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Maomao Hu
Maomao Hu
Department of Energy Science & Engineering, Stanford University
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model
M Hu, F Xiao, L Wang
Applied energy 207, 324-335, 2017
1012017
Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm
M Hu, F Xiao
Applied energy 219, 151-164, 2018
932018
Price-responsive model predictive control of floor heating systems for demand response using building thermal mass
M Hu, F Xiao, JB Jørgensen, R Li
Applied Thermal Engineering 153, 316-329, 2019
822019
Development of an ANN-based building energy model for information-poor buildings using transfer learning
A Li, F Xiao, C Fan, M Hu
Building simulation 14, 89-101, 2021
562021
Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids
M Hu, F Xiao, S Wang
Renewable and Sustainable Energy Reviews 135, 110248, 2021
502021
Quantifying uncertainty in the aggregate energy flexibility of high-rise residential building clusters considering stochastic occupancy and occupant behavior
M Hu, F Xiao
Energy 194, 116838, 2020
502020
Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids
M Hu, F Xiao, JB Jørgensen, S Wang
Applied Energy 242, 92-106, 2019
502019
A model-based control strategy to recover cooling energy from thermal mass in commercial buildings
K Shan, J Wang, M Hu, D Gao
Energy 172, 958-967, 2019
272019
Investigation of the demand response potentials of residential air conditioners using grey-box room thermal model
M Hu, F Xiao
Energy Procedia 105, 2759-2765, 2017
242017
Probabilistic electrical load forecasting for buildings using Bayesian deep neural networks
L Xu, M Hu, C Fan
Journal of Building Engineering 46, 103853, 2022
162022
Classification and characterization of intra-day load curves of PV and non-PV households using interpretable feature extraction and feature-based clustering
M Hu, D Ge, R Telford, B Stephen, DCH Wallom
Sustainable Cities and Society 75, 103380, 2021
72021
Identification of simplified energy performance models of variable-speed air conditioners using likelihood ratio test method
M Hu, F Xiao, H Cheung
Science and Technology for the Built Environment 26 (1), 75-88, 2020
62020
Performance analysis of absorption thermal energy storage for distributed energy systems
L Wang, F Xiao, B Cui, M Hu, T Lu
Energy Procedia 158, 3152-3157, 2019
32019
Model Predictive Control Of Inverter Air Conditioners Responding to Real-Time Electricity Prices In Smart Grids
M Hu, F Xiao
5th International High Performance Buildings Conference at Purdue University …, 2018
32018
Model-based optimal load control of inverter-driven air conditioners responding to dynamic electricity pricing
M Hu, F Xiao
Energy Procedia 142, 1953-1959, 2017
32017
Stochastic modelling of flexible load characteristics of split-type air conditioners using grey-box modelling and random forest method
Z Jiang, J Peng, R Yin, M Hu, J Cao, B Zou
Energy and Buildings 273, 112370, 2022
12022
Impacts of building load dispersion level on its load forecasting accuracy: Data or algorithms? Importance of reliability and interpretability in machine learning
M Hu, B Stephen, J Browell, S Haben, DCH Wallom
Energy and Buildings 285, 112896, 2023
2023
A novel forecast-based operation strategy for residential PV-battery-flexible loads systems considering the flexibility of battery and loads
Z Luo, J Peng, Y Tan, R Yin, B Zou, M Hu, J Yan
Energy Conversion and Management 278, 116705, 2023
2023
Renewable microgrids covering the heat and electricity needs of industrial parks
E Gueguen, D Wallom, M Hu
European Council for the Energy Efficient Economy, 2022
2022
Model-based optimal control of variable-speed air conditioners in response to dynamic pricing in smart grids
M Hu
Hong Kong Polytechnic University, 2019
2019
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