[J1] 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.
[J2] Zhang, L., Xu, P., Mao, J., Tang, X., 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.
[J3] Zhang, L., Plathottam, S., Reyna, J., Merket, N., ... & Muehleisen, R. (2021). High-Resolution Hourly Surrogate Modeling Framework for Physics-Based Large-Scale Building Stock Modeling. Sustainable Cities and Society, 103292.
[J4] Zhang, L., & Wen, J. (2021). Active Learning Strategy for High Fidelity Short-Term Data-Driven Building Energy Forecasting. Energy and Building, 111026.
[J5] 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.
[J6] Zhang, L., & Leach, M. (2021, August). Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation. Building Simulation (pp. 1-10).
[J7] 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, 107338.
[J8] Zhang, L., Leach, M., Bae, Y., Cui, B., Bhattacharya, S., ... & Kuruganti, T. (2021). Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review. Advances in Applied Energy, 100055.
[J9] Zhang, L. (2020). A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems. Sensors, 20(20), 5947.
[J10] Zhang, L., Alahmad, M., & Wen, J. (2020). 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, 110592.
[J11] Zhang, L., Data-Driven Building Energy Modeling with Feature Selection and Active Learning for Data Predictive Control. Energy and Buildings, 2021: p. 111436.
[J12] Zhang, L., & Xu, P. (2014). CFD Model Establishment of Under-floor Air Distribution and IT Equipment Heat Dissipation of Data Center. Building Energy Efficiency, 8, 16-17.
[J13] Zhang, L., & Xu, P. (2014). Evaluation Index of Thermal Environment in Data Center: a Review. Building Energy Efficiency, 6, 92-100
[J14] Chen, J., Zhang, L., Li, Y., …, & 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.
[J15] 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.
[J16] Li, Y., O’Neil, Z, Zhang, L., et al., Grey-box modeling and application for building energy simulations - A critical review. Renewable and Sustainable Energy Reviews, 2021. 146: p. 111174.
[J17] Bae, Y., Bhattacharya, S., Cui, B., Lee, S., Li, Y., Zhang, L., ... & Kuruganti, T. (2021). Sensor impacts on building and HVAC controls: A critical review for building energy performance. Advances in Applied Energy, 100068.
[J18] Zhang, L., Leach, M., Chen, J., & Hu, Y. (2022). Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings. Energy, 125577.
[J19] Zhang, L., Chen, Z., Zhang, X., Pertzborn, A., & Jin, X. (2023, March). Challenges and opportunities of machine learning control in building operations. In Building Simulation (pp. 1-22). Beijing: Tsinghua University Press.
[J20] Zhang, L., Kaufman, Z., & Leach, M. (2024). Physics-informed hybrid modeling methodology for building infiltration. Energy and Buildings, 114580.
[J21] Jiang, G., Ma, Z., Zhang, L., & Chen, J. (2024). EPlus-LLM: A large language model-based computing platform for automated building energy modeling. Applied Energy, 367, 123431.
[C1] Zhang, L., & Leach, M. Sensor Cost-Effectiveness Analysis for Data-Driven Fault Detection and Diagnostics in Commercial Buildings, in 2022 ASHRAE Annual Conference: June 25-29, 2022, Toronto, ON, Canada.
[C2] Zhang, L., & Wen, J. Application of Active Learning in Short-term Data-driven Building Energy Modeling, in 2018 International High Performance Buildings Conference; July 9-12, 2018, West Lafayette, IN, U.S.
[C3] Zhang, L., Wen, J., & Chen, Y. Systematic Feature Selection Process Applied in Short-Term Data-Driven Building Energy Forecasting Models: A Case Study of a Campus Building, in 2017 ASME Dynamic Systems and Control Conference; Oct.11-13, 2017, Tysons, VA, U.S.
[C4] Zhang, L., & Wen, J. A Systematic Feature Selection Procedure for Data-driven Building Energy Forecasting Model Development, in 2017 ASHRAE Annual Conference; June 24-28, 2017, Long Beach, CA, U.S.
[C5] Zhang, L., Wen, J., Cui, C., Li, X., & Wu, T. Experiment Design and Training Data Quality of Inverse Model for Short-term Building Energy Forecasting, in 2016 International High Performance Buildings Conference; July 11-14, 2016, West Lafayette, IN, U.S.
[C6] Zhang, L., Xu, P., & Li, Z. Relationship between Energy Consumption and Service Level: A Survey of Grade-A Office Buildings in Shanghai, in 2014 International Conference on Renewable Energy and Environmental Technology; Aug. 19-20, 2014, Dalian, China.
[C7] Zhang, L., Xu, X., & Xu, P. EnergyPlus Reference Building Modeling in China and Adaptability of Energy-saving Technologies in Buildings, in 1st IBPSA Asian Conference; Nov. 25-27, 2012, Shanghai, China.
Technical Report
[T1] Wilson, E., Parker. A., …, Zhang, L., et al. (2022). End-Use Load Profiles for the US Building Stock: Methodology and Results of Model Calibration, Validation, and Uncertainty Quantification (No. NREL/TP-5500-80889). National Renewable Energy Lab.(NREL), Golden, CO (United States).
[T2] Frank, S. M., ..., Zhang, L., & Granderson, J. (2019). Metrics and Methods to Assess Building Fault Detection and Diagnosis Tools (No. NREL/TP-5500-72801). National Renewable Energy Lab.(NREL), Golden, CO (U.S.).
[T3] Im, P., …, Zhang, L., & Leach, M. (2020). Literature Review for Sensor Impact Evaluation and Verification Use Cases-Building Controls and Fault Detection and Diagnosis (FDD) (No. ORNL/LTR-2020/23). Oak Ridge National Lab.(ORNL), Oak Ridge, TN (U.S.).
[T4] Bae, Y., …, Zhang, L, ..., & Kuruganti, T. (2020) Sensor Impact on Building Controls and Automatic Fault Detection and Diagnosis (AFDD) (No. ORNL/LTR-2020/1).Oak Ridge National Lab.(ORNL), Oak Ridge, TN (U.S.).
[T5] Im, P., …, Zhang, L., & Leach, M. (2020). Sensor Impacts Evaluation and Verification: Expert Interview Responses. Oak Ridge National Lab.(No. ORNL/TM-2020/1636), Oak Ridge, TN (U.S.).
Patent and Software
[S1] Horsey, H., …, Zhang, L., ComStock™, US DOE Office of Energy Efficiency and Renewable Energy, & USDOE Laboratory Directed Research and Development. (2020, October 18). ComStock™ [Computer software]. https://www.osti.gov//servlets/purl/1817851. https://doi.org/10.11578/dc.20210830.5
[S2] Zhang, L., Wen, J. (2021) Active Learning for Building Load Prediction. EPRI licensed code.
[P1] Xu, P., Zhang, L., Shi, J., Sha, H., & Chen, L. (2016). Seasonal Solar Soil Heat Storage System Applied in Greenhouse Heating. Patent No. CN103782846 B.