Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, digital-twin of residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high resolution, residential energy-use dataset for the United States.
Modernization of the U.S. electric grid is occurring at a noteworthy rate due to the installation of new technologies within the grid such as smart meters. They enable two-way communication between the customer and utilities, providing information and granular control of power usage for individual households1,2. The grid is also witnessing rapid transformations due to increasing penetration of electric vehicles (EV) and distributed energy resources (DER) such as rooftop photovoltaics (PV), community solar, and wind energy. While this wave of modernization is beneficial, the electric grid is simultaneously facing a sharp increase in crisis situations as a result of climate change phenomena3,4 such as extreme weather events and global warming. One example of extreme weather is the February 2021 North American cold wave that caused a tremendous strain on the power grid especially in Texas where millions lost power for days5. Another example is where global warming impacts household HVAC energy use. Although the rise of 1° to 2 °C in winter temperatures is expected to decrease heating requirements, a similar rise in summer temperatures is expected to increase cooling needs significantly6.
In the face of these challenges, achieving sustainable energy goals has become paramount for maintaining a healthy grid. To this end, the research community is faced with important questions regarding reduction of carbon footprints7,8,9,10,11, incentivizing DER adoption12, studying benefits of building energy retrofit9,13,14, integration of electric vehicles15 and consumer behavior16 in the grid, and mechanisms for designing electricity pricing17,18 to create efficient residential consumption patterns. Answering many of these questions requires comprehensive knowledge of energy-use patterns, building stock, the structure of distribution networks, consumer behaviors, and so on. However, such exhaustive datasets are rarely freely available (or available at all) for research use, making it hard for the research community to pursue these endeavours19. Reasons for unavailability of such data range from privacy concerns to the lack of a system for making data available to researchers.
Most of the published energy use data are metered data, a result of longitudinal studies conducted by researchers (Table 1) with relatively small samples of households that may not be representative of the wider geographical region and demographics. Some of these studies monitor households over a longer period of time (e.g. two years), however, the downside of such experiments is that it takes a considerable amount of time (e.g. participant consent, equipment setup, monitoring) and manual effort (e.g., data cleaning, imputing missing values) before such data is usable. Although these studies release energy data for free use, many of them limit publishing participant details (e.g. building characteristics and location, household level demographics). Participant details are usually withheld due to privacy reasons/participant consent, lack of information, or unavailability of these attributes in the free version of the data.
Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, digital-twin of residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high resolution, residential energy-use dataset for the United States.
Modernization of the U.S. electric grid is occurring at a noteworthy rate due to the installation of new technologies within the grid such as smart meters. They enable two-way communication between the customer and utilities, providing information and granular control of power usage for individual households1,2. The grid is also witnessing rapid transformations due to increasing penetration of electric vehicles (EV) and distributed energy resources (DER) such as rooftop photovoltaics (PV), community solar, and wind energy. While this wave of modernization is beneficial, the electric grid is simultaneously facing a sharp increase in crisis situations as a result of climate change phenomena3,4 such as extreme weather events and global warming. One example of extreme weather is the February 2021 North American cold wave that caused a tremendous strain on the power grid especially in Texas where millions lost power for days5. Another example is where global warming impacts household HVAC energy use. Although the rise of 1° to 2 °C in winter temperatures is expected to decrease heating requirements, a similar rise in summer temperatures is expected to increase cooling needs significantly6.
In the face of these challenges, achieving sustainable energy goals has become paramount for maintaining a healthy grid. To this end, the research community is faced with important questions regarding reduction of carbon footprints7,8,9,10,11, incentivizing DER adoption12, studying benefits of building energy retrofit9,13,14, integration of electric vehicles15 and consumer behavior16 in the grid, and mechanisms for designing electricity pricing17,18 to create efficient residential consumption patterns. Answering many of these questions requires comprehensive knowledge of energy-use patterns, building stock, the structure of distribution networks, consumer behaviors, and so on. However, such exhaustive datasets are rarely freely available (or available at all) for research use, making it hard for the research community to pursue these endeavours19. Reasons for unavailability of such data range from privacy concerns to the lack of a system for making data available to researchers.
Most of the published energy use data are metered data, a result of longitudinal studies conducted by researchers (Table 1) with relatively small samples of households that may not be representative of the wider geographical region and demographics. Some of these studies monitor households over a longer period of time (e.g. two years), however, the downside of such experiments is that it takes a considerable amount of time (e.g. participant consent, equipment setup, monitoring) and manual effort (e.g., data cleaning, imputing missing values) before such data is usable. Although these studies release energy data for free use, many of them limit publishing participant details (e.g. building characteristics and location, household level demographics). Participant details are usually withheld due to privacy reasons/participant consent, lack of information, or unavailability of these attributes in the free version of the data.
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