Maomao Hu 
- (2023): Developing energy flexibility in clusters of buildings: A critical analysis of barriers from planning to operation. In: Energy and Buildings, v. 300 (Dezember 2023).
- (2023): Multi-objective optimal dispatch of household flexible loads based on their real-life operating characteristics and energy-related occupant behavior. In: Building Simulation, v. 16, n. 11 (September 2023).
- (2023): Empirical exploration of zone-by-zone energy flexibility: A non-intrusive load disaggregation approach for commercial buildings. In: Energy and Buildings, v. 296 (Oktober 2023).
- (2023): Impacts of building load dispersion level on its load forecasting accuracy: Data or algorithms? Importance of reliability and interpretability in machine learning. In: Energy and Buildings, v. 285 (April 2023).
- (2022): Stochastic modelling of flexible load characteristics of split-type air conditioners using grey-box modelling and random forest method. In: Energy and Buildings, v. 273 (Oktober 2022).
- (2022): Probabilistic electrical load forecasting for buildings using Bayesian deep neural networks. In: Journal of Building Engineering, v. 46 (April 2022).
- (2021): Classification and characterization of intra-day load curves of PV and non-PV households using interpretable feature extraction and feature-based clustering. In: Sustainable Cities and Society, v. 75 (Dezember 2021).
- (2020): Development of an ANN-based building energy model for information-poor buildings using transfer learning. In: Building Simulation, v. 14, n. 1 (November 2020).
- (2020): Identification of simplified energy performance models of variable-speed air conditioners using likelihood ratio test method. In: Science and Technology for the Built Environment, v. 26, n. 1 (2 Januar 2020).
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