Energy consumption by end-use appliances and equipment is a function of the following: the efficiency of the device itself (addressed by the U.S. Department of Energy’s [DOE’s] Appliance and Equipment Standards Program); the overall system of which it is a part (e.g., a building’s thermal integrity with heating and cooling devices); and the choices consumers make in controlling the appliance. The chapter will discuss how the second and third elements may become more important if certain trends continue in technology, regulation, and business models. Among these changes, the most potentially relevant for DOE’s Appliance and Equipment Standards Program are advances in Information and Communications Technology (ICT) enabling the Internet of Things (IoT); the evolution of the Integrated Grid accompanying increased use of distributed resources; electrification and fuel switching more generally; and regulatory changes involving electricity markets. There are other changes in the U.S. electric power system under way that have a less direct impact on the Appliance and Equipment Standards Program such as a decrease in coal-powered generation, a decrease in nuclear power generation, and an increase in using central station, utility-scale wind and solar power generation. The key question for DOE is how these trends and the changing future intersect with the standards program.
While the Appliance and Equipment Standards Program addresses energy and water consumption, the Program might find an ideal target in economizing value-weighted or price-weighted energy resources in the overall economy. This approach deviates from DOE’s traditional target in several ways. Until recently, it was impractical to measure the “first-best” target precisely. This chapter discusses several reasons the ideal target is now closer in reach than ever, why these changes could help define the program in the future, and outlines steps to help the program incrementally move in that direction within statutory limits.
The first trend of relevance to the Program relates to the rapid growth in the use of sensors, electronic communication, and computational ability, coupled with an explosion of digitally-based entertainment and other data streams can transform the role of appliance and equipment standards in achieving energy and water conservation. Any appliance or electric energy consuming device can, in principle, be “connected” through electric or communications infrastructure or both. Nearly everything electric, biological and mechanical, including lighting systems, parking meters, refrigerators, fork-lift trucks, and running shoes, is can be connected to the Internet. This IoT that is now made possible affects: appliances, their installation, controls, and the built environment. In this way the IoT, if it becomes established, might imply modifications for DOE’s standards program.
Size, Scope, and Scale
The size, scope, and scale of the future IoT has been projected by the Organisation for Economic Cooperation and Development (OECD) to include the next 50 billion machines and devices that will go online in the coming two decades. OECD projects the number of connected devices in households in OECD countries to be 14 billion by 2022—up from around 1.4 billion in 2012, or from 10 connected devices in a household with two teenagers to 50 in 10 years.1 Approximately 46% of the global population is connected to the Internet today, up from 1% in 1995.2 One example of the proliferation of internet-connected end-use devices is the adoption of electric vehicles, which exceeded one million globally in 2016.3
The use of sensors is enabling of an oft-cited trend known as big data. This is discussed later in the chapter in the section, “Further Sources of Information.”
IoT is Modernizing the Grid
The IoT can foreseeably have an impact on energy end-use. This is mediated through use of smart meters, smart homes, and smart buildings, providing increased visibility into real-time electricity demand. Utilities, consumers, and electricity suppliers can use the information from the IoT to match generation resources accordingly. Vice versa, smart meters and mobile apps will provide consumers with real-time pricing of electricity. Through the latter, consumers can match their use of electricity to reduce their cost, the effect of which is to reduce peak demand and level out the consumption profile, providing both more efficient generation and use of electricity.
The extensive networks of sensors, communications, and computational ability that could be envisaged include the following:
- Smart meters. U.S. Energy Information Administration (EIA) data shows that in 2019 there were 95 million smart meter installations in the United States.4 EIA data includes meters that measure and record electricity use at a minimum of hourly intervals and provide that data to both the relevant utility and its customers daily.5 Smart meters may offer a pathway for real-time data on end-use of electrical appliances and equipment.
- Smart power system equipment. Most equipment installed on the power system is increasingly smart because it is computer- or microprocessor-based, including controllers, remote terminal units (RTUs), and intelligent electronic devices (IEDs). It affects the actual power equipment, such as switches, capacitor banks, or breakers and the equipment inside homes, buildings, and industrial facilities. This embedded computing equipment must be robust to handle future applications for many years without being replaced.
- Blockchain. The public ledgers maintained on the web known as blockchain have applications to distributed energy resources and energy trading platforms (EFI, 2019b). Blockchain is software in a digital record-keeping system that can be used for many things in the electric power system from tracking transactions, like renewable energy certificates, to data about devices as they travel
1 Organisation for Economic Co-operation and Development (OECD), December 11, 2014, “OECD Technology Foresight Forum 2014—The Internet of Things,” https://www.oecd.org/sti/ieconomy/technologyforesight-forum-2014.htm.
- through the supply chain. The idea of blockchain is that digital blocks are “chained” together. For example, blockchain can facilitate payments at an electric vehicle charging station by showing drivers real-time prices of nearby charging stations.
- Electronic communication systems. Communication systems refer to the media (e.g., TV, radio, Internet, etc.), the channel (physical route over which the signal is transmitted), and to developing communication protocols that encode the information into the signal (e.g., the digital format of the data). The digital format issues are in various stages of development. The appliances and equipment, including the power system, must be robust enough to accommodate new media as they emerge from the communications industries while preserving interoperable, secured systems.
- Cybersecurity. Cybersecurity addresses preventing damage to, unauthorized use of, exploitation, and, if needed, restoration of electronic information and communications systems and services (and the information contained therein). This ensures confidentiality, integrity, and availability and applies to end-use appliances and devices and any standards imposed on them.
- Data privacy. Tomorrow’s grid, represented by a widely interconnected system of systems, presents significant concerns about privacy protection and stewardship. Non-intrusive load monitoring may be possible (Paris et al., 2018). Care must be taken by DOE and other stakeholders to ensure that access to information is not an all-or-nothing-at-all choice since various stakeholders will have differing rights to information from the grid.
FINDING: In the modernizing electricity grid, energy-consuming devices will increasingly have sensors and controls that are demand-responsive. Likewise, distributed energy resources such as small-scale renewable generation or energy storage devices can provide real-time information to (and in turn be controlled by) the independent system operators (ISOs) who operate the electricity grid in deregulated markets. This additional embedded equipment will have an indirect role in delivering the energy service currently ascribed solely to the end-use appliance.
Increasing use of Distributed Energy Resources
A further trend that may affect the Appliance and Equipment Standards Program is the change in the electric power generation and delivery. The electric power system has evolved into a topology of large, central power plants interconnected via grids of transmission lines and distribution networks that feed customers’ power. The system is changing—rapidly in some areas—with the rise of distributed energy resources (DERs). These DERs include distributed photovoltaic (PV) power generation, small-scale wind power generation, small hydroelectric power generation; small natural gas-fueled generators, combined heat and power plants (CHPs), and energy storage such as batteries and flywheels. Energy efficiency programs and technologies have often been valuable cost-effective alternative to the other resources.6 In this construct, demand response programs and installations are a further type of DER.
In many places DER have already affected the operation of the electric power grid. For example, in 2016, the California Independent System Operator (CAISO) stated that there were 4,900 megawatts (MW) of DER in its Balancing Authority.7 Through a combination of technological improvements, policy incentives, and consumer choices in technology and service, DER’s role is likely to become more important. Regarding the relationship between DER and energy efficiency standards, DER effectiveness depends on how devices respond to both automatic and manual control. This includes simple controls which energize and de-energize or modulate performance based on the desired output of end use devices
that deliver energy services such as space conditioning (i.e., control of temperature and humidity), motor torque for various purposes (e.g., ventilation), and hygienic services (hot water).
Implications for Appliance and Equipment Standards
The applicability of appliance and equipment standards, currently in effect at the point of energy end-use, may become outdated due to the emergence of integrated grids. In the integrated grid concept of operations, appliances and equipment are flexible and participate in optimizing the value of a combination of energy efficiency and local generation8 and energy storage integrated with central generation and storage. In this new integrated grid, energy efficiency includes both appliances and the buildings or systems that house them as distributed resources. Increasingly these new resources are monitored and sometimes controlled as part of the grid. These resources also work in tandem with the grid, as small independent electrical systems cannot always easily support the starting current of certain appliances and devices like motors without operating in parallel with the grid.
From a total welfare perspective, consumers’ needs can often best be met through deploying energy efficient resources; it is often lower in cost than supplying more electricity from any conventional sources (i.e., electricity cost for generation, transmission, and distribution supplied by coal, oil, natural gas, or nuclear power generation) and has minimal environmental costs. Distributed generation is being installed at a cost that is competitive with the abatement cost of decreasing electricity consumption.
FINDING: Lower energy costs brought about by increased use of renewable energy generation may lessen the value of energy efficiency depending on the daily pattern of use the coincident time of renewable generation.
FINDING: Estimates of energy savings at different times of day and seasons, and the value of those savings under different assumptions on future penetration of renewable energy, will help describe the potential benefit of DERs.
FINDING: In a grid with significant penetration of DERs, the flexibility of appliances to postpone and modulate consumption may be more important than energy efficiency in terms of potential effects on localized and system-wide costs, emissions, and reliability.
FINDING: Appliance and equipment standards can, if misapplied, be a factor constraining or delaying the deployment of technologies that add flexibility to electrical loads.
FINDING: A robust and ever-present grid is essential for consumers that partially meet their electricity requirements from on-site generation (such as solar). The grid is also a back-up to “hardened” small distributed electrical systems for those that seek to self-generate all of their needs. In these cases, efficiency is still of prime importance.
The next trend of relevance to the Appliance and Equipment Standards Program is the move to change between energy carriers—natural gas, electricity, hydrogen and so forth—that are used in providing a given energy service. Some energy services such as hygienic services (e.g., from hot water)
8 Local generation is often referred to as distributed generation (DG). Photovoltaic (PV) power generation installed by customers is a form of DG. Most practitioners also include DG in a basket of technologies called distributed energy resources or DERs, which include DG, responsive loads, and electric energy storage.
and the appliances that provide them can be accomplished with different energy carriers. The term “fuel switching” is often used to describe this trend, but it carries with it various connotations, some of which are misleading. Using this term often relegates electricity to the category of a commodity instead of a high-valued energy form—a premium fuel. The most popular definitions of fuel switching include (1) the substitution of one fossil energy source for another, for instance, in steam or combustion turbine power plants or industrial boilers designed to operate on either coal, natural gas or fuel oil; and (2) programs created to encourage consumers to change out or buy electric appliances for ones using natural gas or fuel oil. In the 1990s, fuel switching was used to promote the idea that society should be aggressively abandoning electric appliances in favor of natural gas appliances (Farnsworth, 2018). Sentiment has since reversed because of three factors:
- The relative efficiency of electric devices has increased more significantly as compared to equivalent natural gas counterparts. This is due to the ability of such devices to harness electricity’s attributes and use, for example, motors instead of engines, microwave heat sources instead of flames, and vapor compression instead of evaporation;
- Environmental priorities have moved the reduction of carbon dioxide and related pollutants emitted by natural gas devices forward as a national priority, and
- Electric end-use appliances increasingly have a lower carbon footprint—they are more efficient, able to be powered by carbon-free sources such as nuclear, solar and wind and, as a result, cause far lower emissions of carbon dioxide (CO2) and emit smaller volumes of criteria pollutants than carbon-based fuels.
While natural gas continues to be a popular fuel for building heating and domestic water heating across the United States, a growing number of states and localities have embraced aggressive policies that encourage “fuel switching” from intended new gas uses to electric. The California cities of San Jose, Menlo Park, San Mateo, West Hollywood, Santa Monica, and Marin County will ban or limit the use of natural gas in new buildings (Groom, 2010; Moench, 2019). Some energy policy experts envisage an electric economy as the viable strategy for decarbonizing the economy (EFI, 2019a).
California has taken an aggressive posture toward the use of building codes to enable carbon reduction through electrification by enabling local governments to enact. So-called “Reach Codes” under The California Codes and Standards (C&S) Reach Codes Program. These codes allow cities and counties to adopt restrictive codes that require one or more energy measures. In the Santa Clara Valley, sometimes referred to as Silicon Valley, thirteen communities have created a Reach Code, which mandates the use of electricity in place of natural gas whenever practical.
The final trend that will affect the Appliance and Equipment Standards Program is one that began in the 1990’s in which governments worldwide have restructured, privatized, and deregulated the electricity industry. With restructuring has come a reexamination of the role that energy efficiency plays in the energy market and a fostering of increased energy efficiency. With market transformation of energy efficiency, DOE will need to reexamine the need for and character of appliance and equipment standards. The term, Market Transformation, has been used to describe changes in energy efficiency.9 This new paradigm for energy efficiency is not yet widely used, nor has it been uniformly defined. More than a dozen states have restricted their electricity markets to some degree. Markets now allow for energy efficiency to be remunerated in multiple ways: via time of use rates, via capacity payments, and so forth. Whether or not DOE’s Appliance and Equipment Standards Program is consonant with this trend—that
is, whether its standards would slow down or even preclude investments—could have an impact on the emergence of new methods and structures that reduce (or mitigate increases in) energy consumption.
FINDING: As DOE currently implements it, the Appliance and Equipment Standards Program remains unaffected in restructured markets. However, a continuation of business as usual could dampen incentives as markets are restructured. Markets now allow energy efficiency remuneration in multiple ways, for example, time-of-use rates or capacity payments.
Market transformation is a coordinated program of activities that fosters private, competitive markets in which consumers seek and firms provide energy efficient products and services. Incentives provided by market changes may reduce “market failures” and obviate the need for standards in many cases and thus may become even more important in supporting consumer choice of efficient appliances.
It could also be the case that governments can design programs to transform markets by reducing market barriers so that energy-efficient products and services, including energy efficient appliances and equipment, become widely available and adopted by consumers without external incentives beyond implementation phases. In such a case, changes in markets resulting from these programs become permanent and self-sustaining, potentially eliminating the need for appliance efficiency standards. (Chapter 5 discusses the various alternatives to the National Standards Program that should be considered in the Regulatory Impact Analysis.)
The changes brought about by digital technologies enabling sensors, communications and computational ability, explained in the previous sections, provide the framework for developing a foundation that may allow DOE to begin to shift its focus from the efficiency of an individual appliance in ideal conditions to a broader focus. As noted above, the efficiency of the device itself; the overall system of which it is a part (e.g., a building’s thermal integrity with heating and cooling devices); and the choices consumers make in controlling the appliance are all determinants of the value of the energy that is supplied. The point of compliance, currently at the point of end-use, (i.e., the consumer product or industrial equipment), might be changed to be at the system itself. Doing so would include such aspects as installing and operating the appliance and the demand and pattern of usage—both of which are governed by consumer behavior. Shifting the point of compliance in this way would emphasize overall system efficiency and may capture untapped opportunities for reducing energy consumption.
This notion seems fanciful today, but digital technologies may make it feasible tomorrow. Nonetheless, any standards or guidelines issued to effect this change might need to specify criteria for installing and controlling appliances and equipment and in doing so anticipate a range of circumstances. A number of variables govern the in-situ energy consumption of an appliance beyond the operating conditions assumed in the testing procedures used when creating an efficiency standard. These include the quality of the installation, as well as the control and operation of the appliance.
For example, in the case of heating, ventilating, and air conditioning appliances that are an integral part of a building, regulations could include elements that define preferred installation procedures, controls, and preferred operating parameters in addition to requirements related to the appliance itself. These could prescribe the need for electrical connections that facilitate real-time operation, enable remote control for participation in demand control programs, and allow prescribed sensors for data collection.
FINDING: The actual energy consumption of consumer products and industrial equipment is a function of several elements beyond the device’s efficiency. Information regarding device-level energy consumption is necessary to enable a program of standards regulation to expand beyond appliances themselves. There are organizations in civil society, including trade associations and utilities, that collect these data. These complement the data collected by Energy Information
Administration in the Residential Energy Consumption Survey (RECS) and Commercial Building Energy Consumption Survey (CBECS) programs.
FINDING: Many manufacturers already include sensors, communications (including access ports), and electronic controls in today’s appliances. These allow manufacturers to add additional features at little-to-no added cost.
Directly metered, whole-house energy consumption data are increasingly available, facilitated by smart meters, which have been deployed in more than half of U.S. buildings for revenue metering purposes. Electric utilities often install these meters to collect data as part of regulatory requirements or capacity expansion planning, rate setting, and rate design. Dissecting whole-building data affords a better window on the impact of its efficiency standards as well on the choice of efficiency levels. Disaggregated data allows energy system planners, regulators and policymakers to best judge equitable policies, adjudicate tariffs for regulated entities, and plan critical community infrastructure. The list below indicates the kind of additional granularity in end-use consumption data that are currently available.
- Directly metered individual appliance data are frequently available. Utilities may collect this as part of load research studies or equipment developers’ research and development activities. Appliance level data can provide reliable engineering analysis to aid in developing standards and verifying assumptions. Added detail will provide clarity to the determination of efficiency level. Utilities and researchers often publish these data online. State public utility commissions and similar regulatory bodies generally insist these data be in the public domain.
- Whole-house data can be disaggregated. Analysts can disaggregate whole-house data to tease out appliance-level data through engineering analysis. This makes it possible to correlate this data with micro-scale analysis of electrical demand coupled with knowledge of the building’s devices. For heating, ventilation, and air conditioning systems, the change in consumption in various time horizons compared to the weighted temperature-humidity index in those time horizons can yield accurate information on disaggregated energy use and performance over time. Alternatively, analysts can marry whole-house metered data obtained in small increments of time with appliance saturation survey data and pattern recognition. Simple models can also yield reliable results by correlating a population of whole-house metered data sets with appliance saturation. Whole-house data would reflect the synergistic impact of some appliances that DOE does not capture in the current program. Examples include buildings that use dehumidifiers and air conditioners; buildings that use outside air for partial cooling; buildings that have multi-stage air conditioners; buildings where heat pump water heaters and dehumidifiers both operate; installations of water heaters and dishwashers simultaneously; and buildings with heating and cooling devices operating at the same time.
- Online monitoring of individual appliances is increasingly available. These data sources are often part of smart grid demonstrations, obtainable from sensors embedded by manufacturers in end-use appliances and devices. In a few cases, utilities or providers install them to enable regulatory justification for expenditures in demonstration projects.
- Use cases for sensors. DOE already encourages the dissemination of smart grid technologies and has made some “Use Cases” available. Use cases are the blueprints that determine what digital information and in what form a digital device sends and receives. Use cases are essential for integrating digital devices in the power system, including the distribution interface with end-use devices and appliances. By creating use cases for all relevant sensor applications in appliances and devices, DOE would become a catalyst for their adoption. This would hasten the possibility of a sensor network that could replace the need for an Appliance and Equipment Standards Program. There may be opportunities for DOE to partner with developers and energy suppliers to assist these efforts.
- Analyses of demand response data can provide a fairly reliable measure of demand during peak periods. Metering data will allow analysts to understand the actual appliance-level behavior of the consumer.
- Data from buildings that are continuously monitored. Such buildings use sensors to provide ongoing observations of energy consumption and environmental conditions to continually “tune” the building’s energy systems. Analyzing data from these buildings alongside data from appliance saturation could reveal the impact of various standards. Tracking the deployment of appliances and devices combined with continuous monitoring data could improve the development of standards.
The sources of data on energy consumption were discussed above in the section “Pattern and Amount of Consumption.” There are in addition data contributing to the understanding of consumer behavior in purchasing appliances and equipment, which are summarized below. The phenomenon of big data is discussed as it applies to energy consumption. Lastly, the importance of data libraries maintained by utilities is described.
There are many data sources providing information that can indicate the success of the sales of appliances or the saturation of those devices or both, often characterizing the device’s performance. The Internet enhances the availability of these data. Initially, efforts may be challenging but it could ultimately provide a reliable estimate of the success of appliance and equipment performance standards. Some data that could be gathered by DOE and others, subject to applicable restrictions on collection and use of data,10 would include the following:
- Manufacturer sales data
- Trade association’s summaries of sales (American Home Appliance Manufacturers, National Electrical Manufacturers Association, American Refrigeration Institute, etc.)
- Energy efficiency advocacy groups (Alliance to Save Energy, American Council for an Energy Efficient Economy, etc.)
- Laboratories (National Renewable Energy Laboratory [NREL], Oak Ridge National Laboratory [ORNL], Pacific Northwest National Laboratory [PNNL], and Electric Power Research Institute [EPRI], etc.)
- Trade press and the Internet
- Electric utility appliance surveys
- Commercial Building Energy Consumption Survey (CBECS) and Residential Energy Consumption Survey (RECS)
- Consumer product registration data (i.e., product registration submitted voluntarily to manufacturers by consumers)
10 Including, for example, the Paperwork Reduction Act.
Size, Scope, and Scale
The sheer volume of public and private data available regarding energy consumption, consumer demographics, consumer behavior, and related data such as weather, geography, and economic activity has caused a new industry to spring up almost overnight.
Several key trends will continue to accelerate the amount of big data relevant to appliance efficiency standards. These are as follows: (1) the increasing use of sensors, connectivity, and computational ability of appliances and devices; (2) an increasing need for power system planners and operators to manage the added stress on the power system resulting from load growth and the increased severity of outages from natural events (e.g., wildfires and hurricanes); (3) the evolution of the smart grid along with various alternative pricing techniques (e.g., time-of-use and dynamic pricing) implemented with smart meters offering more granularity on consumer electricity purchase behavior (Faruqui, 2020); and (4) increasing interest in externalities from regulators and utilities resulting in increased monitoring, measurement, rigorous analysis and data storage adding to the data library.
There is a mass of data that building and appliance sensors generate. When paired with metered data from utility billing and other sources, these data sets can provide key information on need and effectiveness of standards. The energy industry is spending a great deal of effort to develop analytical techniques to unpack the information that a data library of energy demand may reveal. For example, Exelon Utilities’ Utility Analytics lead Brian Hurst reported in Utility Dive that they have “developed 120 use cases for system-integrated analytics and the pipeline is growing” (Trabish, 2019).
There is also a growing interest in continuous monitoring of building energy performance. Continuous monitoring is ongoing observation of a building’s energy consumption and environmental conditions to regularly or continually “tune” its energy systems. By tracking the deployment of appliances and devices combined with continuous monitoring, DOE may be able to simplify the complicated process of developing standards.
FINDING: Appliance manufacturers are increasingly embedding information and communications technology such as sensors, digital displays, communications capability and other functionality in their appliances.
These manufacturers are responding to opportunities in the marketplace. Many consumers indicate strong preferences for appliances and devices that have greater functionality and are more “high-tech” with easy-to-read displays and controls. As alternative electricity pricing and demand response become more widespread, appliance owners and users will have the information and incentives to optimize their energy purchases and use. These ICT enhancements come at almost no cost. Today’s appliance buyers are hard-pressed to buy appliances that still use mechanical controls.
Utilities are interested in these developments because customer analytics help them increase customer satisfaction, enhance demand-side management programs involving energy efficiency and demand response, reduce costs, and improve service quality. There are numerous uses for data analytics, including improving customer service, designing demand-side management programs, and marketing. Conducting parallel analysis of customer satisfaction survey data with automated metering data using artificial intelligence techniques can reveal detailed information on the use of energy efficient equipment. This analysis includes insights into individual appliance purchase and use, the presence and use of home automation, remote auditing and diagnostics, and benchmarking and reporting.
The electric and gas utility industry is increasingly exploring using analytics to mine value from customer data. These data, combined with EIA’s data from RECS and CBECS, and other publicly available data sets could form the basis for analytics focused on customer demanded services, including actual energy consumption, load shape, tariff selections, program participation, information, and special
needs and wants. Ultimately, these may lead to the ability to modify methods for setting and evaluating appliance and equipment standards.
Among the more innovative applications involve using utility billing data along with business data such as the North American Industry Classification System (NAICS) data, geographic data, weather data, ZIP Codes, end-use equipment data, building codes, and other local statistics. DOE could easily combine data from RECS and CBECS in these analyses to create a powerful new method for setting appliance and equipment performance standards or to obviate the need for prescriptive standards entirely.
Data Analytic Techniques
Big data analytics is the process of examining big data to uncover information—such as hidden patterns, correlations, market trends and customer preferences. Big data analytics are being used today as a means by which utilities, consultants and others can leverage the inherent value in all of the data they already have or can easily obtain. Applying big data analytic techniques to appliance and equipment standard monitoring and development could fundamentally change the standard setting process and provide valuable information to manufacturers, consumers, and energy suppliers to further reduce overall energy consumption.
By enabling DOE to anonymously monitor the operation of both whole building and individual appliances and analyze that data with information about buildings, appliances, economics and consumer demographics, energy planners and policymakers could develop detailed models of the end-use dimension of the energy system. DOE could develop integrated end-use engineering econometric models with behavioral elements. They could also develop separate models for select residential market segments, commercial building types, and alternative ownership and operating strategies for those commercial businesses. These models would enable DOE to:
- Monitor the purchase behavior of appliances and devices and assess the impact of equipment standards and other programs such as Energy Star on that behavior;
- Monitor the in situ energy use of end-use appliances, devices, and the buildings and systems deployed in the United States;
- Precisely evaluate the impact of demand response, alternative pricing and demand-side management programs absent bias such as non-response, self-selection, take-back, free riders, and long-term participation; and
- Enable a significant improvement in the accuracy of the amount and pattern of forecasts for energy needs.
This phenomenon of big data revived old data analysis methods and caused development of several new ones (Bhatia, 2018). Analysts typically draw from techniques based in statistics, computer science, math, and economics, to process petabytes of records that are collected at a fast pace from trade transactions, smart meters, or smartphones (McKinsey Analytics, 2018; Ruderman et al., 1989).
DOE developed and has at its disposal EnergyPlus,11 a simulation engine. The BEopt (Building Energy Optimization Tool) software which runs on this simulation engine is an optimization tool that finds least-cost building designs for a given energy savings target. It further can identify near-optimal designs to accommodate builder or contractor preferences.12
FINDING: Improved data would allow for better modeling of building energy efficiency at the whole-house level. There are data available and platforms for analyzing this data currently in use, for example, BEOpt. By calculating changes over time, uses can compare a measure of a building’s energy system’s relative effectiveness to time periods before and after adoption of a particular standard.
FINDING: Analysis of smart meter and power system data can relate energy demand during specified time periods and from specific appliances to variables such as temperature, relative humidity, and the efficiency of a building’s energy system.
FINDING: DOE could gain important insights by adding qualitative data and using qualitative analysis techniques.
Fundamental to the analyses described above is the availability of data. In addition to DOE’s own data from RECS, BECS, and other activities, there are relevant data available from other entities. Several organizations have attempted to establish data repositories or data libraries where energy planners and policy makers can store and access consumer energy consumption data collected at the building or appliance level. These libraries, discussed further below, function under the assumption that such data is transferable so long as the conditions under which it was obtained and cataloged are known. For example, residential end use data is generally thought to be transferable if the climate conditions, housing characteristics, and economic activity of the areas are similar. Among the most extensive of these efforts are those of EPRI, the California Energy Commission (CEC), NREL, and the Association of Edison Illuminating Companies (AEIC).
EPRI’s effort is called Load Shape Library 7.0.13 The objective of the Load Shape Library is “to facilitate the collection, use and functionality of a library of representative electric load shapes by climate zone, geography or by utility.” According to EPRI, the Load Shape Library databases include electric end-use data aggregated over North American Electric Reliability Corporation (NERC) regions, whole premise electric data by U.S city, and residential efficient electric technology measures end-use load data. Data also includes EPRI’s field pilots, results of efforts from a national campaign of the Northwest Energy Efficiency Alliance (NEEA) to collect end-use data, BPA’s Pacific Northwest RBSA (Residential Building Stock Assessment), and EPRI CEED (Center for End-Use Energy Data) PowerShape™ data.
ADM Associates, Inc. publishes the CEC work, labeled California Energy Commission: Electric End-Use Load Shapes.14 CEC has captured the extensive load research data provided by California’s Investor-Owned Utilities in tools such as R-studio and Energy Plus and the CA System Advisor Model.
NREL has developed end-use load profiles for the U.S. building stock.
AEIC, the Association of Edison Illuminating Companies’ National Load Research Committee has announced the creation of a National Load Research Data Repository.15
Bhatia, M. 2018. “Your Guide to Qualitative and Quantitative Data Analysis Methods.” Humans of Data. September 4. https://humansofdata.atlan.com/author/manu-bhatia.
14 ADM Energy Research and Evaluation, https://www.admenergy.com/experience/cec-load-shapes-data-science.
15 Personal communication between Clark Gellings and AEIC National Office.
EFI (Energy Futures Initiative). 2019a. Moniz Presents New EFI Report on Decarbonizing California’s Economy. https://energyfuturesinitiative.org/news/2019/4/11/moniz-presents-new-efi-report-on-decarbonizing-californias-economy.
EFI. 2019b. Promising Blockchain Applications for Energy: Separating the Signal from the Noise. Washington, DC.
Farnsworth, D. 2018. Fuel-Switching: We Just Did This in 1990, So, Why Are We Doing It Again? Regulatory Assistance Project (RAP) Blog Post. June 11. https://www.raponline.org/blog/fuel-switching-we-just-did-this-in-1990-so-why-are-we-doing-it-again.
Faruqui, A. 2020. 6 Reasons Why California Needs to Deploy Dynamic Pricing by 2030. Utility Dive. May 19. https://www.utilitydive.com/news/6-reasons-why-california-needs-to-deploy-dynamic-pricing-by-2030/578156.
Gellings, C.W., A.P. Fickett, and A.B. Lovins. 1990. “Efficient Use of Electricity.” Scientific American 262: 65-74. September.
Groom, N. 2010. San Jose Moves to Ban Natural Gas in New Buildings in New Residential Buildings. Reuters. September 17. https://www.reuters.com/article/us-usa-naturalgas-sanjose/san-jose-moves-to-ban-natural-gas-in-new-residential-buildings-idUSKBN1W302J.
Harris, J. 2019. Market Transformation: Moving Beyond Traditional Energy Efficiency Programs to Cement Change. Utility Dive. July 3. http://www.utilitydive.com.
McKinsey Analytics. 2018. Analytics Comes of Age. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/analytics-comes-of-age.
Moench, M. 2019. California Regulators Clear Way for Natural Gas Bans to Take Effect. San Francisco Chronicle. December 11. https://www.sfchronicle.com/business/article/California-regulators-clear-way-for-natural-gas-14900008.php.
Paris, J., J.S. Donnal, and S.B. Leeb. 2018. “NilmDB: The Non-Intrusive Load Monitor Database.” IEEE Transactions Smart Grid 5(5): 2459-2467. September.
Ruderman, H., J.H. Eto, K. Heinemeier. 1989. Residential End-Use Load Shape Data Analysis: Final Report. LBL-27114. Berkeley, CA: Lawrence Berkeley National Laboratory. April.
Trabish, H.K. 2019. The Biggest Numbers Game in the Power Sector: Data Analytics and the Utility Community of the Future—Software and Data Are Transforming the Utility Industry and Connecting Energy Users. Utility Dive. March 25. https://www.utilitydive.com/news.
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