• Energy Research

This paper surveys past and current research of optimal control for central chilled water plants with ice thermal energy storage. The motivation for thermal energy storage in commercial buildings is provided and common operating strategies for ice storage including their implementation are presented. The concept of optimality serves as the basis for introducing the various approaches to optimal control of thermal energy storage. Optimal strategies minimizing either energy or demand costs, near-optimal rule based control minimizing total cost, comfort-based energy optimal control, and combined optimal sizing and energy cost optimal control are discussed and contrasted with mathematically non-optimal, but heuristically improved operating strategies. Fully optimal control based on perfect knowledge is introduced and subsequent developments of predictive optimal control subject to uncertain weather, load, and utility rate information illustrated. In addition, recent investigations of adaptive optimal reinforcement learning based control are presented.

Abstract Buildings account for over 70% of the electricity use in the US. As cities grow, high peaks of electricity consumption are becoming more frequent, which leads to higher prices for electricity. Demand response is the coordination of electrical loads such that they react to price signals and coordinate with each other to shave the peaks of electricity consumption. We explore the use of multi-agent deep deterministic policy gradient (DDPG), an adaptive and model-free reinforcement learning control algorithm, for coordination of several buildings in a demand response scenario. We conduct our experiment in a simulated environment with 10 buildings.

Architects and planners have been at the forefront of envisioning a future built environment for millennia. However, fragmental views that emphasize one facet of the built environment, such as energy, environment, or groundbreaking technologies, often do not achieve expected outcomes. Buildings are responsible for approximately one-third of worldwide carbon emissions and account for about 40% of primary energy consumption in the U.S. In addition to achieving the very ambitious goal of reducing building-associated greenhouse gas emissions by 75% by 2050, buildings must improve their functionality and performance to meet current and future human, societal, and environmental needs in a changing world. In this article, we introduce a new framework to guide potential evolution of the building stock in the next century, based on greenhouse gas emissions as the common thread to investigate the potential implications of new design paradigms, innovative operational strategies, and disruptive technologies. This framework emphasizes integration of multidisciplinary knowledge, scalability for mainstream buildings, and proactive approaches considering constraints and unknowns. The framework integrates the interrelated aspects of the built environment through a series of quantitative metrics that aim to improve environmental outcomes while optimizing building performance to achieve healthy, adaptive, and productive buildings.

This paper reviews the literature on occupancy-based lighting control as a prelude to the application of sensor networks to building management. Many buildings include systems to detect occupancy and control building services. Current systems use single measurement points to detect occupancy, and there can be significant uncertainty associated with the measurement of occupancy. Long time delay and high detector sensitivity settings compensate for this uncertainty, but these diminish the savings that could be achieved with more accurate occupancy measurement. More effective control may be provided by more extensive sensing, using a network of occupancy sensors, and more extensive analysis of sensor data. The literature reviewed in this paper establishes the need for an investigation of the performance of sensor networks when used for lighting control.

Abstract A prototype energy signal tool is demonstrated for operational whole-building and system-level energy use evaluation. The purpose of the tool is to give a summary of building energy use which allows a building operator to quickly distinguish normal and abnormal energy use. Toward that end, energy use status is displayed as a traffic light, which is a visual metaphor for energy use which is substantially different from expected (red and yellow lights) or more or less the same as expected (green light). Which light to display for a given energy end-use is determined by comparing expected energy use to actual energy use. As expected energy use is necessarily uncertain, we cannot choose the appropriate light with certainty. Instead the energy signal tool chooses the light by minimizing the expected cost of displaying the wrong light. The expected energy use is represented by a probability distribution. Energy use is modeled by a low-order lumped parameter model. Uncertainty in energy use is quantified by a Monte Carlo exploration of the influence of model parameters on energy use. Distributions over model parameters are updated over time via Bayes’ theorem. The simulation study is devised to assess whole building energy signal accuracy in the presence of uncertainty and faults at the submetered level, which may lead to tradeoffs at the whole building level not detectable without submetering.

Abstract A module for ice-based thermal energy storage (TES) systems has been developed and integrated within EnergyPlus. The TES module uses building load and system thermodynamics (BLAST) models for two direct ice systems (ice-on-coil external melt and ice harvester) and one indirect ice system (ice-on-coil internal melt). The TES systems are integrated as part of the EnergyPlus cooling plant components and are able to operate for any charge/discharge rates provided as input data. In this paper, the structure of the TES module as implemented in EnergyPlus is described. In addition, typical input–output variables from the added TES module are illustrated. Moreover, the operation of the TES systems is discussed for various conventional control strategies.

Abstract This research article presents the development and demonstration of a monitoring based commissioning system for commercial buildings. An energy model of an educational building located on the campus of the University of Colorado Boulder was developed and was calibrated to conform to ASHRAE Guideline 14 using hourly measured data. A Latin Hypercube Monte Carlo (LHMC) sampling algorithm was used to obtain a set of plausible solutions by varying each key building parameter. A regional sensitivity analysis was then used to identify the parameters that had the greatest impact on the model's energy performance using a Goodness of Fit (GOF) metric. The calibrated model is used to compare actual building energy use to modeled energy use over various time scales. Deviations in consumption beyond adjustable predefined thresholds are detected as discrete events using a commercially available energy informatics system, while relative deviations against the model are quantified and visualized using a custom energy management application providing insight on model-to-actual deviations over daily, weekly, monthly, quarterly and annual time horizons. Finally, utilizing weather and solar radiation forecasts, the effectiveness of said energy model in a predictive context was investigated, allowing operators to receive 24–48-h predictions of energy consumption and demand by end use as well as forecasts of building variables such as zone temperatures. The model based commissioning system successfully predicted energy and demand in terms of magnitude and timing and correctly forecasted cooling capacity shortfalls.

This paper investigates adaptive optimal control of a grid-independent photovoltaic system consisting of a collector, storage, and a load. The algorithm is based on Q-Learning, a model-free reinforcement learning algorithm, which optimizes control performance through exploration. Q-Learning is used in a simulation study to find a policy which performs better than a conventional control strategy with respect to a cost function which places more weight on meeting a critical base load than on those non-critical loads exceeding the base load.

Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.