Liang Zhang

Interests

Research

Dr. Zhang's research is broadly focused in the area of computing and modeling for smart buildings and cities to promote building energy efficiency, sustainability, and decarbonization. More specifically, his research has emphasized the following topics: 1) grid-interactive efficient building, 2) machine learning control in buildings, 3) fault detection and diagnostics in buildings, 4) smart and connected communities, 5) building energy modeling (building, community, and urban scale)

Teaching

Building Energy Modeling

Courses

Scholarly Contributions

Books

• Zhang, L. (2018). Data-driven whole building energy forecasting model for data predictive control. Drexel University.

Journals/Publications

• Zhang, L., & Chen, Z. (2024). Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems. arXiv preprint arXiv:2402.09584.
• Zhang, L., Chen, Z., & Ford, V. (2024). Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies. arXiv preprint arXiv:2402.09579.
• Zhang, L., Haroon, S. M., & Ryan, A. (2024). Py-Cosim: Python-Based Building Energy Co-Simulation Infrastructure. SSRN.
• Zhang, L., & Chen, Z. (2023). Opportunities and Challenges of Applying Large Language Models in Building Energy Efficiency and Decarbonization Studies: An Exploratory Overview. arXiv preprint arXiv:2312.11701.
• Zhang, L., & Wen, J. (2017). A Systematic Feature Selection Procedure for Data-driven Building Energy Forecasting Model Development. ASHRAE Annual Conference.
• Zhang, L., Chen, J., & Zou, J. (2023). Taxonomy, Semantic Data Schema, and Schema Alignment for Open Data in Urban Building Energy Modeling. arXiv preprint arXiv:2311.08535.
• Zhang, L., Leach, M., Chen, J., & Hu, Y. (2023). Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings. Energy, 263, 125577.
• Chen, J., Zhang, L., Li, Y., Shi, Y., Gao, X., & Hu, Y. (2022). A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems. Renewable and Sustainable Energy Reviews, 161, 112395.
• Zhang, L., & Leach, M. (2022). Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation. Building Simulation, 15(5).
• Zhang, L., Chen, Z., Zhang, X., Pertzborn, A., & Jin, X. (2023). Challenges and Opportunities of Machine Learning Control in Building Operations. Building SImulation, TBD.
• Bae, Y., Bhattacharya, S., Cui, B., Lee, S., Li, Y., Zhang, L., Im, P., Adetola, V., Vrabie, D., Leach, M., & others, . (2021). Sensor impacts on building and HVAC controls: A critical review for building energy performance. Advances in Applied Energy, 4, 100068.
• Li, Y., O'Neill, Z., Zhang, L., Chen, J., Im, P., & DeGraw, J. (2021). Grey-box modeling and application for building energy simulations-A critical review. Renewable and Sustainable Energy Reviews, 146, 111174.
• Zhang, L. (2021). Data-driven building energy modeling with feature selection and active learning for data predictive control. Energy and Buildings, 252, 111436.
• Zhang, L., & Wen, J. (2021). Active learning strategy for high fidelity short-term data-driven building energy forecasting. Energy and Buildings, 244, 111026.
• Zhang, L., Alahmad, M., & Wen, J. (2021). Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study. Energy and Buildings, 231, 110592.
• Zhang, L., Leach, M., Bae, Y., Cui, B., Bhattacharya, S., Lee, S., Im, P., Adetola, V., Vrabie, D., & Kuruganti, T. (2021). Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review. Advances in Applied Energy, 3, 100055.
• Zhang, L., Plathottam, S., Reyna, J., Merket, N., Sayers, K., Yang, X., Reynolds, M., Parker, A., Wilson, E., Fontanini, A., & others, . (2021). High-resolution hourly surrogate modeling framework for physics-based large-scale building stock modeling. Sustainable Cities and Society, 75, 103292.
• Zhang, L., Wen, J., Li, Y., Chen, J., Ye, Y., Fu, Y., & Livingood, W. (2021). A review of machine learning in building load prediction. Applied Energy, 285, 116452.
• Bianchi, C., Zhang, L., Goldwasser, D., Parker, A., & Horsey, H. (2020). Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules. Applied Energy, 276, 115470.
• Im, P., Bae, Y., Cui, B., Lee, S., Bhattacharya, S., Adetola, V., Vrabie, D., Zhang, L., & Leach, M. (2020). Literature Review for Sensor Impact Evaluation and Verification Use Cases-Building Controls and Fault Detection and Diagnosis (FDD).
• Zhang, L. (2020). A pattern-recognition-based ensemble data imputation framework for sensors from building energy systems. Sensors, 20(20), 5947.
• Zhang, L., Frank, S., Kim, J., Jin, X., & Leach, M. (2020). A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings. Building and Environment, 186, 107338.
• Zhang, L., & Wen, J. (2019). A systematic feature selection procedure for short-term data-driven building energy forecasting model development. Energy and Buildings, 183, 428--442.
• Zhang, L., & Wen, J. (2018). Apply Active Learning in Short-term Data-driven Building Energy Modeling.
• Zhang, L., Wen, J., Cui, C., Li, X., & Wu, T. (2016). Experiment Design and Training Data Quality of Inverse Model for Short-term Building Energy Forecasting.
• Zhang, L., Xu, P., Mao, J., Tang, X. u., Li, Z., & Shi, J. (2015). A low cost seasonal solar soil heat storage system for greenhouse heating: Design and pilot study. Applied Energy, 156, 213--222.

Proceedings Publications

• Sharma, A., Li, X., Guan, H., Sun, G., Zhang, L., Wang, L., Wu, K., Cao, L., Zhu, E., Sim, A., & others, . (2023). Automatic Data Transformation Using Large Language Model-An Experimental Study on Building Energy Data. In 2023 IEEE International Conference on Big Data (BigData).
• Pradhan, O., Pertzborn, A., Zhang, L., & Wen, J. (2020). Development and Validation of a Simulation Testbed for the Intelligent Building Agents Laboratory (IBAL) Using TRNSYS (VC-20-C056). In 2020 ASHRAE Virtual Conference.
• Zhang, L., Wen, J., & Chen, Y. (2017). Systematic Feature Selection Process Applied in Short-Term Data-Driven Building Energy Forecasting Models: A Case Study of a Campus Building. In Dynamic Systems and Control Conference, 58295.
• Liu, C., Xu, P., Chen, W., Zhang, L., & Li, W. (2015). Study of Urban Patterns Optimization Employing CFD Method: A Case Study of Chenjiazhen Experimental Ecological Community, Chongming, Shanghai. In Building Resilient Cities in China: The Nexus between Planning and Science: Selected Papers from the 7th International Association for China Planning Conference, Shanghai, China, June 29--July 1, 2013.
• Zhang, L., Xu, P., & Li, Z. W. (2014). Relationship between Energy Consumption and Service Level: A Survey of Class A Office Buildings in Shanghai. In Advanced Materials Research, 953.
• Zhang, L., Xu, P., Mao, J. C., & Tang, X. u. (2014). Design and application of a seasonal solar soil heat storage system applied in greenhouse heating. In Applied Mechanics and Materials, 672.

Others

• Bae, Y., Cui, B., Joe, J., Im, P., Adetola, V., Zhang, L., Leach, M., & Kuruganti, T. (2020, 1). Sensor Impact on Building Controls and Automatic Fault Detection and Diagnosis (AFDD).
• Frank, S. M., Lin, G., Jin, X., Singla, R., Farthing, A., Zhang, L., & Granderson, J. (2019, 1). Metrics and methods to assess building fault detection and diagnosis tools.
• Horsey, H., Parker, A., Farthing, A., Dahlhausen, M., Praprost, M., Bianchi, C., Robertson, J., Horowitz, S., & Zhang, L. (2020, 1). ComStock™.
• Im, P., Bae, Y., Cui, B., Lee, S., Bhattacharya, S., Adetola, V., Vrabie, D., Zhang, L., & Leach, M. (2020, 1). Sensor Impacts Evaluation and Verification: Expert Interview Responses.
• Wilson, E. J., Parker, A., Fontanini, A., Present, E., Reyna, J. L., Adhikari, R., Bianchi, C., CaraDonna, C., Dahlhausen, M., Kim, J., & others, . (2022, March). End-Use Load Profiles for the US Building Stock: Methodology and Results of Model Calibration, Validation, and Uncertainty Quantification.