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This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon supermarkets within their existing building portfolios. Supermarkets include built-up refrigeration systems and refrigerated display cases and often use packaged rooftop units for heating, cooling, and ventilation.
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon midrise apartment buildings within their existing building portfolios. Midrise apartments may use packaged rooftop units for heating, cooling, and ventilation or central plant systems for larger buildings.
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon small office buildings within their existing building portfolios. Small offices are typically less than 50,000 square feet and often use packaged rooftop units for heating, cooling, and ventilation.
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon outpatient healthcare buildings within their existing building portfolios. Outpatient healthcare includes diagnostic and treatment facilities for outpatient care, and these buildings often use packaged rooftop units for heating, cooling, and ventilation.
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon small hotels within their existing building portfolios. Small hotels are typically four floors or less and less than 50,000 square feet and often use packaged rooftop units for heating, cooling, and ventilation.
The National Renewable Energy Laboratory's (NREL's) Industrialized Construction Innovation team first introduced the Industrialized Construction Assessment Framework to achieve affordable, net-zero energy (NZE) modular multifamily buildings in the 2020 ACEEE paper "Integrating Energy Efficiency Strategies with Industrialized Construction for our Clean Energy Future." Since then, NREL has continued to drive the ambitious plan to accelerate optimal integration of energy efficiency strategies during industrialized construction with little or no additional cost, labor, and production time. This follow-on paper introduces the Energy in Modular (EMOD) buildings method and presents NREL's research efforts over the last two years in collaboration with industry, including affordable housing partners. NREL has developed an idealized NZE modular multifamily building design that incorporates five energy efficiency strategies well suited for industrialized construction in factories: (1) envelope thermal control, (2) envelope infiltration control, (3) mechanical, electrical, and plumbing systems, (4) smart controls, and (5) solar plus storage. This paper highlights results from leveraging design for manufacturing and assembly principles, testing, and validation pilots with factory partners; demonstrating pod prototypes in test stand at NREL; and performing simulations. Overall, these research efforts address barriers to whole-building system integration, such as poor installation quality of thermal and air barriers; lack of unitized systems for space conditioning, energy recovery and ventilation, and water heating; problematic on-site installation, commissioning, and configuration of controls; and lack of cost-effective integration for grid-friendly design and emerging technologies. Conclusively, the paper delineates next steps for future work with NREL's partners toward developing a transformational pathway for our clean energy future.
Peak power demand strains electrical grids and increases cost of electricity generation, transmission and distribution infrastructure. Many studies have examined ways of reducing peak power demand, including modification of room air temperature setpoints or reduction of lighting levels. However, very few or no studies have examined the peak power reduction resource offered by temporary curtailment of building ventilation systems. For this reason, we conducted a simulation campaign in which we examined the resource offered by temporary ventilation curtailment in commercial buildings of different use types across the United States and in residences in the state of California, with the essential constraint that any changes resulted in air quality acceptable to occupants through additional ventilation to compensate for the curtailment. To do this, we employed previously validated building models implemented in the airflow and contaminant transport tool CONTAM and building thermal and systems modeling tool EnergyPlus, in some cases co-simulated. Overall, results show savings are highly dependent on building type and climate but range from 0–2 W/ft 2 and up to 40% of total peak building power demand. Depending on building type, this power shed can be conducted for 1.5–8 h before acute exposure or odor concerns are expected, assuming a safety factor of 2. This reduction is of the same magnitude as that offered by thermal control strategies such as setpoint increase, or from lighting reduction strategies.
Occupancy-aware heating, ventilation, and air conditioning (HVAC) control offers the opportunity to reduce energy use without sacrificing thermal comfort. Residential HVAC systems often use manually-adjusted or constant setpoint temperatures, which heat and cool the house regardless of whether it is needed. By incorporating occupancy-awareness into HVAC control, heating and cooling can be used for only those time periods it is needed. Yet, bringing this technology to fruition is dependent on accurately predicting occupancy. Non-probabilistic prediction models offer an opportunity to use collected occupancy data to predict future occupancy profiles. Smart devices, such as a connected thermostat, which already include occupancy sensors, can be used to provide a continually growing collection of data that can then be harnessed for short-term occupancy prediction by compiling and creating a binary occupancy prediction. Real occupancy data from six homes located in Colorado is analyzed and investigated using this occupancy prediction model. Results show that non-probabilistic occupancy models in combination with occupancy sensors can be combined to provide a hybrid HVAC control with savings on average of 5.0% and without degradation of thermal comfort. Model predictive control provides further opportunities, with the ability to adjust the relative importance between thermal comfort and energy savings to achieve savings between 1% and 13.3% depending on the relative weighting between thermal comfort and energy savings. In all cases, occupancy prediction allows the opportunity for a more intelligent and optimized strategy to residential HVAC control.
Oak Ridge National Laboratory researchers compare two different approaches to test how advanced, energy-efficient building technologies such as smart thermostats, heat pump water heaters, and advanced heat pump HVAC (heating, ventilation and air conditioning) can be optimized within a home and connected at a neighborhood-scale load to a community microgrid in the Alabama Power Smart Neighborhood located in Hoover. Working with Southern Company and Alabama Power, ORNL researchers are pioneering that future where smart homes and smart neighborhoods can benefit both homeowners and utilities, by reducing energy consumption by 44% and peak demand by 34%.This project is one of two neighborhoods in the U.S Department of Energy’s (DOE’s) Connected Neighborhood project, supported by Building Technologies Office , where ORNL researchers leverages DOE investment in micro-grids and responsive, flexible building loads research to improve grid reliability – a goal of DOE’s Grid-interactive Efficient Buildings (GEB) Initiative. Researchers control the neighborhood and microgrid to enable utilities achieve their desired load and cost profiles while ensuring the comfort of homeowners in the Smart Neighborhood. This transactive control approach maximizes the utilization of technical resources of the microgrid and controllable loads, while reducing costs for both the homeowners and Alabama Power. These tests partially seek to determine a more precise range of tolerance with respect to occupant comfort as researchers work to facilitate customer acceptance and perception of new building technologies that enable energy savings.
The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.
The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.
The use of residential heating, ventilation, and air conditioning (HVAC) to shift peak demand or provide ancillary services is a potential solution in the presence of older grids and distributed renewables. However, to ensure the efficient use of devices, utilities need to accurately forecast the load and adopt error correction schemes when necessary. While significant theoretical research exists in the area of predictive control of HVAC, little experimental evidence exists. The lack of experimental data in turn causes researchers to be unprepared for unsystematic errors which emerge due to the higher complexity of the data generating process. This study offers an anomaly detection methodology that uses unsupervised machine learning algorithms to detect and isolate these errors with different forecast error ranges. The results of anomaly detection procedure can then be used for error correction and would eventually help develop better predictive controllers. The methodology is tested using real world data from a smart neighborhood that currently operates in Atlanta. GA.
Plug and process loads (PPLs) account for a large portion of U.S. commercial building energy use. There is a huge potential to reduce whole building consumption by targeting PPLs for energy savings measures or implementing some form of plug load management (PLM). Despite this potential, there has yet to be a widely adopted commercial PLM technology. This paper describes the Automatic Type and Location Identification System (ATLIS), a PLM system framework with automatic and dynamic load detection (ADLD). ADLD gives PLM systems the ability to automatically identify devices as they are plugged into the outlets of a building. The ATLIS framework takes advantage of smart, connected devices to identify device locations in a building, meter and control their power, and communicate this information to a central database. ATLIS includes five primary capabilities: location identification, communication, control, energy metering, and data storage. A laboratory proof of concept (PoC) demonstrated all but the energy metering capability, and these capabilities were validated using a series of system tests. The PoC was able to identify when a device was plugged into an outlet and the location of the device in the building. When a device was moved, the PoC's dashboard and database were automatically updated with the new location. The PoC implemented controls to devices from the system dashboard so that devices maintained correct schedules regardless of where they were plugged in within the building. ATLIS's primary technology application is improved PLM, but other applications include asset management, energy audits, and interoperability for grid-interactive efficient buildings. An ATLIS-based system could also be used to direct power to critical devices, such as ventilators, during a brownout or blackout. Such a framework is an opportunity to make PLM more widespread and reduce the amount of energy consumed by PPLs in current and future commercial buildings.
The growth of and reliance on renewable energy necessitate a multi-pronged approach to achieve grid reliability and economics. As they represent a notable portion of U.S. energy consumption, commercial buildings must play an active role in this effort. Conserving energy and responding to grid conditions through demand flexibility can be achieved through the integration of major building systems. Integration of plug and process loads with lighting and heating, ventilation, and air conditioning systems maximizes the effectiveness of integrated building energy management. In this research, we demonstrate the integration of smart outlets into a building automation system. We cover the installation process as well as the architecture required for smart outlets to communicate data to the building automation system and to receive commands back. After recording power measurements for one week as a baseline, we configured the building automation system to turn the smart outlets on and off according to a set schedule. This resulted in energy savings of 66% during 1 week on 25 plug loads. This work demonstrates that grid-interactive efficient buildings are achievable through building system integration.
A fault detection and diagnostics (FDD) tool, as addressed by this study, is a tool that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Although FDD tools can inform operators of building operational faults, currently an action is always required to correct the faults to generate energy savings. Fault auto-correction integrating with commercial FDD technology offerings can close the loop between the passive diagnostics and active control, increase the savings generated by FDD tools, and reduce the reliance on human intervention. This paper presents the field study of seven fault auto-correction algorithms implemented in commercial FDD platforms. Implementation includes software changes in the FDD tools and additional controls hardware or software changes in the BAS that were required to enable the execution of different types of auto-correction algorithms in real buildings. The routines successfully and automatically correct faults and improve the operation of large built-up Heating, Ventilation, and Air Conditioning (HVAC) systems, common in most commercial buildings. The auto-correction algorithms are tested across four buildings and three different building automation systems, following a rigorous procedure to make sure they work properly and do not negatively impact the system and building occupants. Finally, technology benefits, market drivers, and scalability changes are drawn from the implementation effort and test results, to drive future research and industry engagement.
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.
Intelligent Heating, Ventilation, and Air Conditioning (HVAC) control using deep reinforcement learning (DRL) has recently gained a lot of attention due to its ability to optimally control the complex behavior of the HVAC system. However, more exploration is needed on understanding the adaptability challenges that the DRL agent could face during the deployment phase. Using online learning for such applications is not realistic due to the long learning period and likely poor comfort control during the learning process. Alternatively, DRL can be pre-trained using a building model prior to deployment. However, developing an accurate building model for every house and deploying a pre-trained DRL model for HVAC control would not be cost-effective. In this study, we focus on evaluating the ability of DRL-based HVAC control to provide cost savings when pre-trained on one building model and deployed on different house models with varying user comforts. We observed around 30% of cost reduction by pre-trained model over baseline when validated in a simulation environment and achieved up to 21% cost reduction when deployed in the real house. This finding provides experimental evidence that the pre-trained DRL has the potential to adapt to different house environments and comfort settings.
Occupants are the core of the built environment. Traditional Heating, Ventilation, and Air-Conditioning (HVAC) systems operate with predefined schedules and maximum occupancy assumptions with no consideration of specific occupant information. These generalized assumptions usually do not align with the actual demand and result in over-conditioning and occupant discomfort. In recent years, with the aid of Information & Communication Technology (ICT) and Computer Science (CS), it is possible to acquire real-time and accurate occupant information to satisfy the exact thermal requirement through specific HVAC control in one particular built environment. This mechanism is called HVAC “Occupant-centric Control (OCC).” HVAC OCC strategy starts with collecting the occupant’s information (e.g., presence/absence) and then applies it to meet the occupant’s requirement (e.g., thermal comfort). However, even though some research studies and field pilot demonstrations have been devoted to the field of OCC, there is a lack of systematic knowledge about occupant data, which is the principal component of OCC for HVAC researchers and practitioners. To fill this gap, this review paper discusses OCC with a particular emphasis on occupant information and investigates how this information can assist HVAC operation in providing an acceptable built environment in required spaces during the required time. Finally, we provide a fine-grained, comprehensive picture of occupant information, discuss its features, the modalities of information feed-in into the HVAC control, and the application of commonly utilized occupant information for OCC.