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Open Access Publications from the University of California

Scholarly Works (30 results)

  • Article
Building Efficiency and Sustainability in the Tropics (SinBerBEST) (2017)
Indoor environmental quality (IEQ) monitoring is a critical task in building operation, maintenance, and diagnosis. Current approach based on static sensor network is not scalable for IEQ assessment that relies on costly sensing instruments. The study proposes to leverage autonomous mobility to reduce sensing infrastructure cost and enable real-time high-granularity monitoring that can be otherwise inhibitively laborious. Unique to the autonomous mobile sensing methodology, the collected IEQ samples are highly sparse in both spatial and temporal domains. The study develops spatio-temporal (ST) interpolation methods based on ST binning, global trend extraction, and local variation estimation, which efficiently use the data to construct accurate depiction of the indoor environment evolution. The method is evaluated by a standard protocol for ventilation assessment, where the estimation is shown to be highly correlated with the ground truth, and reveals the true ventilation conditions.
    Cover page: Indoor environmental quality monitoring by autonomous mobile sensing
    • Article
    • Peer Reviewed
    Building Efficiency and Sustainability in the Tropics (SinBerBEST) (2017)
    Indoor environmental quality (IEQ) is a critical aspect of the built environment to ensure occupant health, comfort, well-being and productivity. Existing IEQ monitoring approaches rely on sensor networks deployed at selected locations to collect environmental measurements, and are limited in scale and adaptability due to infrastructure cost and maintenance requirement. To enable high-granularity IEQ monitoring with agile adaption to the dynamic indoor environment, we propose an “automated mobile sensing” system that dispatches a sensor-rich navigation-capable robot to actively survey the indoor space. Data collected in this fashion is sparse in the joint temporal and spatial domain, and cannot be used directly for IEQ evaluation. To deal with this special characteristics, we developed a spatio-temporal interpolation algorithm to capture the global trend and local variation in order to use the data efficiently to reconstruct the IEQ dynamics. We compared the performance of the automated mobile sensing with a dense sensor network in a laboratory where we measured the air-change effectiveness (ASHRAE standard 129) for four different conditions. Results indicate that automated mobile sensing is able to accurately estimate the parameters with a minimal sensor cost and calibration effort. Potential applications of this system include indoor thermal comfort, lighting, indoor air quality and acoustic monitoring, pollutant source identification, and building commissioning. We shared publicly the source codes for robot control, sensor setup, and interpolation algorithm to encourage comparison study and further development.
      Cover page: Automated mobile sensing: Towards high-granularity agile indoor environmental quality monitoring
      • Thesis
      • Peer Reviewed
      UC Berkeley Electronic Theses and Dissertations (2017)

      The goal of this research is to enable optimal human-cyber-physical systems (h-CPS) by data-efficient analytics. The capacities of societal-scale infrastructures such as smart buildings and power grids are rapidly increasing, becoming physical systems capable of cyber computation that can deliver human-centric services while enhancing efficiency and resilience. Because people are central to h-CPS, the first part of this thesis is dedicated to learning about the human factors, including both human behaviors and preferences. To address the central challenge of data scarcity, we propose physics-inspired sensing by proxy and a framework of "weak supervision" to leverage high-level heuristics from domain knowledge. To infer human preferences, our key insight is to learn a functional abstraction that can rationalize people's behaviors. Drawing on this insight, we develop an inverse game theory framework that determines people's utility functions by observing how they interact with one another in a social game to conserve energy. We further propose deep Bayesian inverse reinforcement learning, which simultaneously learns a motivator representation to expand the capacity of modeling complex rewards and rationalizes an agent's sequence of actions to infer its long-term goals.

      Enabled by this contextual awareness of the human, cyber, and physical states, we introduce methods to analyze and enhance system-level efficiency and resilience. We propose an energy retail model that enables distributed energy resource utilization and that exploits demand-side flexibility. The synergy that naturally emerges from integrated optimization of thermal and electrical energy provision substantially improves efficiency and economy. While data empowers the aforementioned h-CPS learning and control, malicious attacks can pose major security threats. The cyber resilience of power system state estimation is analyzed. The envisioning process naturally leads to a power grid resilience metric to guide "grid hardening." While the methods introduced in the thesis can be applied to many h-CPS systems, this thesis focuses primarily on the implications for smart buildings and smart grid.

        Cover page: Data-efficient Analytics for Optimal Human-Cyber-Physical Systems
        • Article
        Building Efficiency and Sustainability in the Tropics (SinBerBEST) (2014)
        In this paper, the modeling of building end-use energy profile is comprehensively investigated. Top-down and Bottom-up approaches are discussed with a focus on the latter for better integration with occupant information. Compared to the Time-Of-Use (TOU) data used in previous Bottom-up models, this work utilizes high frequency sampled appliance power consumption data from wireless sensor network, and hence builds an appliance-data-driven probability based end-use energy profile model. ON/OFF probabilities of appliances are used in this model, to build a non-homogeneous Markov Chain, compared to the duration statistics based model that is widely used in other works. The simulation results show the capability of the model to capture the diversity and variability of different categories of end-use appliance energy profile, which can further help on the design of a modern robust building power system.
          Cover page: Modeling of End-Use Energy Profile: An Appliance-Data-Driven Stochastic Approach
          • Article
          • Peer Reviewed
          UC Berkeley Previously Published Works (2017)
          In the face of unprecedented challenges of upcoming fossil fuel shortage and reliability and security of the grid, there is an increasing interest in adopting distributed, renewable, energy resources, such as microgrids (MGs), and engaging flexible electric loads in power system operations to potentially drive a paradigm shift in energy production and consumption patterns. Prior work on MG dispatch has leveraged decentralized technologies like combined heat and power (CHP) and heat pumps to promote efficiency and economic gains; however, the flexibility of demand has yet to be fully exploited in cooperation with the grid to offer added benefits and ancillary services. The object of the study is to develop microgrid optimal dispatch with demand response (MOD-DR), which fills in the gap by coordinating both the demand and supply sides in a renewable-integrated, storage-augmented, DR-enabled MG to achieve economically viable and system-wide resilient solutions. The key contribution of this paper is the formulation of a multi-objective optimization with prevailing constraints and utility trade-off based on the model of a large-scale MG with flexible loads, which leads to the derivation of strategies that incorporate uncertainty in scheduling. Evaluation using real datasets is conducted to analyze the uncertainty effects and demand response potentials, demonstrating in a campus prototype a 17.5% peak load reduction and 8.8% cost savings for MOD-DR compared to the non-trivial baseline, which is on par with the Oracle for perfect predictions.
            Cover page: MOD-DR: Microgrid optimal dispatch with demand response
            • Article
            • Peer Reviewed
            Indoor Environmental Quality (IEQ) (2018)
            Indoor environmental quality (IEQ) is a critical aspect of the built environment to ensure occupant health, comfort, well-being and productivity. Existing IEQ monitoring approaches rely on sensor networks deployed at selected locations to collect environmental measurements, and are limited in scale and adaptability due to infrastructure cost and maintenance requirement. To enable high-granularity IEQ monitoring with agile adaption to the dynamic indoor environment, we propose an “automated mobile sensing” system that dispatches a sensor-rich navigation-capable robot to actively survey the indoor space. Data collected in this fashion is sparse in the joint temporal and spatial domain, and cannot be used directly for IEQ evaluation. To deal with this special characteristics, we developed a spatio-temporal interpolation algorithm to capture the global trend and local variation in order to use the data efficiently to reconstruct the IEQ dynamics. We compared the performance of the automated mobile sensing with a dense sensor network in a laboratory where we measured the air-change effectiveness (ASHRAE standard 129) for four different conditions. Results indicate that automated mobile sensing is able to accurately estimate the parameters with a minimal sensor cost and calibration effort. Potential applications of this system include indoor thermal comfort, lighting, indoor air quality and acoustic monitoring, pollutant source identification, and building commissioning. We shared publicly the source codes for robot control, sensor setup, and interpolation algorithm to encourage comparison study and further development.
              Cover page: Automated mobile sensing: Towards high-granularity agile indoor environmental quality monitoringCreative Commons 'BY-NC-SA' version 4.0 license
              • Article
              • Peer Reviewed
              LBL Publications (2020)
              We present an initial study of off-policy evaluation (OPE), a problem prerequisite to real-world reinforcement learning (RL), in the context of building control. OPE is the problem of estimating a policy's performance without running it on the actual system, using historical data from the existing controller. It enables the control engineers to ensure a new, pretrained policy satisfies the performance requirements and safety constraints of a real-world system, prior to interacting with it. While many methods have been developed for OPE, no study has evaluated which ones are suitable for building operational data, which are generated by deterministic policies and have limited coverage of the state-action space. After reviewing existing works and their assumptions, we adopted the approximate model (AM) method. Furthermore, we used bootstrapping to quantify uncertainty and correct for bias. In a simulation study, we evaluated the proposed approach on 10 policies pretrained with imitation learning. On average, the AM method estimated the energy and comfort costs with 1.84% and 14.1% error, respectively.
                Cover page: Towards Off-policy Evaluation as a Prerequisite for Real-world Reinforcement Learning in Building Control
                • Article
                • Peer Reviewed
                UC Berkeley Previously Published Works (2018)
                In the face of unprecedented challenges in environmental sustainability and grid resilience, there is an increasingly held consensus regarding the adoption of distributed and renewable energy resources such as microgrids (MGs), and the utilization of flexible electric loads by demand response (DR) to potentially drive a necessary paradigm shift in energy production and consumption patterns. However, the potential value of distributed generation and demand flexibility has not yet been fully realized in the operation of MGs. This study investigates the pricing and operation strategy with DR for a MG retailer in an integrated energy system (IES). Based on co-optimizing retail rates and MG dispatch formulated as a mixed integer quadratic programming (MIQP) problem, our model devises a dynamic pricing scheme that reflects the cost of generation and promotes DR, in tandem with an optimal dispatch plan that exploits spark spread and facilitates the integration of renewables, resulting in improved retailer profits and system stability. Main issues like integrated energy coupling and customer bill reduction are addressed during pricing to ensure rates competitiveness and customer protection. By evaluating on real datasets, the system is demonstrated to optimally coordinate storage, renewables, and combined heat and power (CHP), reduce carbon dioxide emission while maintaining profits, and effectively alleviate the PV curtailment problem. The model can be used by retailers and MG operators to optimize their operations, as well as regulators to design new utility rates in support of the ongoing transformation of energy systems.
                  Cover page: Microgrid to enable optimal distributed energy retail and end-user demand response
                  • Article
                  • Peer Reviewed
                  Indoor Environmental Quality (IEQ) (2019)
                  A personal comfort model is an approach to thermal comfort modeling, for thermal environmental design and control, that predicts an individual's thermal comfort response, instead of the average response of a large population. We developed personal thermal comfort models using lab grade wearable in normal daily activities. We collected physiological signals (e.g., skin temperature, heart rate) of 14 subjects (6 female and 8 male adults) and environmental parameters (e.g., air temperature, relative humidity) for 2–4 weeks (at least 20 h per day). Then we trained 14 models for each subject with different machine-learning algorithms to predict their thermal preference. The results show that the median prediction power could be up to 24%/78%/0.79 (Cohen's kappa/accuracy/AUC) with all features considered. The median prediction power reaches 21%/71%/0.7 after 200 subjective votes. We explored the importance of different features on the prediction performance by considering all subjects in one dataset. When all features included for the entire dataset, personal comfort models can generate the highest performance of 35%/76%/0.80 by the most predictive algorithm. Personal comfort models display the highest prediction power when occupants' thermal sensations is outside thermal neutrality. Skin temperature measured at the ankle is more predictive than measured at the wrist. We suggest that Cohen's kappa or AUC should be employed to assess the performance of personal thermal comfort models for imbalanced datasets due to the capacity to exclude random success.
                    Cover page: Personal thermal comfort models with wearable sensorsCreative Commons 'BY-NC-SA' version 4.0 license
                    • Article
                    • Peer Reviewed
                    Building Efficiency and Sustainability in the Tropics (SinBerBEST) (2014)
                    We present analysis and results of a social game encouraging energy efficient behavior in occupants by distributing points which determine the likelihood of winning in a lottery. We estimate occupants utilities and formulate the interaction between the building manager and the occupants as a reversed Stackelberg game in which there are multiple followers that play in a non-cooperative game. The estimated utilities are used for determining the occupant behavior in the non-cooperative game. Due to nonconvexities and complexity of the problem, in particular the size of the joint distribution across the states of the occupants, we solve the resulting the bilevel optimization problem using a particle swarm optimization method. Drawing from the distribution across player states, we compute the Nash equilibrium of the game using the resulting leader choice. We show that the behavior of the agents under the leader choice results in greater utility for the leader.
                      Cover page: Social Game for Building Energy Efficiency: Incentive Design
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