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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Tracking the PACE of household energy usage: Energy usage impacts of projects financed through Property Assessed Clean Energy programs in California

This report examines the household-level energy impacts of residential property assessed clean energy (R-PACE) projects using normalized metered energy consumption methods. Our analysis covers projects that occurred through an R-PACE program between 2009 and 2017 and includes more than 25,000 electricity meters and 15,000 gas meters. We employ a comparison group, drawn from other R-PACE households with similar locational and usage characteristics whose projects were implemented at different times, to control for some non-project and non-weather factors that may impact energy use. We find that projects consisting of energy efficiency technologies save, on average, about 3% of household electricity usage and 3.5% of household gas usage. R-PACE financing, however, can be used to install central heating or air conditioning equipment for the first time. These projects would be expected to increase energy consumption. Since our data do not directly indicate which projects are new installations, we develop a simple algorithm for identifying them. Removing these inferred installation projects yields average savings of about 5% for electricity and 6% for gas for those households that remain in the sample. Given the mild California climate and the results of another study of similar California projects using similar methods, these results are in line with expectations. Solar PV projects yield large reductions in grid electricity use, averaging 69% of household consumption. We estimate that, collectively, all R-PACE projects installed in California through 2019 would generate annual reductions in grid-tied electricity consumption of 506 GWh (mostly due to solar PV) and gas consumption reductions of 2 million therms in a normal weather year. These impacts are equivalent to the electricity consumption of about 74,000 California households (including both efficiency and PV generation) and the gas consumption of about 4700 California households.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Using Residential and Office Building Archetypes for Energy Efficiency Building Solutions in an Urban Scale: A China Case Study

Building energy consumption accounts for 36% of the overall energy end use worldwide and is growing rapidly as developing countries continue to urbanize. Understanding the energy use at urban scale will lay the foundation for identification of energy efficiency opportunities to be deployed at speed. China has almost half of global new constructions and plays an important role in building suitability. However, an open source national building energy consumption database is not available in China. To provide data support for building energy consumptions, this paper used a simulation method to develop an urban building energy consumption database for a pilot city in Wuhan, China. First, residential, small, and large office building archetype energy models were created in EnergyPlus to represent typical building energy consumption in Wuhan. The baseline reference model simulation results were further validated using survey data from the literature. Second, stochastic simulations were conducted to consider different design parameters and occupants’ energy usage intensity scenarios, such as thermal properties of the building envelope, lighting power density, equipment power density, HVAC (heating, ventilation and air conditioning) schedule, etc. A building energy consumption database was generated for typical building archetypes. Third, data-driven regression analysis was conducted to support quick building energy consumption prediction using key high- level building information inputs. Finally, a web-based urban energy platform and an interface were developed to support further third-party application development. The research is expected to provide fast energy efficiency building design solutions for urban planning, new constructions as well as building retrofits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Multi-objective Eco-Routing Model Development and Evaluation for Battery Electric Vehicles.

This paper develops a multi-objective eco-routing algorithm (combined eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Eco-routing is a technique that finds the most energy efficient route. ICEV and BEV energy consumption patterns are significantly different with regard to their sensitivity to driving cycles. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared to highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce the BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,” “moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared to the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Multiplex growth rate phenotyping of synthetic mutants in selection to engineer glucose and xylose co‐utilization in Escherichia coli

ABSTRACT Engineering the simultaneous consumption of glucose and xylose sugars is critical to enable the sustainable production of biofuels from lignocellulosic biomass. In most major industrial microorganisms glucose completely inhibits the uptake of xylose, limiting efficient sugar mixture conversion. In E . coli removal of the major glucose transporter PTS allows for glucose and xylose co‐consumption but only after prolonged adaptation, which is an effective process but hard to control and prone to co‐evolving undesired traits. Here we synthetically engineer mutants to target sugar co‐consumption properties; we subject a PTS − mutant to a short adaptive step and subsequently either delete or overexpress key genes previously suggested to affect sugar consumption. Screening the co‐consumption properties of these mutants individually is very laborious. We show we can evaluate sugar co‐consumption properties in parallel by culturing the mutants in selection and applying a novel approach that computes mutant growth rates in selection using chromosomal barcode counts obtained from Next‐Generation Sequencing. We validate this multiplex growth rate phenotyping approach with individual mutant pure cultures, identify new instances of mutants cross‐feeding on metabolic byproducts, and, importantly, find that the rates of glucose and xylose co‐consumption can be tuned by altering glucokinase expression in our PTS − background. Biotechnol. Bioeng. 2017;114: 885–893. © 2016 Wiley Periodicals, Inc.

Groot, Joost↗
DOE OSTI2023

An automated and portable method for selecting an optimal GPU frequency

Power consumption poses a significant challenge in current and emerging graphics processing unit (GPU) enabled high-performance computing systems. In modern GPUs, dynamic voltage frequency scaling (DVFS) appears to be a reliable control to regulate power consumption and performance. However, the DVFS design space is large - hence, brute-force approaches are infeasible to select the optimal frequency. Furthermore, no single frequency can be universally optimal for applications with varying computational intensities. Thus, the application's complexity and the availability of a wide range of frequency settings are a challenge in selecting the optimal frequency configuration for a given GPU workload. To that end, this paper proposes a systematic approach that consists of three steps. The feature characterization study identifies the fine-grain GPU utilization metrics that influence the power consumption and execution time of a given workload. To understand the performance, power, and energy consumption behaviors of a workload across GPU's DVFS design space, we derived analytical power and performance models using the identified fine-grain features. Here, it is shown that the same set of GPU utilization metrics can estimate both the power consumption and execution time while being agnostic of changes to frequency and input sizes. Applying a power control with the single objective of reducing power may cause performance degradation, leading to more energy consumption. A multi-objective approach is proposed to select the optimal GPU DVFS configuration for a workload that reduces power consumption with negligible degradation in performance. The evaluation was conducted using SPEC ACCEL benchmarks and three real applications - NAMD LAMMPS, and LSTM on NVIDIA GV100, GA100, and AMD MI210 GPUs. On average, real applications showed 29.6% energy savings with a performance loss of 5.2% on GA100 and 22.6% energy savings with a performance loss of 4.7% on GV100. Moreover, the proposed models are portable to real applications, GPU architectures, and vendors, and require metric collection at only the default frequency rather than all supported DVFS configurations. Additionally, we conducted a comparison between our models and the GPU assembly instructions (PTX)-based static models. The results revealed a significant reduction in the average error rates, with a decrease from 19.7% to 3.1% for power models and from 29.4% to 5.2% for performance models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Balancing Water Sustainability and Productivity Objectives in Microalgae Cultivation: Siting Open Ponds by Considering Seasonal Water-Stress Impact Using AWARE-US

Microalgae have great potential as an energy crop. Scaling-up algal biofuel production in the United States (US) should be done with careful attention to water stress. This study evaluates the regional and seasonal water-stress impact of potential algae-pond deployments in the US. Three site-selection strategies focusing on biomass yield, water-use efficiency (WUE), and water-stress impact, respectively, are applied and compared to meet a US algae biomass production target of 30 million metric tons/yr ash-free dry weight, which converts to 20.8 billion L renewable diesel, via hydrothermal liquefaction. Ranking algae ponds based on biomass yield leads to freshwater consumption of 2.66 km3/yr, resulting in the highest water-stress impact (39.1 US equivalent km3). Under the WUE scenario, water consumption is reduced by 81%, but biomass yield is reduced by 12%. In contrast, adding a water-stress constraint to the biomass-yield ranking reduces water consumption by 50% and water-stress impact by 97%, with a small yield reduction (1.7%). Results show that pond location has a significant effect on water stress and that water stress is not proportional to water consumption or yield. Furthermore, capturing seasonal water patterns is critical for planning because sites in water-abundant regions can have short-term but significant water-stress impacts.

algae, Biofuel, water scarcity footprint, hydrothe↗
DOE OSTI2019

An Automated Vehicle Fuel Economy Benefits Evaluation Framework Using Real-World Travel and Traffic Data

Increasing automation is a consistent development trend in the automobile industry. However, real-world evaluation of the operational and energy consumption differences between automated vehicles and comparable manually driven vehicles has been limited. This study helps fill the information gap by comparing the operation and fuel economy of vehicles in adaptive cruise control (ACC) and non-ACC modes based on large-scale field test data collected by Volvo Car Corporation (Volvo Cars) from vehicles traveling on the designated 'Drive Me' project road network in Gothenburg, Sweden. The test vehicles' travel data are classified by driving mode (ACC vs. non-ACC) and driving conditions, which refer to traffic speed and road grade in this study. The results from the data logging fleet are used to estimate the aggregate fuel consumption differences at the Drive Me road-network level for vehicles traveling in ACC vs. non-ACC mode based on appropriately weighting the total amount of travel that took place on the network under different driving conditions. At the ACC penetration levels observed in the field test data, vehicles tended to drive more smoothly in ACC mode than in non-ACC mode. The corresponding travel-weighted fuel consumption rate for vehicles in ACC mode was about 5%-7% lower than for vehicles in non-ACC mode when traveling at similar conditions. Sensitivity analyses impart confidence in this result, and in the future, the established evaluation framework could be used to objectively quantify potential on-road fuel consumption impacts from vehicles with even higher levels of automated driving capability.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

Cost-of-Service Segmentation of Energy Consumers

Uncertainty in consumption is a key challenge at energy utility companies, which are faced with balancing highly stochastic demand with increasingly volatile supply characterized by significant penetration rates of intermittent renewable sources. This paper proposes a methodology to quantify uncertainty in consumption that highlights the dependence of the cost-of-service with volatility in demand. We use a large and rich dataset of consumption time series to provide evidence that there is a substantial degree of high-level structure in the statistics of consumption across users which may be partially explained by certain characteristics of the users. To uncover this structure, we propose a new technique for extracting typical statistical signatures of consumption-energy demand distributions (EDDs)-that is based on clustering distributions using a fast, approximated algorithm. We next study the factors influencing the choice of consumption signature and identify certain types of appliances and behaviors related to appliance operation that are most predictive. Finally, we comment on how structure in consumption statistics may be used to target residential energy efficiency programs to achieve greatest impact in curtailing cost of service.

Albert, A↗
DOE OSTI2023

Low-Temperature Plasma Assisted Kinetics Study of Ethanol

Non-equilibrium plasma assisted kinetics of ethanol at 0.5 atm over a temperature range of 523-1203 K were studied at stoichiometric conditions by using a plasma flow reactor (PFR). The effects of plasma chemistry on fuel and its influence on neutral chemistry is decoupled with both experimental and modelling efforts. Enhanced reactivity was observed experimentally in both plasma-assisted oxidation and pyrolysis cases with fuel consumption beginning at temperatures as low as 523 K. Absolute fuel consumption was observed at temperatures nearly 200 K earlier than pure thermal reactions highlighting the effectiveness of non-equilibrium plasma to promote reactivity. Furthermore, modelling results illustrated the effect of electron impact reactions towards accelerating fuel consumption and promoting low temperature chemistry by generating excited state N2 and reactive O and OH radicals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Thermoelectric heating and cooling–integrated dishwasher with thermal energy storage

Household dishwashers must address several performance goals: maximize washing and drying performance, while minimizing cycle duration, energy consumption, and water consumption. This study develops and examines a novel thermoelectric heating and cooling (TEHC) system with thermal energy storage applied to a household dishwasher (DW), aiming to improve the energy and drying performance of a commercially-available dishwasher while maintaining its washing performance, water consumption, and cycle duration. Experimental testing conducted on the novel TEHC-DW system demonstrates an 8.7% reduction in total energy consumption, lowering the per-cycle usage to 0.952 kWh, and a 40% reduction in energy consumption for internal water heating. The novel TEHC-DW system also demonstrates better drying performance, shortening the drying time by 42% to reach the same remaining moisture content as a commercially-available system. In addition, a resistance-capacitance network model is developed that predicts total drying time, total energy consumption, and the highest temperature reached in the tub (53.4 °C). The model accuracy is validated with experimental data (within ±1 K), and the model functions as a design tool via a parametric study to evaluate the next-generation design and the effect of the number of thermoelectric modules and thermoelectric driving force (i.e., current). Overall, this study demonstrates the potential for TEHC technology to improve energy efficiency and drying performance in household dishwashers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Field test and simulation evaluation of variable refrigerant flow systems performance

Our study aims to compare the performance of a Variable Refrigerant Flow (VRF) system with a Roof Top Unit, (RTU) and a variable-air-volume (VAV) system through field tests and energy simulations. The field test was conducted in as similar conditions as possible between the two systems, such as the installation and operating environments of heating, the ventilation and air conditioning (HVAC) system, including internal heat gain and outdoor conditions, including buildings to compare the performance of the two systems accurately. A VRF system and RTU were installed at the test building located in Oak Ridge, Tennessee, in the USA. The same internal heat gain was generated at the same operating time of the two systems using lighting, electric heaters, and humidifiers inside the building. The HVAC system was alternately operated between cooling and heating operations to acquire energy performance data and to compare energy usage. Furthermore, an hourly building energy simulation model was developed with regard to the VRF system and RTU, and then the model was calibrated using actual measured data. Then, annual energy consumption of the two systems were compared and analyzed using the calibrated model. Moreover, additional analysis was conducted when the controlled discharge air temperature in the RTU was changed. The field test result showed that when energy consumptions of two systems were compared at the same outdoor conditions, using the weather-normalized model, the VRF system exhibited an energy reduction of approximately 17% during cooling operation and of approximately 74% during heating operations. A comparison on the annual energy consumption using simulations showed that the VRF system reduced energy consumption more than that of the RTU by 60%. Furthermore, when the discharge air temperature in the RTU was controlled according to the outdoor air temperature, energy consumption of the RTU was reduced by 6% in cooling operations and by 18% in heating operation. As a result, energy consumption of the VRF system was reduced by more than that of the RTU by 55% approximately.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Evaluation of thermostat location for multizone commercial building performance

In multi-zone buildings, it is often found that a single shared thermostat controls more than one conditioned zones. Although these shared zones are supposed to have similar thermal needs (e.g., cooling and heating load), in reality, they are not mainly due to different orientations, sizes of windows, occupancy, space types, etc. This can cause unnecessary energy waste or thermal discomfort for the occupants. How to quantify this impact in multizone buildings remains a research gap. Therefore, this study aims to evaluate the impact of different sensor (i.e., thermostat) locations for multizone commercial buildings through a comprehensive modeling study. Here, two different scenarios for the sensor locations were selected to evaluate the impact in terms of energy and thermal comfort. The scenario (1) is that one to five sensors distributed among the five zones, but the sensor readings from selected zones will be used for no-sensor zones, which is no-mean sensor scenario. The scenario (2) is that one to five sensors distributed among the five zones, but the average temperature from the shared zones will be used for each of the shared zones, which is a mean sensor scenario. The uncertainty analysis was performed for different sensor location scenarios.(a)The major findings from an energy perspective, for scenario (1), the differences of cooling energy go as high as 17% more or 12% less, compared with the baseline. For heating energy consumption, the discrepancies go as high as 51% more or 52% less, compared with baseline. For site energy consumption, the discrepancies go as high as 3.2% more or 3.2% less, compared with baseline. For fan energy consumption, the discrepancies go as high as 3.2% more or as low as 1.0% less, compared with baseline. For scenario (2), the discrepancies of cooling energy go as high as 3% more, or 0.5% less, compared with the baseline. For heating energy consumption, the discrepancies, are go as high as 10.1% more or as low as 3.0% less, compared with baseline. For site energy consumption, the discrepancies go as high as 1.3% more or 0.3% less, compared with baseline. For fan energy consumption, the discrepancies go as high as 3.1% more or as low as 1.0% less, compared with baseline.(b) In terms of the indoor thermal comfort, for the no-mean-sensor scenarios, the discrepancies of unmet hours for cooling mode can be as high as 1,200 h, compared with the baseline. The discrepancies of unmet hours for heating mode can be as high as 740 h, compared with the baseline. For the mean-sensor scenarios, the discrepancies of unmet hours for cooling mode can be as high as 750 h, compared with the baseline. The discrepancies of unmet hours for heating mode can be as high as 50 h, compared with the baseline.

42 ENGINEERING↗
DOE OSTI2020

Socioeconomic Effects of Pension Spending: Evidence from Spain

The purpose of this study is to provide broader understanding of the significant role that the pension system has in the Spanish economy by estimating the sectoral production, employment and income sustained by pensioners' consumption. Based on input-output tables by the World Input-Output Database and consumption data from the Household Budget Survey by the Spanish Statistical Office, a demoeconomic model is applied to quantify the direct impacts, indirect impacts from interindustry links and induced impacts from income-consumption connections over a nine-year period (2006–2014). Then, the factors driving the evolution of total output, employment and value added during such period have been examined by using structural decomposition analysis. The growing participation of consumption by pensioner households in final demand had proven crucial during the 2008 crisis to alleviate the negative trend in production and employment derived from the collapse in consumption suffered by the rest of households. Determining the underlying factors driving changes in both employment and income during the 2008 crisis can be of interest in political decision-making on the sustainability of the Spanish pension system. The results of estimating both the employment and income supported by pensioners' consumption reveal the significant stabilizing effect of the public spending on pensions, particularly during the 2008 crisis. The current Spanish approach of attaining the pension system sustainability by merely reducing social protection costs ignores the adverse consequences of a lower pensioners' demand. This paper addresses an alternative view in which pension spending is not considered a burden on economic growth but rather a means of improving the level of production and employment.

employment↗
DOE OSTI2015

Cost-Optimal Pathways to 75% Fuel Reduction in Remote Alaskan Villages: Preprint

There are thousands of isolated, diesel-powered microgrids that deliver energy to remote communities around the world at very high energy costs. The Remote Communities Renewable Energy program aims to help these communities reduce their fuel consumption and lower their energy costs through the use of high penetration renewable energy. As part of this program, the REopt modeling platform for energy system integration and optimization was used to analyze cost-optimal pathways toward achieving a combined 75% reduction in diesel fuel and fuel oil consumption in a select Alaskan village. In addition to the existing diesel generator and fuel oil heating technologies, the model was able to select from among wind, battery storage, and dispatchable electric heaters to meet the electrical and thermal loads. The model results indicate that while 75% fuel reduction appears to be technically feasible it may not be economically viable at this time. When the fuel reduction target was relaxed, the results indicate that by installing high-penetration renewable energy, the community could lower their energy costs by 21% while still reducing their fuel consumption by 54%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Whole-Home Dehumidifiers: Field-Monitoring Study

Lawrence Berkeley National Laboratory (LBNL) initiated a WHD field-metering study to expand current knowledge of and obtain data on WHD operation and energy consumption in real-world applications. The field study collected real-time data on WHD energy consumption, along with information regarding housing characteristics, consumer behavior, and various outdoor conditions expected to affect WHD performance and efficiency. Although the metering study collected similar data regarding air conditioner operation, this report discusses only WHDs. The primary objectives of the LBNL field-metering study are to (1) expand knowledge of the configurations, energy consumption profiles, consumer patterns of use (e.g., relative humidity [RH] settings), and environmental parameters of whole-home dehumidification systems; and (2) develop distributions of hours of dehumidifier operation in four operating modes: off, standby, fan-only, and compressor (also called dehumidification mode). Profiling energy consumption entails documenting the power consumption, duration of power consumption in different modes, condensate generation, and properties of output air of an installed system under field conditions of varying inlet air temperature and RH, as well as system configuration. This profiling provides a more detailed and deeper understanding of WHD operation and its complexities. This report describes LBNL’s whole-home dehumidification field-metering study conducted at four homes in Wisconsin and Florida. The initial phase of the WHD field-metering study was conducted on one home in Madison, Wisconsin, from June to December of 2013. During a second phase, three Florida homes were metered from June to October of 2014. This report presents and examines data from the Wisconsin site and from the three Florida sites.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Using Field-Metered Data to Quantify Annual Energy Use of Portable Air Conditioners

As many regions of the United States experience rising temperatures, consumers have come to rely increasingly on cooling appliances (including portable air conditioners) to provide a comfortable indoor temperature. Home occupants sometimes use a portable air conditioner (PAC) to maintain a desired indoor temperature in a single room or enclosed space. Although PACs in residential use are few compared to centrally installed and room air conditioning (AC) units, the past few years have witnessed an increase of PACs use throughout the United States. There is, however, little information and few research projects focused on the energy consumption and performance of PACs, particularly studies that collect information from field applications of PACs. The operation and energy consumption of PACs may differ among geographic locations and households, because of variations in cooling load, frequency, duration of use, and other user-selected settings. In addition, the performance of building envelope (thermal mass and air leakage) as well as inter-zonal mixing within the building would substantially influence the ability to control and maintain desirable indoor thermal conditions. Lawrence Berkeley National Laboratory (LBNL) conducted an initial field-metering study aimed at increasing the knowledge and data related to PAC operation and energy consumption in the United States. LBNL performed its field-metering study from mid-April to late October 2014. The study, which monitored 19 sites in the Northeastern United States (4 in upstate New York and 15 near Philadelphia), collected real-time data on PAC energy consumption along with information regarding housing characteristics, consumer behavior, and environmental conditions that were expected to affect PAC performance. Given the limited number of test sites, this study was not intended to be statistically representative of PAC users in the United States but rather to understand the system response to the cooling demand and to some extent, the operating hours of the studied units. Specifically, the primary objectives of the field-metering study were to (1) expand knowledge of the installation, energy consumption profiles, consumer patterns of use, and environmental parameters related to PAC use; (2) develop distributions of hours of PAC operation for three operating modes: standby, 1 fan-only, and cooling; and (3) describe how individual consumers’ selection of PAC capacity, the area of the space to be cooled, the temperature set point, and environmental conditions affect energy use. Beginning to understand the energy consumption of PACs operating in American homes and commercial settings will help develop a more accurate energy use profile that characterizes relevant variables. This report on LBNL’s field-metering study of PAC energy use describes: a general definition of a PAC and how it operates (section 2); current practices and sources of data for estimating PAC energy use (section 3); the process LBNL used to select field-metering sites, along with characteristics of the sites and the PACs studied (section 4); data collection methods and instrumentation (section 5); analysis methods (section 6); results and discussion (section 7); and conclusions (section 8).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Low-Cost Multi-Modal Wireless Sensor Platform for Smart Buildings

Buildings consume 73% of the energy produced in the United States [1, 2]. Advanced sensors, controls, and communications have the potential to reduce the energy consumption of buildings by 20–40% [3, 4]. Currently, installation and wiring costs for sensors are quite high, making it cost-prohibitive for building managers to deploy large quantities of advanced sensors [5]. Wireless sensors have recently been deployed in buildings to provide the information necessary for optimal control of heating, ventilation, and air-conditioning and lighting systems [6, 7]. Wireless sensors have the unique advantage of being suitable for easily retrofitting existing buildings at a minimal labor cost, and the flexibility to be placed at optimal, observable locations. However, current commercially available wireless sensors are still very expensive ($150–300/node). Thus, revolutionary technological improvements in wireless sensors are required to promote inherently low-cost manufacturing for building applications to successfully exploit the energy efficiency opportunities in buildings.In this report, we present the efforts carried out at Oak Ridge National Laboratory and Molex Inc. toward exploring the design, implementation, and performance evaluation of low-cost, self-powered wireless sensors—including temperature, relative humidity, occupancy, and indoor air quality sensors—to enable advanced control applications that reduce energy consumption in buildings. Our focus is to demonstrate a novel multifunctional sensor platform that will evolve with advances in materials technology, low-cost printing techniques, nano-antennas, and co-integration of monitoring, control, and communication circuitry. We also present a low-power and bandwidth-efficient wireless communication technology that can be driven by energy harvesting in self-powered wireless sensors. It is based on code-phase-shift keying (CPSK) spread-spectrum signaling that improves both transmitter range and power consumption. The objective is to minimize the number of components within the wireless transceiver and reduce power consumption to allow sensor networks powered by energy harvesting, thus eliminating the expense and effort of periodically replacing the sensor batteries. Current state-of-the-art radio technologies have demonstrated 25–30 mA current consumption for the transmitter. Our prototype demonstrations lowered the consumption to 4 mA using spread-spectrum-based CPSK so that solar cells can be used with indoor lighting to recharge a battery indefinitely. Therefore, the system can run with no attention or maintenance for the life of the components, which is expected to be 20 years. The developed technology has the potential to generate fully printable wireless sensors with costs on the order of $1–10/node.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Energy Intensity Baselining and Tracking Guidance

Each company joining the U.S. Department of Energy’s (DOE’s) Better Buildings, Better Plants Program (Better Plants) commits to establishing an energy consumption and energy intensity (EI) baseline and to tracking its energy performance over a 10-year period against that baseline. The baseline must reflect a company’s energy consumption over a 12-month period, covering all its U.S.-based operations. Energy consumption is calculated by fuel type in terms of primary energy (also known as source energy). EI is broadly defined as the amount of energy consumed per unit of output produced. For this guidance document and for the program, the term energy performance represents an evaluation of a facility’s capacity to use energy efficiently. Metrics used to assess a facility’s energy performance can include EI, energy consumption, improvements in EI, etc. Establishing an energy baseline and tracking system is a critical first step in effectively managing energy use. Developing a baseline can help a company understand energy use within the corporation and give it a point of comparison to evaluate future efforts to improve energy performance. It can also support efforts to validate a company’s energy management activities, improve comparative analyses when using benchmarks, and help in predicting future energy needs. In addition, a company that normalizes its performance data can determine highly defensible measures of energy savings generated through implemented energy efficiency projects. Establishing a baseline and tracking energy performance is also a requirement for ISO 50001 certification. Although basic energy data can be collected through utility bills, most manufacturers will have to perform additional analyses to develop accurate and robust energy baselines and tracking systems. Energy is consumed in many ways within the manufacturing sector and can come from multiple sources. Energy is sometimes generated and sold to other parties or captured and reused on-site. External events can exert a significant impact on a facility or company’s energy use independent of any purposeful efforts to improve energy efficiency. Operational changes, such as production shifts—which may be inevitable for some companies over the 10-year period covered by the program—can also make a big difference in energy use. Since Better Plants asks companies to account for all their U.S.-based operations, mergers, acquisitions, and divestitures can also have significant implications for a company’s energy metrics. This document aims to demystify the sometimes complex baselining process. It devotes special attention to the task of normalizing and adjusting energy consumption to account for external factors, such as weather and production changes. A key recommendation is that companies use regression analysis to normalize their energy consumption data whenever possible. Regression analysis is a statistical technique that estimates the dependence of a variable (i.e., energy use in the context of Better Plants) on one or more independent variables such as ambient temperature, while controlling for the influence of other variables at the same time. A properly developed regression analysis can provide a reliable estimate of energy savings resulting from energy improvement strategies and projects by accounting for the effects of variables such as annual production levels and weather. DOE has developed a companion Energy Performance Indicator software tool (EnPI) to simplify the baselining process. This tool can run regression models, calculate changes in EI at the facility level, and automatically compile facility-level data into a corporate-wide metric. Note that although the relevant equations used to calculate EI are provided in this document, the EnPI tool will automatically perform most calculations for the user. Additionally, Better Plants Partners (Partners) can call on their Technical Account Manager (TAM) to help them establish a baseline and assist with the necessary calculations to track progress.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗