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

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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Probabilistic electrical load forecasting for buildings using Bayesian deep neural networks JOURNAL OF BUILDING ENGINEERING Xu, L., Hu, M., Fan, C. 2022; 46
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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