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Machine learning for smart building energy management: A model predictive control approach

Research output: Thesis › Master's Thesis

Published

Standard

Machine learning for smart building energy management: A model predictive control approach. / Li, Carmen.
Lancaster University, 2020. 80 p.

Research output: Thesis › Master's Thesis

Harvard

Li, C 2020, 'Machine learning for smart building energy management: A model predictive control approach', Masters by Research, Lancaster University. https://doi.org/10.17635/lancaster/thesis/1207

APA

Li, C. (2020). Machine learning for smart building energy management: A model predictive control approach. [Master's Thesis, Lancaster University]. Lancaster University. https://doi.org/10.17635/lancaster/thesis/1207

Vancouver

Li C. Machine learning for smart building energy management: A model predictive control approach. Lancaster University, 2020. 80 p. doi: 10.17635/lancaster/thesis/1207

Author

Li, Carmen. / Machine learning for smart building energy management : A model predictive control approach. Lancaster University, 2020. 80 p.

Bibtex

@mastersthesis{2e38bef822f94861bae919b5551342c3,
title = "Machine learning for smart building energy management: A model predictive control approach",
abstract = "We examine the various machine learning methods to be adopted by Q-PLUS, a data-driven smart building energy management system under development by Qbots Energy. Q-PLUS aims to help commercial buildings to save on electricity cost, as well as to generate extra income via the provision of ancillary services, by shifting their electricity demand from the grid away from the peak hours. It is a battery storage, heating, ventilation and air-conditioning (HVAC) control system based on model predictive control (MPC), where machine learning tools are used to predict i), the half-hourly electricity demand of the building and ii), the response of the indoor environment to the HVAC controls. In this thesis, we test and compare different machine learning algorithms to find the most suitable set of tools for the development of Q-PLUS. We also design a battery control algorithm under the MPC framework and fully develop it in Python.",
author = "Carmen Li",
year = "2020",
doi = "10.17635/lancaster/thesis/1207",
language = "English",
publisher = "Lancaster University",
school = "Lancaster University",

}

RIS

TY - THES

T1 - Machine learning for smart building energy management

T2 - A model predictive control approach

AU - Li, Carmen

PY - 2020

Y1 - 2020

N2 - We examine the various machine learning methods to be adopted by Q-PLUS, a data-driven smart building energy management system under development by Qbots Energy. Q-PLUS aims to help commercial buildings to save on electricity cost, as well as to generate extra income via the provision of ancillary services, by shifting their electricity demand from the grid away from the peak hours. It is a battery storage, heating, ventilation and air-conditioning (HVAC) control system based on model predictive control (MPC), where machine learning tools are used to predict i), the half-hourly electricity demand of the building and ii), the response of the indoor environment to the HVAC controls. In this thesis, we test and compare different machine learning algorithms to find the most suitable set of tools for the development of Q-PLUS. We also design a battery control algorithm under the MPC framework and fully develop it in Python.

AB - We examine the various machine learning methods to be adopted by Q-PLUS, a data-driven smart building energy management system under development by Qbots Energy. Q-PLUS aims to help commercial buildings to save on electricity cost, as well as to generate extra income via the provision of ancillary services, by shifting their electricity demand from the grid away from the peak hours. It is a battery storage, heating, ventilation and air-conditioning (HVAC) control system based on model predictive control (MPC), where machine learning tools are used to predict i), the half-hourly electricity demand of the building and ii), the response of the indoor environment to the HVAC controls. In this thesis, we test and compare different machine learning algorithms to find the most suitable set of tools for the development of Q-PLUS. We also design a battery control algorithm under the MPC framework and fully develop it in Python.

U2 - 10.17635/lancaster/thesis/1207

DO - 10.17635/lancaster/thesis/1207

M3 - Master's Thesis

PB - Lancaster University

ER -