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

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

Links

All Publications

  • Developing energy flexibility in clusters of buildings: A critical analysis of barriers from planning to operation ENERGY AND BUILDINGS Le Dreau, J., Lopes, R., 'Connell, S., Finn, D., Hu, M., Queiroz, H., Alexander, D., Satchwell, A., Osterreicher, D., Polly, B., Arteconi, A., Pereira, F., Hall, M., Kirant-Mitic, T., Cai, H., Johra, H., Kazmi, H., Li, R., Liu, A., Nespoli, L., Saeed, M. 2023; 300

    View details for DOI 10.1016/j.enbuild.2023.113608

    View details for Web of Science ID 001101041500001

  • Multi-objective optimal dispatch of household flexible loads based on their real-life operating characteristics and energy-related occupant behavior BUILDING SIMULATION Luo, Z., Peng, J., Hu, M., Liao, W., Fang, Y. 2023; 16 (11): 2005-2025

    View details for DOI 10.1007/s12273-023-1036-y

    View details for Web of Science ID 001091201000001

  • Empirical exploration of zone-by-zone energy flexibility: A non-intrusive load disaggregation approach for commercial buildings ENERGY AND BUILDINGS Hu, M., Rajagopal, R., de Chalendar, J. A. 2023; 296

    View details for DOI 10.1016/j.enbuild.2023.113339

    View details for Web of Science ID 001045931400001

  • Multi-objective optimal dispatch of household flexible loads based on their real-life operating characteristics and energy-related occupant behavior BUILDING SIMULATION Luo, Z., Peng, J., Hu, M., Liao, W., Fang, Y. 2023

    View details for DOI 10.1007/s12273-023-1036

    View details for Web of Science ID 001059861400001

  • 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

https://orcid.org/0000-0001-8042-322X