About
18
Publications
1,522
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491
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Citations since 2017
Introduction
I am a Postdoctoral Researcher in the Department of Energy Science and Engineering at Stanford University. My research interests include data analytics, data-driven modelling, numerical optimisation, and model predictive control of the building and urban energy systems for GHG reduction, energy efficiency, energy flexibility, and energy resiliency.
For more info, visit: https://maomaohu.net/
Additional affiliations
March 2020 - November 2021
University of Oxford
Position
- Postdoctoral Fellow
Education
July 2015 - December 2018
Publications
Publications (18)
The rapid developments of advanced metering infrastructure and dynamic electricity pricing provide great opportunities for residential electrical appliances, especially air conditioners (ACs), to participate in demand response (DR) programs to reduce peak power consumptions and electricity bills. One of the biggest challenges faced by residential D...
Floor heating (FH) system is a widely-used thermally active building system, which can take advantage of building thermal mass to shift energy demands to off-peak hours. Its ability of effective utilization of low-temperature energy resources also helps to increase energy efficiency and reduce greenhouse gas emissions. However, control of FH system...
In the context of smart grids, residential air conditioners, as the major contributors to home electricity bills and loads on electrical grids, need to be not only energy-efficient, but also grid-responsive to relieve power supply-demand imbalance. Existing demand response control strategies for residential air conditioners focus on single-speed ty...
Modern buildings are expected to be not only energy efficient but also energy flexible to facilitate reliable integration of intermittent renewable energy sources into smart grids. Estimating the aggregate energy-flexibility potential at a cluster level plays a key role in assessing financial benefits and service area for energy-flexibility service...
The management of demand-side flexibility plays a key role in reliable integration of intermittent renewable energy sources into residential microgrids. Residential microgrid is a dynamic and complex cyber-physical system, which consists of multiple cooperative, non-cooperative and even conflicting entities. Random and separate demand-side manageme...
Split-type air conditioners (ACs) are the major contributors to the total electricity use and peak power demand in residential buildings due to their high penetration. Accurate prediction of their energy consumption plays a significant role in demand side management (DSM), because it can help exploit the demand response (DR) potential of these ACs...
Load pattern categorization plays a significant role in enhancing the understanding of demand characteristics of different cohorts of energy customers on a distribution network. For a distribution network with embedded photovoltaic (PV) systems, it is also desirable to enumerate these premises since unreported PV installations can have a detrimenta...
Deep learning techniques are increasingly applied in building electrical load analysis thanks to the enrichment of information-intensive sensory data. However, uncertainty is seldom considered among existing deep learning models. This study proposes three Bayesian deep neural network models for probabilistic building electrical load forecasting. Th...
Accurate building energy prediction is vital to develop optimal control strategies to enhance building energy efficiency and energy flexibility. In recent years, the data-driven approach based on machine learning algorithms has been widely adopted for building energy prediction due to the availability of massive data in building automation systems...
Variable-speed air conditions (ACs) are gradually replacing single-speed ACs in residential buildings in densely populated cities like Hong Kong due to better control accuracy and higher energy efficiency at part-load conditions. Simplified energy performance models of variable-speed ACs are needed for different purposes, including building energy...
Building structures and furniture have the capability of storing thermal energy and therefore could be called building thermal mass. In commercial buildings, room conditions remain in thermal comfort zone for a while after the end of office hours when the air-conditioning systems are tuned off. It is possible to recover the stored cooling energy fr...
In recent years, distributed energy systems (DES) have attracted worldwide attention. Distributed generation unit (DG) in DES usually works under part load during night, which results in low efficiency and the waste heat of DG cannot be fully utilized. This study proposes a novel absorption thermal energy storage system together with electric energ...
Dynamic electricity pricing provides a great opportunity for residential consumers to participate into demand response (DR) programs to reduce the electricity bills. The lack of automatic response to time-varying electricity prices is one of the challenges faced by the residential electric appliances. Most of the existing studies on DR of residenti...
Over the last few years, the development of information and communication technologies has provided a great opportunity for the residential sector to take part in demand response (DR) programs in smart grids (SGs). Optimal load scheduling via home energy management systems (HEMSs) is a typical technique used to reduce the power consumptions during...
This paper investigates demand response (DR) potentials of residential air conditioners (AC) under different control strategies using a grey-box room thermal model. The proposed resistance-capacitance (RC) thermal model combines the essential prior knowledge of room thermal characteristics with data-driven techniques. With the aim of saving the opt...
Projects
Project (1)
AMIDiNe aims to devise analytics that link point measurement to whole system to address the increasingly problematic management of electrical load on distribution networks as the UK transitions to a low carbon energy system. Through unification of power system modelling and machine learning, AMIDiNe will develop the underlying models that will leverage Smart Meter and enhanced low voltage network monitoring data can predict plausible operating characteristics such as voltage throughout distribution networks that are in alignment with future operating scenarios.
