AND DISTRIBUTION CONFERENCE AND EXPOSITION (T&D) | 2018年
关键词：
Machine learning; mode based control; residential energy storage; PV system; neural network; OPTIMIZATION;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
This paper introduces a machine learning approach for real-time battery optimal operation mode prediction in residential PV applications. First, from the historical data, the optimal battery operation mode for each operation interval is derived. Then, a best performing algorithm for the prediction of the optimal modes is obtained. Performances are tested with different number of features in the training test and different training lengths. Then, the features will be used to predict future operation mode in real-time operations. A comparison on bill savings is made with the model-predictive control approach using the residential load and PV data from the Pecan Street project website under the Hawaiian electricity rate. Simulation results show a 9 points improvement in performance.
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页数：5
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