Maomao Hu

发表

Identification of simplified energy performance models of variable-speed air conditioners using likelihood ratio test method

F. Xiao, H. Cheung, Maomao Hu, 2020, Science and Technology for the Built Environment.

Classification and characterization of intra-day load curves of PV and non-PV households using interpretable feature extraction and feature-based clustering

Bruce Stephen, Rory Telford, Maomao Hu, 2021, Sustainable Cities and Society.

Development of an ANN-based building energy model for information-poor buildings using transfer learning

Ao Li, Fu Xiao, Cheng Fan, 2020, Building Simulation.

Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids

Fu Xiao, Shengwei Wang, Maomao Hu, 2021 .

Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids

Fu Xiao, Shengwei Wang, John Bagterp Jørgensen, 2019, Applied Energy.

Price-responsive model predictive control of floor heating systems for demand response using building thermal mass

John Bagterp Jørgensen, Fu Xiao, Rongling Li, 2019, Applied Thermal Engineering.

Probabilistic electrical load forecasting for buildings using Bayesian deep neural networks

Maomao Hu, Cheng-Yu Fan, Lei Xu, 2022, Journal of Building Engineering.

Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model

Fu Xiao, Maomao Hu, Lingshi Wang, 2017 .

A model-based control strategy to recover cooling energy from thermal mass in commercial buildings

Kui Shan, Dian-ce Gao, Jiayuan Wang, 2019, Energy.

Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm

Fu Xiao, Maomao Hu, F. Xiao, 2018, Applied Energy.

Quantifying uncertainty in the aggregate energy flexibility of high-rise residential building clusters considering stochastic occupancy and occupant behavior

Fu Xiao, Maomao Hu, F. Xiao, 2020 .

Impacts of building load dispersion level on its load forecasting accuracy: Data or algorithms? Importance of reliability and interpretability in machine learning

D. Wallom, B. Stephen, S. Haben, 2023, Energy and Buildings.

A novel forecast-based operation strategy for residential PV-battery-flexible loads systems considering the flexibility of battery and loads

Jinqing Peng, Rongxin Yin, Yutong Tan, 2023, Energy Conversion and Management.