Maomao Hu

Postdoctoral Scholar, Energy Resources Engineering

Web page: http://www.maomaohu.net

Bio

Maomao Hu is a postdoctoral researcher in the Department of Energy Science and Engineering at Stanford University since November 2021. Prior to joining Stanford, he was a postdoc in the Department of Engineering Science at the University of Oxford for two years. He received his PhD degree in Building Environment and Energy Engineering from the Hong Kong Polytechnic University in 2019. In 2018, he studied as a guest PhD student in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark.

His research interests include data analytics, data-driven modelling, numerical optimization, and model predictive control of the building and urban energy systems for GHG emission reduction, energy efficiency, energy flexibility, and energy resiliency. He has 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).

Honors & Awards

Best Student Paper Award (1st place), 5th International High Performance Buildings Conference, Purdue University, IN, USA (July 2018)

Professional Education

Ph.D., The Hong Kong Polytechnic University, Building Environment and Energy Engineering (2019)

Stanford Advisors

Adam Brandt, Postdoctoral Faculty Sponsor

Contact

Academic
maomaohu@stanford.edu
University - Scholar Department: Department of Energy Science and Engineering Position: Postdoctoral Scholar
- Room 355, Green Earth Sciences Building
- 367 Panama St.
- Stanford, California 94305

Additional Info

Mail Code: 2220
ORCID:
https://orcid.org/0000-0001-8042-322X

All Publications

Impacts of building load dispersion level on its load forecasting accuracy: Data or algorithms? Importance of reliability and interpretability in machine learning ENERGY AND BUILDINGS Hu, M., Stephen, B., Browell, J., Haben, S., Wallom, D. H. 2023; 285

View details for DOI 10.1016/j.enbuild.2023.112896

View details for Web of Science ID 000991857500001
A novel forecast-based operation strategy for residential PV-battery-flexible loads systems considering the flexibility of battery and loads ENERGY CONVERSION AND MANAGEMENT Luo, Z., Peng, J., Tan, Y., Yin, R., Zou, B., Hu, M., Yan, J. 2023; 278

View details for DOI 10.1016/j.enconman.2023.116705

View details for Web of Science ID 000927473800001
Stochastic modelling of flexible load characteristics of split-type air conditioners using grey-box modelling and random forest method ENERGY AND BUILDINGS Jiang, Z., Peng, J., Yin, R., Hu, M., Cao, J., Zou, B. 2022; 273

View details for DOI 10.1016/j.enbuild.2022.112370

View details for Web of Science ID 000858955500008
Probabilistic electrical load forecasting for buildings using Bayesian deep neural networks JOURNAL OF BUILDING ENGINEERING Xu, L., Hu, M., Fan, C. 2022; 46

View details for DOI 10.1016/j.jobe.2021.103853

View details for Web of Science ID 000772623100002
Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids RENEWABLE & SUSTAINABLE ENERGY REVIEWS Hu, M., Xiao, F., Wang, S. 2021; 135

View details for DOI 10.1016/j.rser.2020.110248

View details for Web of Science ID 000592379200001
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 Hu, M., Ge, D., Telford, R., Stephen, B., Wallom, D. C. 2021; 75

View details for DOI 10.1016/j.scs.2021.103380
Development of an ANN-based building energy model for information-poor buildings using transfer learning BUILDING SIMULATION Li, A., Xiao, F., Fan, C., Hu, M. 2021; 14 (1): 89-101

View details for DOI 10.1007/s12273-020-0711-5

View details for Web of Science ID 000568472300002
Quantifying uncertainty in the aggregate energy flexibility of high-rise residential building clusters considering stochastic occupancy and occupant behavior ENERGY Hu, M., Xiao, F. 2020; 194

View details for DOI 10.1016/j.energy.2019.116838

View details for Web of Science ID 000519654200069
Identification of simplified energy performance models of variable-speed air conditioners using likelihood ratio test method SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT Hu, M., Xiao, F., Cheung, H. 2020; 26 (1): 75-88

View details for DOI 10.1080/23744731.2019.1665446

View details for Web of Science ID 000505886600008
Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids APPLIED ENERGY Hu, M., Xiao, F., Jorgensen, J., Wang, S. 2019; 242: 92-106

View details for DOI 10.1016/j.apenergy.2019.03.127

View details for Web of Science ID 000470045800008
Price-responsive model predictive control of floor heating systems for demand response using building thermal mass APPLIED THERMAL ENGINEERING Hu, M., Xiao, F., Jorgensen, J., Li, R. 2019; 153: 316-329

View details for DOI 10.1016/j.applthermaleng.2019.02.107

View details for Web of Science ID 000470194400033
A model-based control strategy to recover cooling energy from thermal mass in commercial buildings ENERGY Shan, K., Wang, J., Hu, M., Gao, D. 2019; 172: 958-967

View details for DOI 10.1016/j.energy.2019.02.045

View details for Web of Science ID 000464488100078
Performance analysis of absorption thermal energy storage for distributed energy systems Wang, L., Xiao, F., Cui, B., Hu, M., Lu, T., Yan, J., Yang, H. X., Li, H., Chen ELSEVIER SCIENCE BV. 2019: 3152-3157

View details for DOI 10.1016/j.egypro.2019.01.1017

View details for Web of Science ID 000471031703080
Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm APPLIED ENERGY Hu, M., Xiao, F. 2018; 219: 151-164

View details for DOI 10.1016/j.apenergy.2018.03.036

View details for Web of Science ID 000430519200013
Model-based optimal load control of inverter-driven air conditioners responding to dynamic electricity pricing Hu, M., Xiao, F., Yan, J., Wu, J., Li, H. ELSEVIER SCIENCE BV. 2017: 1953-1959

View details for DOI 10.1016/j.egypro.2017.12.395

View details for Web of Science ID 000452901602018
Investigation of the Demand Response Potentials of Residential Air Conditioners Using Grey-box Room Thermal Model Hu, M., Xiao, F., Yan, J., Sun, F., Chou, S. K., Desideri, U., Li, H., Campana, P., Xiong, R. ELSEVIER SCIENCE BV. 2017: 2759-2765

View details for DOI 10.1016/j.egypro.2017.03.594

View details for Web of Science ID 000404967902134
Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model Applied Energy Hu, M., Xiao, F., Wang, L. 2017; 207: 324-335

View details for DOI 10.1016/j.apenergy.2017.05.099