Zheng O'Neill

Uncertainty and sensitivity decomposition of building energy models

As building energy modelling becomes more sophisticated, the amount of user input and the number of parameters used to define the models continue to grow. There are numerous sources of uncertainty in these parameters, especially when the modelling process is being performed before construction and commissioning. Past efforts to perform sensitivity and uncertainty analysis have focused on tens of parameters, while in this work, we increase the size of analysis by two orders of magnitude (by studying the influence of about 1000 parameters). We extend traditional sensitivity analysis in order to decompose the pathway as uncertainty flows through the dynamics, which identifies which internal or intermediate processes transmit the most uncertainty to the final output. We present these results as a method that is applicable to many different modelling tools, and demonstrate its applicability on an example EnergyPlus model.

Integrating Home Energy Simulation and Dynamic Electricity Price for Demand Response Study

This paper studies how to develop and evaluate demand response strategies from the consumers perspective through a computational experiment approach. The proposed approach includes a home energy consumption simulator, a demand response mechanism obtained through optimization, particle swam or heuristic method, and an integrative computing platform that combines the home energy simulator and MATLAB together for demand response development and evaluation. Several demand response strategies are developed and evaluated through the computational experiment technique. The paper investigates and compares characteristics of different demand response strategies and how they are affected by dynamic pricing tariffs, seasons, and weather. Case studies are conducted by considering home energy consumption, dynamic electricity pricing schemes, and demand response methods.

An Optimal and Learning-Based Demand Response and Home Energy Management System

This paper focuses on developing an interdisciplinary mechanism that combines machine learning, optimization, and data structure design to build a demand response and home energy management system that can meet the needs of real-life conditions. The loads of major home appliances are divided into three categories: 1) containing fixed loads; 2) regulate-able loads; and 3) deferrable loads, based on which a decoupled demand response mechanism is proposed for optimal energy management of the three categories of loads. A learning-based demand response strategy is developed for regulateable loads with a special focus on home heating, ventilation, and air conditioning (HVACs). This paper presents how a learning system should be designed to learn the energy consumption model of HVACs, how to integrate the learning mechanism with optimization techniques to generate optimal demand response policies, and how a data structure should be designed to store and capture current home appliance behaviors properly. This paper investigates how the integrative and learning-based home energy management system behaves in a demand response framework. Case studies are conducted through an integrative simulation approach that combines a home energy simulator and MATLAB together for demand response evaluation.

Model-based real-time whole building energy performance monitoring and diagnostics

Building energy systems often consume approximately 16% more energy [Mills, E. 2011. “Building Commissioning: A Golden Opportunity for Reducing Energy Costs and Greenhouse Gas Emissions in the United States.” Energy Efficiency 4 (2): 145–173] than is necessary due to system deviation from the design intent. Identifying the root causes of energy waste in buildings can be challenging largely because energy flows are generally invisible. To help address this challenge, we present a model-based, real-time whole building energy diagnostics and performance monitoring system. The proposed system continuously acquires performance measurements of heating, ventilation and air-conditioning, lighting and plug equipment usage and compare these measurements in real-time to a reference EnergyPlus model that either represents the design intent for the building or has been calibrated to represent acceptable performance. A proof-of-concept demonstration in a real building is also presented.

Building energy doctors: SPC and Kalman filter-based fault detection

Buildings worldwide account for nearly 40% of global energy consumption. The biggest energy consumer in buildings is the Heating, Ventilation and Air Conditioning (HVAC) systems. HVAC also ranks top in terms of number of complaints by tenants. Maintaining HVAC systems in good conditions through early fault detection is thus a critical issue. The problem, however, is difficult since HVAC systems are large in scale, consisting of many coupling subsystems, building and equipment dependent, and operating under uncertain conditions. In this paper, a model-based and data-driven method is presented for robust system-level fault detection with potential for large-scale implementation. It is a synergistic integration of (1) Statistical Process Control (SPC) for measuring and analyzing variations; (2) Kalman filtering based on gray-box models to provide predictions and determine SPC control limits; and (3) system analysis for analyzing fault propagation. The method has been tested against a simulation model of a 420-meter-high building. It detects both sudden faults and gradual degradation, and differentiates faults within a subsystem or propagated from elsewhere. Furthermore, the method is simple and generic, and should have good replicability and scalability.

Advanced building energy management system demonstration for Department of Defense buildings

This paper presents an advanced building energy management system (aBEMS) that employs advanced methods of whole‐building performance monitoring combined with statistical methods of learning and data analysis to enable identification of both gradual and discrete performance erosion and faults. This system assimilated data collected from multiple sources, including blueprints, reduced‐order models (ROM) and measurements, and employed advanced statistical learning algorithms to identify patterns of anomalies. The results were presented graphically in a manner understandable to facilities managers. A demonstration of aBEMS was conducted in buildings at Naval Station Great Lakes. The facility building management systems were extended to incorporate the energy diagnostics and analysis algorithms, producing systematic identification of more efficient operation strategies. At Naval Station Great Lakes, greater than 20% savings were demonstrated for building energy consumption by improving facility manager decision support to diagnose energy faults and prioritize alternative, energy‐efficient operation strategies. The paper concludes with recommendations for widespread aBEMS success.

Development of a Hardware-in-the-loop Framework with Modelica for Energy Efficient Buildings: Poster Abstract

This poster details a development of a real-time Hardware-in-the-loop (HIL) testbed for analysis of advanced and intelligent building control strategies using a real-time emulator with actual Heating, Ventilation and Air Conditioning (HVAC) local controllers. Models running on this real-time emulator are created using a non-proprietary, object-oriented, equation based language, namely, Modelica. A real-time HIL study of single maximum control logic for a Variable Air Volume (VAV) box in a single thermal zone is presented.