Bio

Aqsa Naeem works in the Department of Energy Science and Engineering at Stanford University. Her research spans various aspects of energy systems, including data-driven modeling of different entities and the design and control of sustainable, energy-efficient systems. Her current work focuses on leveraging data analytics for building energy management, with an interest in creating insights that drive innovation through interactive visualization, modeling tools, and custom dashboard designs.

Naeem obtained her PhD in Electrical Engineering from Lahore University of Management Sciences (LUMS) in Pakistan, where she worked on designing resilient, cost-effective microgrids to promote the adoption of renewable energy in the power sector. Her work emphasizes the importance of using complementary energy sources to mitigate the inherent intermittency of renewable energy.

Academic Appointments

Contact

  • Academic
    anaeem@stanford.edu
    University - Academic staff Department: Energy Science & Engineering Position: Physical Science Research Scientist

Additional Info

  • Mail Code: 2220
  • SUNet ID(s):
    anaeem

Links

Research Interests

  • Assessment, Testing and Measurement
  • Data Sciences

Current Research and Scholarly Interests

Energy System Modeling and Optimization

All Publications

  • Data-driven characterization of cooling needs in a portfolio of co-located commercial buildings ISCIENCE Naeem, A., Benson, S. M., de Chalendar, J. A. 2024; 27 (7)

    View details for DOI 10.1016/j.isci.2024.110398

    View details for Web of Science ID 001268388800001

  • Data-driven characterization of cooling needs in a portfolio of co-located commercial buildings. iScience Naeem, A., Benson, S. M., de Chalendar, J. A. 2024; 27 (7): 110398

    Abstract

    The increasing cooling needs in commercial buildings, exacerbated by climate change, warrant immediate attention. These buildings, characterized by their long lifespans and slow stock turnover, change consumption over time. This study develops simple, interpretable data-driven models using weather- and occupancy-related features to analyze the cooling in different types of co-located buildings. Over five years, our models effectively predict the cooling load across buildings with R-squared values of 81%-87%. Factoring out geography-driven differences, we identify strong heterogeneity within and across different buildings. The average estimated base load cooling varies between 0.50 and 4.4 MJ/m2/day across buildings, with healthcare facilities exhibiting the highest loads and residences the lowest. Consumption increases by 7.6%-9.8% for every 1°C increase in mean daily outside temperature, with up to 27% reductions in offices on weekends. These insights enable diagnoses of inefficiencies, post-retrofitting performance tracking, and proactive planning for climate-related impacts.

    View details for DOI 10.1016/j.isci.2024.110398

    View details for PubMedID 39092179

    View details for PubMedCentralID PMC11292538

  • Maximizing the Economic Benefits of a Grid-Tied Microgrid Using Solar-Wind Complementarity ENERGIES Naeem, A., Ul Hassan, N., Yuen, C., Muyeen, S. M. 2019; 12 (3)

    View details for DOI 10.3390/en12030395

    View details for Web of Science ID 000460666200063

  • Indoor Positioning Using Visible LED Lights: A Survey ACM COMPUTING SURVEYS Ul Hassan, N., Naeem, A., Pasha, M., Jadoon, T., Yuen, C. 2015; 48 (2)

    View details for DOI 10.1145/2835376

    View details for Web of Science ID 000368081600004

  • Understanding Customer Behavior in Multi-Tier Demand Response Management Program IEEE ACCESS Naeem, A., Shabbir, A., Ul Hassan, N., Yuen, C., Ahmad, A., Tushar, W. 2015; 3: 2613–25

    View details for DOI 10.1109/ACCESS.2015.2507372

    View details for Web of Science ID 000371388200199