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Open Access Publications from the University of California

Scholarly Works (3 results)

  • Article
  • Peer Reviewed
Controls and Information Technology (2009)

This study developed several technologies to enable demand response for residential electricity customers in order to reduce peak consumption. First, along with a team of students and professors, I designed and tested the Demand Response Electrical Appliance Manager (DREAM). This wireless network of sensors, actuators, and controller with a user interface intelligently controls a residential heating and cooling system and informs people of their energy usage. Secondly, I evaluated machine-learning to predict a person’s seasonal temperature preferences by analyzing existing data from office workers. The third part of the research developed an algorithm that generated temperature setpoints based on outdoor temperature and compared simulated energy use with these versus the default setpoints of a programmable thermostat. Finally, I developed and tested a user interface for a thermostat and in-home energy display. This part of the study tested the effects of both energy versus price information and the context of sponsorship on the behavior of subjects, and also surveyed subjects on the usefulness of various displays.

The wireless network succeeded in providing detailed data to enable an intelligent controller and provide feedback to the users. The learning algorithm showed mixed results. The adaptive temperature setpoints saved energy in both annual and summertime simulations. The context in which I introduced the DREAM interface affected behavior, but the type of information displayed did not. The subjects responded that appliance-level feedback and tools that provided choices would be useful in a dynamic tariff environment.

    Cover page: California DREAMing: the design of residential demand responsive technology with people in mind
    • Article
    • Peer Reviewed
    Planning & Evaluation (2023)
    Given the uncertainty around climate change and the need to design systems that anticipate future needs, risks, and costs or values related to resilience, the current rules-based regulatory and policy frameworks designed for the centralized system of large-scale energy generation and delivery may not be ‘fit for purpose' for smaller scale local installations centered on community microgrids. This research examines regulatory challenges and potential impediments to implementing a multi-customer community-based microgrid in California through discussion of lessons learned in current pilot projects supported in part by initiatives of the California Energy Commission's Electric Program Investment Charge (EPIC). The extent to which regulation has the flexibility to anticipate future needs and risks and support experimentation is evaluated in light of the state's complex and evolving energy system requirements. To illustrate challenges, two case studies of EPIC-supported projects are included. Multiple uncertainties, including future impacts of climate change, energy demands, and advances in technology, highlight the potential need to rethink best approaches to energy regulation. Principles drawn from Resilience Thinking and Anticipatory Regulation are discussed for their potential value in supporting development of new models for community-scale energy production, distribution, and use. Drawing on the experiences of the pilot projects, suggested principles to guide a new regulatory regime specific to microgrids are proposed.
      Cover page: Advancing California's microgrid communities through anticipatory energy resilience
      • Article
      • Peer Reviewed
      Indoor Environmental Quality (IEQ) (2019)
      Indoor air movement affects many functions of buildings, including ventilation and air quality, comfort and health of occupants, fire safety, and building energy use. Accurately measuring air movement has been difficult and expensive over extended periods of time, especially for velocities below 1 m/s. A new type of high frequency ultrasonic transceiver provides high sensitivity measurements and low cost through microelectromechanical systems (MEMS) manufacturing. However, at high frequencies, conventional ultrasonic signal processing algorithms function only over small ranges of ambient temperature and velocity. In this paper, we describe three algorithms that use the complex phase angle of an ultrasonic pulse to measure velocity and temperature over extended ranges of temperature and velocity. They employ heuristics to track the vibration cycle of the measured phase angle. These methods are applied in a pulse-based anemometer whose 176kHz MEMS transceivers both transmit and receive. In wind tunnel tests between 0-4 m/s, the tracking algorithm with a low-pass filter measured air speed with high sensitivity and accuracy (0.026 m/s mean absolute error). The ability to monitor to this accuracy with such low power draw and low cost is currently unprecedented in the industry.
        Cover page: Measuring air speed with a low-power MEMS ultrasonic anemometer via adaptive phase trackingCreative Commons 'BY-NC-SA' version 4.0 license
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