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Whirlpool Brand U. Kitchen Appliances Refrigerators. See fridge models with this feature. Learn More. All Refrigerators Find innovations and purposeful organization to fit your family. For example, if a homeowner upgrades to a more efficient air-conditioning unit, they might end up using the air conditioning in the home more frequently, as it is less expensive to operate. The true value of the rebound is somewhat uncertain, but is likely small compared to the other variables in the model such as housing stock growth, climate change and changes in electricity prices Supplementary Discussion.

We validate our model results by developing a simple linear regression relationship between max daily temperature and residential electricity consumption. We then create a forecast with this regression model to compare against our archetype model. For the regression, we utilize daily maximum temperatures for downtown Los Angeles weather station code CQT from between and , downloaded from the Iowa Environmental Mesonet Since our model includes only residential buildings, we then estimate the proportion of LADWP sales that are residential using monthly totals of residential sales from US Energy Information Administration Form We multiply the residential sales proportion, by the total that is, combined residential and commercial hourly Federal Energy Regulatory Commission electricity sales to obtain hourly estimates of residential electricity consumption.

We sum then aggregate the estimated hourly sales to daily sales for equal temporal comparison to the temperature data. When we fit a regression curve to predict electricity consumption from daily maximum temperature Supplementary Fig. Using the second-order model, we then forecast electricity consumption between and using the future average temperature data for each RCP as the independent variable Supplementary Fig.

Next, we make some modifications to our archetype model in order to compare it to the regression model. The regression model has the assumptions of constant building stock and population, so we remove the dynamic elements of the model population, appliance mix, building turnover and so on for the purposes of validation. The only change in the archetype model for validation is the weather data. We forecast residential electricity consumption in this constant stock and technology case between and The data that support the findings of this study are available from the corresponding author upon reasonable request.

How to cite this article: Reyna, J. Energy efficiency to reduce residential electricity and natural gas use under climate change. Sailor, D. Air conditioning market saturation and long-term response of residential cooling energy demand to climate change. Energy 28 , — Davis, L. Contribution of air conditioning adoption to future energy use under global warming. Natl Acad. USA , — Bartos, M. Impacts of rising air temperatures on electric transmission ampacity and peak electricity load in the United States. Sathaye, J. Estimating risk to California energy infrastructure from projected climate change.

Report No. US Census Bureau. California Energy Commission.


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Electricity Consumption by County. Accessed February 26 KEMA, Inc. California Department of Finance. McCollum, D.

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Energy Strateg. Morrison, G. Comparison of low-carbon pathways for California. Change , — Energy Information Administration. Everywhere but Northeast, fewer homes choose natural gas as heating fuel.

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Today in Energy. Impacts of climate change on electric power supply in the Western United States. Change 5 , — Power-generation system vulnerability and adaptation to changes in climate and water resources. Change 6 , — State of California.

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