Climate sensitivity of end-use electricity consumption in the built environment: An application to the state of Florida, United States

Mukherjee, Sayanti; Nateghi, Roshanak

doi:10.1016/j.energy.2017.04.034

Cited by 55 publications

(50 citation statements)

References 17 publications

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“…BART algorithm and modeling process. We leveraged a non-parametric Bayesian ensemble-of-trees algorithm to characterize the climate sensitivity of electricity consumption, since the algorithm was shown to outperform other climate-demand nexus models in terms of predictive accuracy 1,14,15,31,38,39 . The independent variables in the development of the BART models were heat stress measures, and the response variable was state-level electricity consumption.…”

Section: Methodsmentioning

confidence: 99%

“…Comparing the results of the airtemperature-only and selected-features models, we determine the influence of each measure of heat stress on the overall climate sensitivity of demand. The predictive models are developed using a state-of-the-art, stochastic, non-parametric Bayesian ensembleof-trees algorithm 38 (see Methods), which has been shown to outperform other climate-demand nexus models in terms of predictive accuracy 1,14,15,31,39 .…”

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confidence: 99%

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The critical role of humidity in modeling summer electricity demand across the United States

2020

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Cooling demand is projected to increase under climate change. However, most of the existing projections are based on rising air temperatures alone, ignoring that rising temperatures are associated with increased humidity; a lethal combination that could significantly increase morbidity and mortality rates during extreme heat events. We bridge this gap by identifying the key measures of heat stress, considering both air temperature and near-surface humidity, in characterizing the climate sensitivity of electricity demand at a national scale. Here we show that in many of the high energy consuming states, such as California and Texas, projections based on air temperature alone underestimates cooling demand by as much as 10-15% under both present and future climate scenarios. Our results establish that air temperature is a necessary but not sufficient variable for adequately characterizing the climate sensitivity of cooling load, and that near-surface humidity plays an equally important role.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

The critical role of humidity in modeling summer electricity demand across the United States

2020

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“…Previous research has shown that unlike the transportation and industrial sectors, the residential and commercial sectors are most sensitive to climatic variability and change [ 3 , 11 – 14 ]. We therefore limited the scope of our analyses to the residential and commercial sectors.…”

Section: Introductionmentioning

confidence: 99%

A multi-paradigm framework to assess the impacts of climate change on end-use energy demand

Nateghi

Mukherjee

2017

PLoS ONE

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Projecting the long-term trends in energy demand is an increasingly complex endeavor due to the uncertain emerging changes in factors such as climate and policy. The existing energy-economy paradigms used to characterize the long-term trends in the energy sector do not adequately account for climate variability and change. In this paper, we propose a multi-paradigm framework for estimating the climate sensitivity of end-use energy demand that can easily be integrated with the existing energy-economy models. To illustrate the applicability of our proposed framework, we used the energy demand and climate data in the state of Indiana to train a Bayesian predictive model. We then leveraged the end-use demand trends as well as downscaled future climate scenarios to generate probabilistic estimates of the future end-use demand for space cooling, space heating and water heating, at the individual household and building level, in the residential and commercial sectors. Our results indicated that the residential load is much more sensitive to climate variability and change than the commercial load. Moreover, since the largest fraction of the residential energy demand in Indiana is attributed to heating, future warming scenarios could lead to reduced end-use demand due to lower space heating and water heating needs. In the commercial sector, the overall energy demand is expected to increase under the future warming scenarios. This is because the increased cooling load during hotter summer months will likely outpace the reduced heating load during the more temperate winter months.

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“…Lebassi et al (2010) used historical records to study CDD, demand, and consumption in California, finding large sensitivity to regional scale phenomena (e.g., increased sea-breeze impacting demand near the coast). Mukherjee and Nateghi (2017) found that mean dew point temperature was a better predictor of cooling and heating loads than CDD, with other climatic variables like wind speed and precipitation playing an important role.…”

Section: Introductionmentioning

confidence: 92%

Climate change impacts on peak building cooling energy demand in a coastal megacity

Ortiz

González

Lin

2018

Environ. Res. Lett.

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Climate sensitivity of end-use electricity consumption in the built environment: An application to the state of Florida, United States

Cited by 55 publications

References 17 publications

The critical role of humidity in modeling summer electricity demand across the United States

The critical role of humidity in modeling summer electricity demand across the United States

A multi-paradigm framework to assess the impacts of climate change on end-use energy demand

Climate change impacts on peak building cooling energy demand in a coastal megacity

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