Maomao Hu

Postdoctoral Researcher

Stanford University

Hi, I am a Postdoctoral Scholar in the Department of Energy Resources Engineering at Stanford University since November 2021. Prior to joining Stanford, I worked with Prof. David Wallom as a Postdoc in the Energy and Environmental Informatics Group in the Department of Engineering Science at the University of Oxford since March 2020. I received my PhD degree in Building Environment and Energy Engineering from The Hong Kong Polytechnic University in 2019 (Supervisor: Prof. Linda Xiao). In 2018, I studied as a guest PhD student with Prof. John Bagterp Jørgensen in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark.

My research interests include data analytics, data-driven modelling, numerical optimization, and model predictive control of the building and urban energy systems for GHG reduction, energy efficiency, energy flexibility, and energy resiliency. I have been actively contributing to international collaborations, including the ongoing IEA EBC Annex 81 (Data-Driven Smart Buildings) and Annex 82 (Energy Flexible Buildings Towards Resilient Low Carbon Energy Systems).

Research Methods

Creating sustainable data-centric technologies for building and urban energy systems at the interface of Building Environment and Energy Engineering, Computer Science, and Control Engineering.

Data Analytics

Data Cleaning | Data Visualization | Data Clustering | EDA

Data-driven Modelling

Grey-/Black-box | Machine Learning | Deep Learning

Physics-based Modelling

Building Thermal Dynamics | Refrigeration/HVAC systems | ODEs and State Space Representations

Computational Optimisation

Mathmatical and Metaheuristic (GA/PSO) Algorithms

Model Predictive Control (MPC)

Moving from predictive to prescriptive analytics | MPC = Data-driven Modeling + Receding-horizon Numerical Optimization + Online Feedback Control

Uncertainty Analysis

Monte Carlo technique |Stochastic Occupancy and Occupant Behavior (Markov Chain)

MPC of Building Energy Systems

For energy efficiency/energy flexibility

How MPC works?

Receding Horizon Optimisation: Opt.→Decision→Measurement→Opt.→Decision→Measurement→…

Stochastic Data-driven Occupancy Model

Using Heterogeneous Markov-chain Monte Carlo method

Data-driven Resistance-Capacitance Building Thermal Model

Simple and ready for control/large-scale purpose

News & Updates

Jan 2022 | Our new paper entitled "Probabilistic electrical load forecasting for buildings using Bayesian deep neural networks" was published in *Journal of Building Engineering*!

Nov 2021 | New journey in Dept. of Energy Resources Engineering at Stanford University!

Oct 2021 | Our new paper entitled "Classification and characterization of intra-day load curves of PV and non-PV households using interpretable feature extraction and feature-based clustering" was published in *Sustainable Cities and Society*!

Aug 2021 | A talk was given at the Sino-UK Joint Symposium on Post Covid-19 Pandemic - A Reconsideration for the Built Environment.

Aug 2021 | A talk was given to the School of Environmental Science and Engineering, Huangzhong University of Science and Technology.

Selected Projects

COOLER at Stanford

To enable low-carbon, electrified community energy systems using Stanford campus as a real-life testbed.

AMiDine in the UK

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.

Quantifying Uncertainty in Aggregate Energy Flexibility of Building Clusters

To quantify the uncertainty in the aggregate energy flexibility of residential building clusters using a data-driven stochastic occupancy model that can capture the stochasticity of occupancy patterns.

MPC of Variable-speed ACs for Demand Response in Smart Grids

To directly control the operating frequency of ACs in response to high-granularity electricity price signals, i.e., 5-minute real-time electricity prices,in smart grids using MPC method.

MPC of Floor Heating Systems in Denmark

To develop model predictive control for floor heating systems to provide energy flexibiltiy. The proposed MPC can simultaneously consider all the influential variables including weather conditions, occupancy and dynamic electricity prices.

Recent Publications

Quickly filtering publications here.

Lei Xu, Maomao Hu, Cheng Fan (2022). Probabilistic electrical load forecasting for buildings using Bayesian deep neural networks. Journal of Building Engineering.

Maomao Hu, Dongjiao Ge, Rory Telford, Bruce Stephen, David C.H. Wallom (2021). Classification and characterization of intra-day load curves of PV and non-PV households using interpretable feature extraction and feature-based clustering. Sustainable Cities and Society.

Ao Li, Fu Xiao, Cheng Fan, Maomao Hu (2021). Development of an ANN-based building energy model for information-poor buildings using transfer learning. Building Simulation.

Maomao Hu, Fu Xiao, Shengwei Wang (2021). Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids. Renewable and Sustainable Energy Reviews.

Maomao Hu, Fu Xiao, Howard Cheung (2020). Identification of simplified energy performance models of variable-speed air conditioners using likelihood ratio test method. Science and Technology for the Built Environment.

See all publications

Gallery

In my spare time, I am into photography, especially when exporing cities.