Elsevier

Energy

Volume 28, Issue 9, July 2003, Pages 941-951
Energy

Air conditioning market saturation and long-term response of residential cooling energy demand to climate change

https://doi.org/10.1016/S0360-5442(03)00033-1Get rights and content

Abstract

Existing state-level models relating climate parameters to residential electricity consumption indicate a nominal sensitivity of 2–4% for each degree Celsius increase in ambient temperatures. Long-term climate change will also impact electricity consumption through corresponding increases in the market saturation of air conditioning. In this paper we use air conditioning market saturation data for 39 US cities to develop a generalized functional relationship between market saturation and cooling degree days. The slope of this saturation curve is particularly high for cities that currently have low to moderate saturation. As a result, the total response of per capita electricity consumption to long-term warming may be much higher than previously thought. A detailed analysis of 12 cities in four states shows that for some cities changes in market saturation may be two to three times more important than the role of weather sensitivity of current loads. While actual behavioral response to climate change will be more complicated than that captured in our model of market saturation, this approach provides a new perspective on the sensitivity of space conditioning electricity consumption in the US to climate change.

Introduction

Many of the studies that have investigated the sensitivity of electricity consumption to weather have focused on short-term load forecasting [1], [2], [3], [4]. Some have extended this sort of analysis to investigate the role of weather variability on carbon emissions [5]. Other studies have extended weather sensitivity to address issues of electricity consumption response to climate variability and change [6], [7], [8], [9]. The most extensive effort to evaluate end-use electricity consumption sensitivity to climate at regional scales can be found in the residential and commercial energy consumption surveys, RECS and CBECS, respectively. These surveys gathered energy-related data for a statistical sample of residential and commercial buildings [10], [11]. They include national level information about building construction, occupancy, and load schedules. The data are aggregated into five broad climatic zones based on levels of cooling and heating degree days (CDD and HDD) for the entire US. This sample survey approach has been used along with projections of climate change and projections of future (2030) commercial building stock to investigate the potential impact of climate change on commercial building energy consumption [12]. The most recent RECS report also includes more detailed summaries of the four largest states in the survey, but the authors of this report point out that they would need to increase their sample size by at least a factor of 5 in order to have the necessary data coverage to make statistically valid state-level analyses for the entire US. This limitation and other drawbacks of the survey approach provided the motivation for the development of a more robust regression-based methodology to isolate weather-related factors that determine electricity consumption at regional scales [6], [7]. The electricity consumption data in this approach is trend-adjusted for non-climatic factors (e.g. changes in building stock and economic factors). The resulting models, however, provide only an assessment of short-term response to weather variability, assuming all other factors remain constant. In the case of long-term climate change it is likely that there will be a wholesale change in the market saturation of air conditioning systems. This is particularly the case in areas for which saturation is currently only low to moderate (<0.8), but for which a modest shift in climate could precipitate a dramatic increase in saturation. Hence, the actual response of electricity consumption to long-term climate change may be significantly underestimated by the sensitivity models presented by Sailor [6]. This observation is consistent with the work of Pettersen who used a Monte Carlo approach to simulate energy consumption in dwellings [13]. He noted that variability in residential consumer behavior was a more significant factor in determining energy consumption in his model than were variations in climate. As pointed out by others, the key socio-economic factors that affect these behavioral differences include household income, household size, and electricity price [14]. The purpose of the present paper is to investigate the potential for changes in market saturation to exacerbate the increased electricity demand resulting from climate change.

Section snippets

Overview of electricity consumption sensitivity models

The methodology for modeling per capita electricity consumption developed in Ref. [10] was applied to energy-intensive states in diverse geographical locations resulting in monthly aggregated statewide predictive models for both the residential and commercial sectors [6]. The focus of the present paper is on the residential sector in three cities within each of four states from this study. The sensitivity results from Sailor [6] are given subsequently with a brief summary of the methodology

Air conditioning market saturation

Air conditioning market saturation depends on a large number of factors including economic, social, and climatic conditions. While the electricity consumption models discussed above deal solely with sensitivity to short-term weather variations, the response of market saturation to long-term shifts in climate may play an important role in determining how electricity consumption on the whole will respond to global warming. Within the US there are a number of warm climate regions where the market

AC electricity consumption response to long-term climate change

To investigate the impact of climate parameters on per capita residential air conditioning electricity consumption it is useful to separate the climate-sensitive and climate-insensitive components of consumption as follows:E′r=E′r-AC+E′r-H+E′r-base,where Er-AC, Er-H are the climate-sensitive (AC and heating) components, and Er-base is the climate-insensitive (base) component. The electricity models presented in Table 1 do not explicitly differentiate among these components, although, in

Conclusions

An analysis of air conditioning market saturation in 39 US cities has revealed a strong relationship between saturation and cooling degree days. While saturation of central AC systems increases monotonically with CDD, there appears to be a peak in window unit saturation at around 500 °C days. The total saturation curve has been well modeled with an exponential saturation function. Only one of the 39 cities deviates substantially from this curve.

For many of the cities in this study there is

Acknowledgements

The authors wish to acknowledge the thoughtful comments of anonymous reviewers.

References (17)

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