A Supervised Machine Learning Approach to Control Energy Storage Devices

Henri, Gonzague; Lu, Ning

doi:10.1109/tsg.2019.2892586

A Supervised Machine Learning Approach to Control Energy Storage Devices

Journal Article · Thu Mar 07 23:00:00 EST 2019 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/tsg.2019.2892586· OSTI ID:2329478

^[1]; Lu, Ning ^[2]

North Carolina State University, Raleigh, NC (United States); North Carolina State University
North Carolina State University, Raleigh, NC (United States)

This paper introduces a supervised machine learning approach to predict and schedule the real-time operation mode of the next operation interval for residential PV/Battery systems controlled by mode-based controllers. The performance of the mode-based economic model-predictive control (EMPC) approach is used as the benchmark. The residential load and PV data used in the paper are 1-minute data downloaded from the the Pecan Street Project website. The optimal operation mode for each control interval is first derived from the historical data used as the training set. Then, four machine learning algorithms (i.e. neural network, support vector machine, logistic regression, and random forest algorithms) are applied. We compared the performance of the four algorithms when using different number of features and length of the training sets extracted from different months of the year. Simulation results show that using the machine learning approach can effectively improve the performance of the mode-based control system and reduce the computation effort of local controllers because the training can be completed on a cloud-based Machine Learning engine. Furthermore, the work presented in this paper paves the way for using a shared-learning platform to design controllers of residential PV/storage systems. This may significantly reduce the cost for implementing such systems.

View Accepted Manuscript (DOE)

Research Organization:: North Carolina State University, Raleigh, NC (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: EE0008770

OSTI ID:: 2329478

Journal Information:: IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 6 Vol. 10; ISSN 1949-3053

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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25 ENERGY STORAGE
Discrete control
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Mode-based scheduling

A Supervised Machine Learning Approach to Control Energy Storage Devices

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