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Ten questions concerning reinforcement learning for building energy management
As buildings account for approximately 40% of global energy consumption and associated greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The increased integration of variable energy sources, such as renewables, introduces uncertainties and unprecedented flexibilities, necessitating buildings to adapt their energy demand to enhance grid resiliency. Consequently, buildings must transition from passive energy consumers to active grid assets, providing demand flexibility and energy elasticity while maintaining occupant comfort and health. This fundamental shift demands advanced optimal control methods to manage escalating energy demand and avert power outages. Reinforcement learning (RL) emerges as a promising method to address these challenges. Here, in this paper, we explore ten questions related to the application of RL in buildings, specifically targeting flexible energy management. We consider the growing availability of data, advancements in machine learning algorithms, open-source tools, and the practical deployment aspects associated with software and hardware requirements. Our objective is to deliver a comprehensive introduction to RL, present an overview of existing research and accomplishments, underscore the challenges and opportunities, and propose potential future research directions to expedite the adoption of RL for building energy management.
Design-Space Data: Informing Common Design Decisions with Pre-Simulated Data
Design Space Exploration (DSE) analysis techniques represent a data-centric approach to integrating performance analysis in early design phases when there is the greatest potential to cheaply improve the energy efficiency of a building. We focus on a novel extension of DSE called Universal Design Space Exploration (UDSE), which leverages massive databases of pre-simulated analysis that represent all possible outcomes of common analysis workflows. These databases, called Design Spaces, become “universal” when a single pre-simulated design space can be re-applied to future unknown projects. Unlike current simulation methods, which require a design to exist before it can be analyzed and often take minutes or hours to simulate, UDSE leverages pre-simulation to deliver rapid and relevant insight as new designs are conceptualized. The data underpinning UDSE enables advanced statistical and Artificial Intelligence methods, allowing UDSE to deliver a greater understanding of the larger problem being explored, rather than simply delivering analysis of several pre-conceived design options. We believe that UDSE can provide instantaneous, relevant analysis for all building design projects at negligible cost. This paper has two main goals, to develop a relevant Universal Design Space that showcases the potential of UDSE and to release this data freely to industry and academia; thereby lowering the barrier to entry to digital literacy in statistics, ML and AI within the architecture, engineering, construction (AEC) industry.
Performance Testing of Low-Cost Sensor Technology for Laboratories: Cooperative Research and Development Final Report (CRADA)
This work will gather real-world data on the performance and potential industry impact on laboratory ventilation control of a new low-cost, open-source sensor that reports important metrics of the laboratory environment. A small network of sensors will be installed at an NREL laboratory facility.
High-Efficiency Thermoelectric Clothes Dryer (CRADA Final Report)
A typical clothes dryer in the US accounts for 7% of the average residential customer’s electric bill. Nationwide, consumers pay about $\$$9 billion annually for clothes drying. While energy efficiency for most household appliances has improved by a factor of 2 or more in recent decades, today’s clothes dryers perform similarly to units from the 1970s. Dryer efficiency is measured by the combined energy factor (CEF), with today’s units typically drying 3.73 lb of cloth per kWh consumed. An ENERGY STAR qualified unit must achieve 3.93 lb/kWh (for standard size electric units) and dry in less than 80 minutes. ORNL and CRADA partner Samsung Electronics America have developed an efficient prototype clothes dryer that uses thermoelectric heat pumps instead of electric resistance to dry the clothes. The prototype fabricated at ORNL successfully demonstrated in the laboratory a CEF of 6.89 lb/kWh at standard conditions of 75°F and 50% Relative Humidity (RH), exceeding the original project target of 6.0 lb/kWh. Additional trials on the same prototype achieved faster dry time with slightly lower CEF, meeting all requirements for ENERGY STAR product qualification. Deploying dryers with energy factor of 6 nationwide represents a technical potential of 234 TBtu/yr primary energy savings. The modeling and prototype development activities for the thermoelectric clothes dryer under this CRADA are summarized in this final report.
New England Combined Heat and Power Technical Assistance Partnership (Final Technical Report)
The New England CHP TAP provided Technical Assistance (TA) to potential CHP end-users for the purpose of identifying, evaluating, and providing technical support to new and existing CHP projects. This report summarizes all contract milestones in support of this effort.
Regional Carbon Emission Reduction Prediction via Retrofits of Commercial Buildings with a Case Study of US School Buildings in Hot Climates
Retrofitting commercial buildings has great potential to reduce carbon emissions as demonstrated by previous studies in some specific cities, but their regional carbon emission reduction potential is still unknown. Thus, we develop a method to predict the long-term regional carbon emission reduction potential by retrofitting commercial buildings. School buildings in hot climate zones in the continental U.S. are selected as an example. The results show that the aggregated carbon emission reduction potential of school buildings in that region reduces from 3.33 to 2.01 megatons from 2024 to 2050 due to the increased penetration of renewable energy.
Two-dimensional materials for bio-realistic neuronal computing networks
Two-dimensional (2D) van der Waals materials have found broad utility in a diverse range of applications including electronics, optoelectronics, renewable energy, and quantum information technologies. Meanwhile, exponentially growing digital data coupled with the ubiquity of artificial intelligence algorithms have generated significant interest in edge neuromorphic computing as an alternative to centralized cloud computing. The drive to incorporate neuroscience principles into computing hardware is motivated by the low power consumption, parallel processing, and reconfigurability of the human brain. The diverse library of 2D materials with atomic-level thicknesses, exceptional electrostatic tunability, and integration versatility is particularly well-suited for realizing bio-realistic synaptic and neuronal functionality. Here, we summarize past and present work in this field and outline the frontier challenges that have not yet been overcome. Here we also delineate potential solutions and suggest that the neuroscience principles of criticality and synchrony have the potential to inspire breakthrough applications of 2D materials in neuronal computing networks.
Engineering isoprenoids production in metabolically versatile microbial host Pseudomonas putida
Abstract With the increasing need for microbial bioproduction to replace petrochemicals, it is critical to develop a new industrial microbial workhorse that improves the conversion of lignocellulosic carbon to biofuels and bioproducts in an economically feasible manner. Pseudomonas putida KT2440 is a promising microbial host due to its capability to grow on a broad range of carbon sources and its high tolerance to xenobiotics. In this study, we engineered P. putida KT2440 to produce isoprenoids, a vast category of compounds that provide routes to many petrochemical replacements. A heterologous mevalonate (MVA) pathway was engineered to produce potential biofuels isoprenol (C 5 ) and epi-isozizaene (C 15 ) for the first time in P. putida . We compared the difference between three different isoprenoid pathways in P. putida on isoprenol production and achieved 104 mg/L of isoprenol production in a batch flask experiment through optimization of the strain. As P. putida can natively consume isoprenol, we investigated how to prevent this self-consumption. We discovered that supplementing l -glutamate in the medium can effectively prevent isoprenol consumption in P. putida and metabolomics analysis showed an insufficient energy availability and an imbalanced redox status during isoprenol degradation. We also showed that the engineered P. putida strain can produce isoprenol using aromatic substrates such as p -coumarate as the sole carbon source, and this result demonstrates that P. putida is a valuable microbial chassis for isoprenoids to achieve sustainable biofuel production from lignocellulosic biomass. Graphical Abstract
First Solar Thermal Energy Planner (STEP 1) and Nationwide Industrial Heat and Power Analysis
The First Solar Thermal Energy Planner (STEP 1) and Nationwide Industrial Heat and Power Analysis (aka the STEP 1 Project or the Project) aimed to (1) developed a brand-new web tool that could provide decision support through free, rapid techno-economic analysis of behind-the-meter solar+storage systems for industrial process heat and (2) conduct high-level analyses of the cost-competitiveness of the same systems across the US in key sectors. The STEP 1 web tool collects key location, land availability, thermal load profile, proccess heat temperature and media, and other key parameters through an easy-to-use user interface (UI). The UI was designed to meet the user at their level of understanding by minimizing the number of required inputs as much as possible while including options for more nuanced inputs if the user desires. STEP 1 advises users on which solar thermal tehcnologies that could fit their needs based on the inputs provided (namely process media and temperature). The tool can model a wide range of solar thermal technologies including flat plate collectors, evacuated tubes, parabolic troughs, linear Fresnel, and molten salt towers all with corresponding thermal energy storage (TES) - solar PV with resistive heating and TES is also included. Once the parameters are collected, a nominal thermal energy production profile for the facility's location is generated using NREL's System Advisory Model (SAM) and then passed to a modified version of NREL's REopt platform to optimize the size (capacity) and dispatch of the solar+storage system to minimize lifecycle costs subject to energy balance, fuel and electricity rates, emission reductions goals, and other constraints. This entire process takes less than 20 minutes, is completely free, requires zero coding skills, and provides the user with a high-level assessment on the techno-economic feasability of deploying solar+storage systems for their energy needs. In addition to the development of the STEP 1 web tool, the project completed two complementary analyses focused on leveraging the backend code of STEP 1, public industrial facility locations and fuel consumption data, and key sector information to assess the economic opportunity of reducing fuel costs at various levels of capacity factor around the US. Both analyses found that there are key markets, locations, industrial sectors (namely food and beverage), and degrees of offset where solar thermal technologies could be cost-effectively deployed, highlighting a key market entry point for these technologies, and assessing deployment potential. In summary, the STEP 1 project improved the opportunities for solar+storage systems to expand into the industrial process heat market through breaking down barriers to assessing these technologies.
Radiological Recovery Logistics Tool - 20161
Argonne is building and testing a tool, the Radiological Recovery Logistics Tool (RRLT), that can be used during the response and recovery from a radiological or nuclear incident to effectively allocate appropriate commercial and public works equipment to mitigate, remove, and contain radiological contamination. The requirements for this tool - as well as development of the resulting software - is overseen by a steering committee of stakeholders from DHS's National Urban Security Technology Laboratory (NUSTL), the Federal Emergency Management Agency (FEMA), and the Environmental Protection Agency (EPA). One essential requirement is for RRLT to support the efficient and appropriate allocation of resources for a radiological response. Subsequent discussions between ANL and stakeholders have solidified the nature of this support to include identification of the types of resources to be allocated. The study reported in this paper has both factored fundamental concepts and connections out of this identification process and created a Knowledge Base detailing support goals, response scenarios, and efficacy information on dozens of equipment types. In short, RRLT will dynamically apply these findings to situational conditions surrounding contamination incidents. RRLT's Domain, the model of elements, ideas and relationships with which the tool will work, draws concepts from technical reports and stakeholder vocabularies to connect response goals and scenarios to types of equipment that offer utility towards those goals in those scenarios. RRLT's Knowledge Base will contain details on dozens of equipment types and facilitate the operator's discovery and consumption of these details most pertinent to a dynamically selected subset of goals. The core of its Domain Model is based on a report authored by this team. This report [1] contains a comprehensive list of proposed equipment to accomplish various missions or scenarios that might arise after a large-scale radiological contamination incident in an urban environment or critical infrastructure. The report divides potential response and recovery efforts into five support goals: Survey and monitoring of the contaminated area; Mitigation of received dose to first responders: Decontamination (gross and final) of buildings, vehicles, roadways, parks, and other surfaces: Waste management of solid waste generated during recovery operations: and Containment of wastewater and other waste generated during the response and recovery phases. RRLT's development is driven by use cases. A use case is an intention with which a user approaches the software. Use cases are grouped into delivery increments to schedule development, testing, and presentation to stakeholders. This model partitions the system into seven increments: User Arrival and Authentication, Search and Navigation, Equipment Recommendation, Plan Management, Content Management, and Expanded Access. Once a user 15 authenticated, RRLT will present the user with a dashboard that allows them to explore or search RRLT's content. The dashboard will also include a 'Plan' panel for collecting decisions and relevant observations about an incident at hand to facilitate development of an equipment list. RRLT will offer three general modes of access to items in the knowledge base: - Keyword search for direct discovery of items, - Navigation along predetermined paths from recovery goal towards equipment types, and - Interactive guidance towards equipment types by an autonomous software agent: the Equipment Recommendation Wizard. This presentation will detail progress in the development of the RRLT and also discuss opportunities for those interested in providing feedback on its content and functionality. (authors)
Adaptive Laboratory Evolution for Enhanced Performance of Cupriavidus Necator on Formic Acid
The threat of global warming, driven by rising carbon emissions, highlights the need to decarbonize our economy. This requires innovative solutions for managing carbon waste and its effective utilization. One promising method for CO2 capture and sequestration is the electrochemical reduction of CO2 to formic acid, a soluble C1 molecule that can be used to store carbon and energy, and as a feedstock for biological conversion. Cupriavidus necator H16, a soil bacterium capable of consuming and growing on formic acid as its sole carbon and energy source, is well positioned to upgrade CO2-derived formic acid into platform chemicals and fuel precursors. To improve the performance of C. necator on formic acid, adaptive laboratory evolution (ALE), a proven tool for improving microbial fitness, has been conducted using continuous pH-stat bioreactors. The system works on the basis that consumption of formic acid raises the pH and triggers the addition of more formic acid to maintain the pH (in this case 6.7), such that formic acid is provided at the same rate as it is consumed. This system has been coupled with level control to achieve continuous fermentation where cells acquiring mutations that improve growth on formic acid become more abundant in the population, from which they can be isolated and characterized. During developmental experiments it was discovered that formic acid accumulated to inhibitory levels. It was determined that the nitrogen source, ammonium hydroxide, must be tailored to the carbon consumption to avoid formic acid accumulation. The ALE ran in three lineages for approximately 3000 hours and more than 500 generations. Evolved isolates obtained from each lineage demonstrated an increase in growth rate in conjunction with improve formate utilization compared to the parental strain when evaluated in pH-stat bioreactors. The isolates with improved performance were then subjected to whole genome sequencing to identify potentially causative mutations. Mutations in several key genes across different lineages have been found and will be evaluated individually and in combination to identify those that improve growth on formic acid. Incorporating these mutations into production strains has the potential to greatly improve formic acid conversion and further industrial decarbonization.
Pressure-dependent kinetics of cyclopentene consumption
Recent experiments have uncovered deficiencies in the ability of current kinetic models for cyclopentane combustion to predict species profiles as a function of O2 concentration. One likely source of these discrepancies flows from the limited availability of relevant kinetic data for key functionalized intermediates, requiring the use of distant analogies to estimate their rates. This work fills this gap for cyclopentene, the most prominent C5 intermediate in low-temperature cyclopentane combustion. Ab initio transition-state-theory based master equation methods are used to calculate pressure-dependent rate constants across four potential energy surfaces (C5H8 + OH, C5H8 + HO2, C5H7, and C5H7 + O2). Potential energy surfaces are characterized at the CCSD(T)-F12/cc-pVDZ-F12//revDSD-PBEP86-D3BJ/def2-TZVPP level of theory. Pressure-dependent rate constants and branching fractions are determined via 1D master equation calculations. The results are used to analyze the competition for radicals between cyclopentene and cyclopentane in the intermediate temperature region, and to identify the dominant pathways flowing from these initiation channels. On the OH initiation surface, we find good agreement with recently published experimental rate constants at high temperature, and we elucidate the complex temperature dependence below 800 K, where 𝜋𝜋 addition becomes prominent. On the HO2 initiation surface, we find an interesting alternative route to bicyclic ether formation via well-skipping over the HO2 adduct well. On the cyclopentenyl radical surface, the major product is cyclopentadiene plus H. On the cyclopentenyl + O2 surface, we find that resonance stabilization largely prevents the allylic radical from undergoing oxidation, whereas the alkylic radical has a somewhat elevated propensity for oxidation pathways relative to cyclopentyl.
Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors
Demand flexibility (DF) has the potential to increase the saturation of renewables in the grid and reduce operating costs for both utilities and customers. However, less than 8% of U.S. residential electric customers are enrolled in DF programs. A major research gap on this topic is an uneven understanding of behavioral drivers of electricity use and DF program participation at the household level. In this study, we employ machine learning models to predict residential occupant behavior in activities relevant to DF. We model occupants' extensive decisions (i.e., choice of action) and intensive behaviors (i.e., amount of time spent) during peak and off-peak time periods using the publicly available American Time Use Survey, which includes activities data for approximately 200,000 respondents. In our machine learning models, predictions for both extensive and intensive behavior fell within a +/-20% error margin at the aggregate level. We identify 13 key sociodemographic predictors of DF-related intensive behavior using LASSO inference and beta coefficient ranking. However, these top predictors differ by activity, suggesting potential scope for differential user targeting for DF events and technologies during program design. This work also contributes to understanding when and who might adopt these DF technologies based on their daily routine activities.
Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States
Demand-response programs can help utilities manage rapidly evolving electric grids, but these programs are subject to the complexities of human behavior. This paper explores a novel method for uncovering heterogeneity in households. In this work, we use a machine-learning method known as a Conditional Inference Tree (c-tree) algorithm to categorize households based on their energy behavior characteristics collected via smart meters, and explore how this translates through into heterogeneity in their real-world response to a DR program. Using data from randomized controlled trial, we generate estimates of the changes in energy use caused by the program within each household group. Our results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods. Thus, the c-tree analysis enables the most tailored targeting of major potential energy savers and could provide the greatest increase in cost-effectiveness of household recruitment into DR programs. Our results also offer fresh insights into the relationships between household energy behavior characteristics – such as peak energy use and “structural winningness” (the ability to save money under a DR program without changing energy-use behaviors) – and household decisions about enrolling in DR programs and reducing energy use. Our research also demonstrates the potential of smart meter data, combined with machine learning and econometric methods, to provide significant value to utilities, program implementers, researchers, and other stakeholders.
Electric Vehicle Charging Data Falsification Attacks Utilizing Behavioral Models
A charging station (CS) and its associated electric vehicle supply equipment (EVSE) and charging electric vehicle (EV) interactions are potential targets for data falsification attacks since CSs are typically unmanned public facilities that are connected to the internet and EVs incorporate the vulnerable CAN bus network, which are both susceptible to remote attacks. The research question being addressed is how is the EV owner and CS negatively affected by CAN bus EV battery current sensor and battery temperature sensor data falsification attacks. Negative effects include economic losses from reduced life span of the battery, battery thermal runaway and fire (and potential loss of surrounding structure), and reduced utilization of the CS due to delayed departure time (longer charging times).
Optimization of Residential Air Source Heat Pump using Low–Global Warming Potential Refrigerants
We report low–global warming potential refrigerants can significantly reduce the direct emissions of CO 2 originating from HVAC systems. However, high-efficiency systems are needed to reduce indirect CO 2 emissions. In this study, an R410A residential 5-Ton heat pump was optimized using R32, R454A, R454B, R454C, and R455A. Among these options, R455A and R454C have the lowest global warming potential but have lower volumetric capacity and high glide. Optimization results using 5 mm tube heat exchangers showed 12.4% to 19.1% efficiency improvements and 13% to 33% reduction in CO 2 emissions.
Deploying a Model Predictive Traffic Signal Control Algorithm - A Field Deployment Experiment Case Study: Preprint
This paper presents a field deployment experiment of a real-time traffic signal control algorithm. We implemented the Model Predictive Control (MPC) algorithm based on Virtual Phase-Link (VPL) model. We selected the deployment locations and times based on an energy saving potential concept. We developed a set of experiment systems to enable the deployment including sensing, processing, and actuating components. We tested the systems rigorously before the experiment days. We reported the key procedures on the experiment days including the steps took, real-time control procedure, and the monitoring of the experiment. We evaluated the impact of the deployment with the changes in delay and energy consumption.