No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Revealing spatial and temporal patterns of residential cooling in Southern California through combined estimates of AC ownership and use
... We also introduce a novel metric, the household AEF (HAEF) that captures the emissions intensity of a household's electricity consumption during a period of time. Finally, we utilize an established methodology to identify electricity consumption for air-conditioning [16,17] and analyze the emissions associated with AC use. We address three primary research questions in this work: ...
... We also investigated the emissions associated with electricity consumed for AC use and compared these results with overall HEE. First, we identified which households have AC using the AC ownership Algorithm developed in Peplinski et al [16]. In this methodology, households are fit to four models that represent different dependencies between hourly electricity use and demand. ...
Electrifying the residential sector is critical for national climate change adaptation and mitigation strategies, but increases in electricity demand could drive-up emissions from the power sector. However, the emissions associated with electricity consumption can vary depending on the timing of the demand, especially on grids with high penetrations of variable renewable energy. In this study, we analyze smart meter data from 2019 for over 100 000 homes in Southern California and use hourly average emissions factors from the California Independent System Operator, a high-solar grid, to analyze household CO2 emissions across spatial, temporal, and demographic variables. We calculate two metrics, the annual household electricity-associated emissions (annual-HEE), and the household average emissions factor (HAEF). These metrics help to identify appropriate strategies to reduce electricity-associated emissions (i.e. reducing demand vs leveraging demand-side flexibility) which requires consideration of the magnitude and timing of demand. We also isolate the portion of emissions caused by AC, a flexible load, to illustrate how a load with significant variation between customers results in a large range of emissions outcomes. We then evaluate the distribution of annual-HEE and HAEF across households and census tracts and use a multi-variable regression analysis to identify the characteristics of users and patterns of consumption that cause disproportionate annual-HEE. We find that in 2019 the top 20% of households, ranked by annual-HEE, were responsible for more emissions than the bottom 60%. We also find the most emissions-intense households have an HAEF that is 1.7 times higher than the least emissions-intense households, and that this spread increases for the AC load. In this analysis, we focus on Southern California, a demographically and climatically diverse region, but as smart meter records become more accessible, the methods and frameworks can be applied to other regions and grids to better understand the emissions associated with residential electricity consumption.
... In practice, many empirical studies are constrained to coarse-grained spatial scales primarily due to the unavailability of high-resolution spatial information for agent-based data (Peplinski, Mayes, & Sanders, 2025;Wu, Heppenstall, Meier, Purshouse, & Lomax, 2022). Data on consumer energy consumption and household energy behaviors are often sourced from surveys or energy providers (Tuccillo et al., 2023;Zhang et al., 2018). ...
Analyzing household energy behaviors from a spatial perspective is crucial for both policymakers and network operators. However, conducting spatial analysis at a fine scale presents significant challenges, primarily due to data scarcity. This article demonstrates the potential of integrating spatial microsimulation and spatial econo-metric models to address this issue. Specifically, we construct a static spatial microsimulation model to create a synthetic population for the entire Netherlands, simulating neighborhood-level spatial distributions of household energy behaviors. The results indicate significant neighborhood-level spatial heterogeneity in the adoption of energy behaviors. Subsequently, this simulated output is used as input for spatial econometric models to analyze the neighborhood-level spatial determinants of household energy behavior adoption. We identify the roles of both spatial endogenous effects and neighborhood-specific characteristics in contributing to the observed spatial variations in household energy behaviors.
Global cooling capacity is expected to triple by 2050, as rising temperatures and humidity levels intensify the heat stress that populations experience. Although air conditioning (AC) is a key adaptation tool for reducing exposure to extreme heat, we currently have a limited understanding of patterns of AC ownership. Developing high resolution estimates of AC ownership is critical for identifying communities vulnerable to extreme heat and for informing future electricity system investments as increases in cooling demand will exacerbate strain placed on aging power systems.
In this study, we utilize a segmented linear regression model to identify AC ownership across Southern California by investigating the relationship between daily household electricity usage and a variety of humid heat metrics for 200,000 homes. We hypothesize that AC penetration rate estimates, i.e., the percentage of homes in a defined area that have AC, can be improved by considering indices that incorporate humidity as well as temperature. We run the model for each household with each unique metric for the years 2015 and 2016 and compare differences in AC ownership estimates at the census tract level. In total, 81% of the households were identified as having AC by at least one heat metric while 69% of the homes were determined to have AC with a consensus across all five of the heat metrics. Regression results also showed that the r2 values for the dry bulb temperature (0.39) regression were either comparable to or higher than the r2 values for humid heat metrics (0.15-0.40). Our results suggest that using a combination of heat metrics can increase confidence in AC penetration rate estimates, but using dry bulb temperature alone produces similar estimates to other humid heat metrics, which are often more difficult to access, individually. Future work should investigate these results in regions with high humidity.
We elucidate mid-century climate change impacts on electricity demand accounting for endogenous adoption of residential air-conditioning (AC) in affluent, cooler countries in Europe, and in poorer, hotter states in India. By 2050, in a high-warming scenario (SSP585) AC prevalence grows twofold in Europe and fourfold in India, reaching around 40% in both regions. We document a mitigation-adaptation tradeoff: AC expansion reduces daily heat exposures by 150 million and 3.8 billion person degree-days (PDDs), but increases annual electricity demand by 34 TWh and 168 TWh in Europe and India, respectively (corresponding to 2% and 15% of today’s consumption). The increase in adoption and use of AC would result in an additional 130 MMTCO2, of which 120 MMTCO2 in India alone, if the additional electricity generated were produced with today’s power mix. The tradeoff varies geographically and across income groups: a one PDD reduction in heat exposure in Europe versus India necessitates five times more electricity (0.53 kWh vs 0.1 kWh) and two times more emissions (0.16 kgCO2 vs 0.09 kgCO2), on average. The decomposition of demand drivers offers important insights on how such tradeoff can be moderated through policies promoting technology-based and behavioral-based adaptation strategies.
The residential sector accounts for around 24% of the total electricity consumption in India. Recent studies show that air conditioners (ACs) have become a significant contributor to residential electricity consumption. Further, it is predicted that by 2037, the demand for ACs will increase by four times due to their affordability and availability. Not many studies have been found on residential AC usage patterns and the factors (AC load, setpoint, hours of usage) that influence household electricity consumption. This paper investigates the residential AC usage patterns and AC’s contribution to total residential electricity consumption. Twenty-five urban homes from a wet and dry climatic region of India were monitored for nine months (in 2019) to determine overall household electricity consumption patterns, AC usage, and indoor environment during summer, monsoon, and winter. Analysis of seasonal consumption patterns shows a significant difference in electricity usage between homes with ACs and homes without ACs during the summer season. The average electricity consumption for AC homes was 15.1 kWh/day during summer, 6.6 kWh/day during monsoon, and 6.1 kWh/day during the winter season. Results showed that AC alone contributed to 39% of the total household consumption in summers. The peak AC usage in all homes is observed during sleep hours which was generally between 10:00 pm and 6:00 am and the average AC runtime was 6.2 h. The average indoor temperature was recorded as 26.9 °C during the AC ON period. The AC peak load, i.e., the maximum electricity demand during the AC ON period, is 1.7 kW on average during the study period. The average annual consumption of homes with ACs was 2881 kWh, and for non-AC homes, the consumption was 2230 kWh. Findings from our analysis provide a detailed understanding of AC consumption profiles and the difference in electricity consumption characteristics between AC and non-AC homes across different seasons.
Purpose of Review
We review the literature on the potential for electrifying energy use in residential in commercial buildings, focusing on the USA. In so doing, we identify critical economic and non-economic factors that bear on the prospects for building electrification.
Recent Findings
At present electrification is most viable in new buildings, in milder climates, where a single electric heat pump can replace both existing heating and cooling units that are at or near retirement, and especially where local gas infrastructure costs can be avoided.
Summary
Per the reviewed studies, electrification is already viable in many buildings today, and likely stands to become viable in more buildings over time. A number of policy factors may promote or impede electrification; rapid electrification would likely require significant policy intervention. The literature is mostly focused on residential buildings and additional research on commercial buildings would be welcome.
Extreme heat events are increasing in frequency and intensity, challenging electricity infrastructure due to growing cooling demand and posing public health risks to urbanites. In order to minimize risks from increasing extreme heat, it is critical to (a) project increases in electricity use with urban warming, and (b) identify neighborhoods that are most vulnerable due in part to a lack of air conditioning (AC) and inability to afford increased energy. Here, we utilize smart meter data from 180 476 households in Southern California to quantify increases in residential electricity use per degree warming for each census tract. We also compute AC penetration rates, finding that air conditioners are less prevalent in poorer census tracts. Utilizing climate change projections for end of century, we show that 55% and 30% of the census tracts identified as most vulnerable are expected to experience more than 16 and 32 extreme heat days per year, respectively.
Climate change, urbanization, and economic growth are expected to drive increases in the installation of new air conditioners, as well as increases in utilization of existing air conditioning (AC) units, in the coming decades. This growth will provide challenges for a diversity of stakeholders, from grid operators charged with maintaining a reliable and cost-effective power system, to low-income communities that may struggle to afford increased electricity costs. Despite the importance of building a quantitative understanding of trends in existing and future AC usage, methods to estimate AC penetration with high spatial and temporal resolution are lacking. In this study we develop a new classification method to characterize AC penetration patterns with unprecedented spatiotemporal resolution (i.e. at the census tract level), using the Greater Los Angeles Area as a case study. The method utilizes smart meter data records from 180 476 households over two years, along with local ambient temperature records. When spatially aggregated, the overall AC penetration rate of the Greater Los Angeles Area is 69%, which is similar to values reported by previous studies. We believe this method can be applied to other regions of the world where household smart meter data are available.
Although occupants play a critical role in a building’s energy consumption, their air-conditioning use behaviour is often ignored, with it usually approximated as being a continuous operation. This in turn leads to a large difference between the actual and expected levels of energy consumption, raising the need to study air-conditioning use behaviour of building occupants. In this survey, 3083 questionnaires dealing with air-conditioning use behaviour were received from 34 cities distributed throughout China. The results show occupancy rates of 20% to 75% in residences and around 80% in office buildings during the period of 8:00–18:00. Air-conditioning use behaviour only meets a liveable requirement in residences and reaches a comfortable level in office buildings. This leads to daily air-conditioning operation times of between 0.85 and 4.31 hours in residences, and between 1.67 and 7.22 hours in office buildings, indicating most air-conditioning systems do not run continuously but intermittently, and that the assumption of continuous operation is far removed from reality. Based on these survey results, the large differences between the realistic (intermittent) and assumed (continuous) use of air-conditioning are employed to reveal the weaknesses in the present standards and to examine the optimization of building energy efficiency.
Climate change is expected to decrease heating demand and increase cooling demand for buildings and affect outdoor thermal comfort. Here, we project changes in residential heating degree-days (HDD) and cooling degree-days (CDD) for the historical (1981-2010) and future (2080-2099) periods in the United States using median results from the Climate Model Intercomparison Project phase 5 (CMIP5) simulations under the Representation Concentration Pathway 8.5 (RCP8.5) scenario. We project future HDD and CDD values by adding CMIP5 projected changes to values based on historical observations of US climate. The sum HDD + CDD is an indicator of locations that are thermally comfortable, with low heating and cooling demand. By the end of the century, station median HDD + CDD will be reduced in the contiguous US, decreasing in the North and increasing in the South. Under the unmitigated RCP8.5 scenario, by the end of this century, in terms of HDD and CDD values considered separately, future New York, NY, is anticipated to become more like present Oklahoma City, OK; Denver, CO, becomes more like Raleigh, NC, and Seattle, WA, becomes more like San Jose, CA. These results serve as an indicator of projected climate change and can help inform decision-making.
Occupant behaviour and its relation to energy use within the built environment have become more important in recent years. This paper first describes three statistical models based on a field survey and measurement for summer and winter respectively, which can be used to predict the percentage of air-conditioning-units used during night-time with a realistic assumption of human behaviour. The first statistical models are the results of a common approach to connect the occupant behaviour with the mean outdoor air temperature through the use of logistic regression. The present investigation extends these models to one including further external factors such as the mean outdoor air temperature the night before. The final models also include the preference and background of the individual subject. The statistical models are then compared to a theoretical model of occupant behaviour based on a comprehensive literature review. In conclusion, mean outdoor air temperature of the foregoing night(s) had a major impact on occupant behaviour during summertime, but a minor one in wintertime. The impact of the individual factors had the same magnitude as the external factors in summer, but an eight times higher impact in winter. Detailed research on the occupant behaviour is proposed to clarify further aspects of the theoretical model.
Improved means of controlling electricity consumption plays an important part in boosting energy efficiency in the Swedish power market. Developing policy instruments to that end requires more in-depth statistics on electricity use in the residential sector, among other things. The aim of the study has accordingly been to assess the extent of variance in annual electricity consumption in single-family homes as well as to estimate the impact of household features and building properties in this respect using independent samples t-tests and one-way as well as univariate independent samples analyses of variance. Statistically significant variances associated with geographic area, heating system, number of family members, family composition, year of construction, electric water heater and electric underfloor heating have been established. The overall result of the analyses is nevertheless that variance in residential electricity consumption cannot be fully explained by independent variables related to household and building characteristics alone. As for the methodological approach, the results further suggest that methods for statistical analysis of variance are of considerable value in indentifying key indicators for policy update and development.
This article presents an approach applying computational intelligence for detecting the use of air conditioners in households. The main objective is determining the intensive use of air conditioning with a high level of confidence. Unsupervised (-means) and supervised (Artificial Neural Networks) approaches are developed for classifying consumers for a case study in Uruguay, using data collected by a smart meters network and open weather data. Data from 29 Uruguayan cities were considered in the period from January 1, 2022, to December 31, 2022. Two thermal models are developed for estimating the temperature inside households. The main results indicate that the proposed approach is able to reach a high classification accuracy, up to 94.5% and a high classification recall, up to 95%, for the considered real case study. The final scope of the work is developing smart tools for classifying consumers, to design and suggest specific commercial products that promote energy efficiency.
The adoption of air conditioners (ACs) in households is rapidly growing in response to rising global temperatures, thereby posing significant sustainability challenges. It is vital to identify the characteristics and patterns of household demand for AC-induced electricity consumption. However, owing to huge costs and privacy concerns, appliance-level data sets are rare. Therefore, we conducted the Chinese Residential Energy Consumption Survey (CRECS) 2021, collected high-frequency operation data from 1140 active ACs from 673 households in 40 southern Chinese cities, and depicted the overall residential space-cooling electricity consumption profile. We found that, first, urban households in southern China showed a highly consistent pattern in AC time use. The peak usage in both living rooms and bedrooms occurred at noon (11 a.m.–3 p.m.) and night (9 p.m.–1 a.m.), when electricity consumption was 29%–38% higher than at other times of the day. Second, AC usage patterns showed great heterogeneity in terms of city-level and household-level characteristics. Households in large cities typically owned less energy-efficient equipment and consumed more AC-related electricity than did those in mega- and small-and-medium cities. Middle-income households, smaller room sizes, less-educated heads of households, and households with older adults were associated with lower AC-related electricity use.
Appliance energy consumption tracking in a building is one of the vital enablers of energy and cost saving. An economical and viable solution would be to estimate individual appliance consumption from a single-point measurement without using dedicated appliance-wise sensors or sub-meters. Such solutions are called non-intrusive load monitoring or energy disaggregation. Though the technology was introduced around three decades ago, the recent deployments of smart meters and machine learning-based algorithms attract the researcher to develop many novel solutions to the technology. To this end, this paper aims to provide a comprehensive, unbiased, and systematic state-of-the-art review of the energy disaggregation technology by evaluating two research questions. These are, first, how did the research community approach to solve the NILM problem and how has the technology evolved, and second, how do various NILM solutions stand compared to others in terms of a few key attributes. Further, some key challenges and future research directions of the technology are highlighted.
There has been an increasing interest in addressing and discovering the factors influencing the households’ load profiles instead of their end-use energy demand. The rationale behind this tendency is to provide households with load shifting recommendations and flatten the load profiles by making use of the knowledge obtained from these temporal and contextual determinants. Methodologies connecting households’ activities and presence to load profiles are often under-investigated, and the flexibility in the presence and activity routines of households throughout a long period is ignored. In this study, a data-driven framework is developed to extract households’ daily occupancy patterns throughout a year, determine the regular high- and low-energy consumption periods, and discover influencing activity factors of energy consumption within the obtained periods. The purpose of this study is to provide households with customized load-shifting and energy-saving suggestions based on their specific traits and routines. The results suggest that the distribution of occupancy patterns between seasons and weekdays varies considerably among different households. It is further recognized that days with similar occupancy patterns can have nearly similar peak timings in different apartments. The developed framework is generic and can be generalized to different households with different presence and activity routines.
Residential air conditioning loads (ACLs) are promising demand response (DR) resources with a certain flexibility and controllability that can enhance the operational flexibility and resource utilization of the power grid. The evaluation of DR potential is of great importance for estimating the power reduction, targeting appropriate DR customers, and obtaining constrained boundary for economic dispatch. To better reveal the multi-faceted factors and multi-uncertainties during DR, this paper present a novel definition and evaluation approach of operational DR potential from single customer to large-scale load centers. Aimed at resolving two main issues of evaluation: disaggregation of ACLs component and parameter estimation, an unsupervised load decomposition methodology considering load level difference and seasonal variation is proposed to disaggregate the whole-house energy consumption into ACLs and baseload components non-intrusively. Subsequently, based on the thermal dynamics model of ACLs, a segment analysis methodology is developed, including a constrained regression method for the static parameter estimation and a hybrid method for the dynamic parameter estimation. Data experiments based on ground truth data of residential customers in Austin, Texas, U.S., smart homes in Western Massachusetts and low voltage area in a developed city, Jiangsu province, China, validate the better performance of accuracy and robustness using the proposed methods. The proposed methods are further implemented to four application scenarios, including ACLs consumption behavior learning, operational DR potential analysis, customers targeting for different time-scale DR programs, and day ahead scheduling. These results also demonstrate that great difference in terms of ACLs usage patterns (a total of 19 patterns), DR potential (a maximum of 0.7 kW) results from different DR duration and operational conditions. The DR programs designers and load aggregators are suggested to consider the proposed 5 basic indicators to target customer and select participants in different DR scenarios. And the operational DR potential is more reliable and suitable to generate strategies for day ahead scheduling.
A high-quality building energy retrofit analysis requires knowledge of building characteristics like the type of installed heating system. This means auditing the building in person or conducting a detailed survey, which is not readily scalable for many buildings.
This paper presents a data-driven methodology to identify building characteristics from raw smart meter data sets to allow large scale, high-quality building retrofit analysis. We use the concept of energy signatures, a scatter plot with outside air temperature on the x-axis and electricity consumption on the y-axis, which condenses each building’s electricity use into one highly informative graph. Using a Support-Vector Regression model we extract the shape of each signature and cluster them subsequently. Dynamic time warping is used to align the signature shapes of all buildings. In two case studies, consisting of smart meter data sets from 408 and 480 buildings respectively, we show that our clusters correlated well to the heating system type and the building type by comparing to building-level metadata or demographic data.
Residential buildings are typically associated with high energy consumption, especially because of air-conditioning (AC) usage. Occupant behaviour can noticeably affect how an AC operates and thereby contribute to the increasing energy consumption. This problem is more pronounced with the split-type air-conditioner that is popular in South China. This study identified three representative patterns related to occupancy and AC on/off settings according to AC operation recordings from 102 bedrooms in several residential buildings of metropolitan Guangzhou, South China. Bedroom AC operation details were recorded using individual smart socket units. The resulting data showed clustering for daily on/off times, the duration of each operation, and AC ‘run-over-night’ probability. These clusters revealed three user-configuration patterns (i.e. night-time AC usage only (49%), both night-time and noon AC usage (6%), and low AC usage (45%)). Occupant age, total floors, and room area indices were most closely related to occupant behavioural patterns. Results indicated that AC units should be ‘turned on’ in timed steps when operation rates are higher than 30% or 20% to achieve acceptable thermal comfort performance. These findings should be useful for those intending to reduce overall energy consumption because the analysed schedules were based on realistic usage rather than conventional assumption (i.e. the full-time/full-space usage).
With growing health risks from rising temperatures in the Global South, the lack of essential indoor cooling is increasingly seen as a dimension of energy poverty and human well-being. Air conditioning (AC) is expected to increase significantly with rising incomes, but it is likely that many who need AC will not have it. We estimate the current location and extent of populations potentially exposed to heat stress in the Global South. We apply a variable degree days (VDD) method on a global grid to estimate the energy demand required to meet these cooling needs, accounting for spatially explicit climate, housing types, access to electricity and AC ownership. Our results show large gaps in access to essential space cooling, especially in India, South-East Asia and sub-Saharan Africa. Between 1.8 to 4.1 billion, depending on the required indoor temperatures and days of exposure, may need AC to avoid heat related stresses under current climate and socio-economic conditions. This number far exceeds the energy poverty gap indicated by the Sustainable Development Goal for electricity access (SDG7). Covering this cooling gap would entail a median energy demand growth of 14% of current global residential electricity consumption, primarily for AC. Solutions beyond improved AC efficiency, such as passive building and city design, innovative cooling technologies, and parsimonious use of AC will be needed to ensure essential cooling for all with minimized environmental damage. Meeting the essential cooling gap, as estimated by this study, can have important interactions with achieving several of the SDGs.
Predicting multi-building energy use at campus or city district scale has recently gained more attention; and more researchers have started to define reference buildings and study inter-impact between building groups. However, how to integrate the relationship to define reference buildings and predict multi-building energy use, using significantly less amount of building data and reducing complexity of prediction models, remains an open research question. To resolve this, this study proposed a novel method to predict multi-building energy use by integrating a social network analysis (SNA) with an Artificial Neural Network (ANN) technique. The SNA method was used to establish a building network (BN) by identifying reference buildings and determine correlations between reference buildings and non-reference buildings. The ANN technique was applied to learn correlations and historical building energy use, and then used to predict multi-building energy use. To validate the SNA-ANN method, 17 buildings in the Southeast University campus, located in Nanjing, China, were studied. These buildings have three years of actual monthly electricity use data and were grouped into four types: office, educational, laboratory, and residential. The results showed the integrated SNA-ANN method achieved average prediction accuracies of 90.67% for the office group, 90.79% for the educational group, 92.34% for the laboratory group, and 83.32% for the residential group. The results demonstrated the proposed SNA-ANN method achieved an accuracy of 90.28% for the predicted energy use for all building groups. Finally, this study provides insights into advancing the interdisciplinary research on multi-building energy use prediction.
Residential air-conditioner loads are known to be a key driver of summer demand peaks for some electricity industries. Such demand peaks occur infrequently but have potentially severe implications for network and peaking generation plant investment, and hence electricity prices for end users. Detailed assessments of air-conditioner peak demand have, however, been hampered by limited residential consumption data, and even when interval meter data is available, the complexities of other household loads and behavior. This study utilizes data from a significant appliance level consumption monitoring project in Sydney, Australia, to analyze the actual contribution of air-conditioners to regional summer demand peaks. K-means clustering is used to characterize air-conditioner load profiles across a large number of households at times of these overall demand peaks. These clustered air-conditioning load profiles, combined with a number of possible load control strategies, are then used to estimate possible peak load reductions from air-conditioner demand response across the Australian State of New South Wales. Our findings suggest that residential air-conditioning presents a significant demand response opportunity, approaching perhaps 9% of total peak demand in some circumstances.
Prior studies conclude that climate plays one of the most important roles in driving variations in residential electricity consumption. While some past studies have quantified sensitivities of electricity use to ambient temperature, 1) few previous studies utilize both high temporal and spatial resolution electricity data, and 2) no research to our knowledge has investigated how the temporal and spatial resolution of electricity data, and choice of ambient temperature indicators, affects quantification of these sensitivities. In this study, we use smart meter data records of electricity use for 1245 households across California, along with hourly ambient temperature records, to compute electricity–temperature sensitivities using a segmented linear regression approach. We find that electricity use and temperature show the strongest relationships when computed using daily accumulated electricity use and daily average temperatures; using these metrics results in a mean electricity–temperature sensitivity of 0.11 kW °C⁻¹. This value is higher than corresponding sensitivities computed using spatially aggregated data, with values ranging from 0.097–0.10 kW °C⁻¹ depending on the amount of spatial aggregation. Through presenting probability density functions of household-level electricity–temperature sensitivities, we illustrate insights that can be gleaned using high resolution electricity datasets such as that used here. We note that values of electricity–temperature sensitivity reported here are representative of the 1245 households under investigation.
Energy conservation in residential buildings has gained increased attention due to its large portion of global energy use and potential of energy savings. Occupant behavior has been recognized as a key factor influencing the energy use and load diversity in buildings, therefore more realistic and accurate air-conditioning (AC) operating schedules are imperative for load estimation in equipment design and operation optimization. With the development of sensor technology, it became easier to access an increasing amount of heating/cooling data from thermal energy metering systems in residential buildings, which provides another possible way to understand building energy usage and occupant behaviors. However, except for cooling energy consumption benchmarking, there currently lacks effective and easy approaches to analyze AC usage and provide actionable insights for occupants. To fill this gap, this study proposes clustering analysis to identify AC use patterns of residential buildings, and develops new key performance indicators (KPIs) and data analytics to explore the AC operation characteristics using the long-term metered cooling energy use data, which is of great importance for inhabitants to understand their thermal energy use and save energy cost through adjustment of their AC use behavior. We demonstrate the proposed approaches in a residential district comprising 300 apartments, located in Zhengzhou, China. Main outcomes include: Representative AC use patterns are developed for three room types of residential buildings in the cold climate zone of China, which can be used as more realistic AC schedules to improve accuracy of energy simulation; Distributions of KPIs on household cooling energy usage are established, which can be used for household AC use intensity benchmarking and performance diagnoses.
Human individual differences widely and markedly affect thermal comfort and should be carefully considered in the design and operation of the built environment. This paper aims to list and examine the magnitude and significance of individual difference in the preferred/neutral/comfort temperature through reviewing previous climate chamber and field studies. Causal factors for individual differences are investigated, including sex, age and etc. There is no clear and consistent conclusions as to the significance and size of inter-group differences in thermal comfort (between females and males, or the young and the old). To address the issue of individual difference, a paradigm shift from centralized to personalized air condition is on the way with the following three steps: first, collecting individual physiological and psychological response; second, predict individual comfort with machine learning algorithms; and third, accommodating individual difference with Personalized Comfort Systems.
Accurate estimations of the stochastic natures of energy demand for air-conditioner (A/C) use, which are affected by the diverse occupant behaviour, are needed, for the design and operation of residential cogeneration systems and distributed generation systems coupled with renewable energy sources and supply-demand management technologies. However past studies have mainly focused on developed countries, and knowledge of tropical regions, including many emerging countries with large populations, remains limited. We conducted field measurements of occupants' A/C usage behaviour in 38 dwellings in Kuala Lumpur, Malaysia. Measured data were considered as a function of start and end times, duration, and frequency of A/C usage in each dwelling. Analysis showed that A/C events were rare during daytime; however, there was a drastic increase in the frequency of A/C usage at night. Based on this, a simple algorithm to synthesize stochastic time patterns of A/C operation schedules was developed. The validity of the model was demonstrated through comparison with observational data.
As the tropics and subtropics become increasingly urban, industrial and affluent, energy demands for thermal comfort may evolve differently than it has historically across the global North. Already, heating, ventilation and air conditioning account for 35% of total primary energy in the United States, and are expected to reach similar proportions in China within 5 years. With increasing population in high temperature areas, electricity demand for increased air-conditioning usage may drive extreme electricity peak demands and total usage. This paper presents comparative estimates of peak and annual electric cooling and heating electricity usage at the city-scale, including both OECD and non-OECD member cities. Our results indicate that mature urban economies of the OECD exhibit a cooling electricity response of 35–90 Watts per °C per capita above room temperature for cooling (interquartile range of estimates). Tropical/subtropical cities outside the OECD (mostly in South Asia, Africa and the Middle East) currently demand just 2–9 W/°C/capita, indicating significant growth in temperature-dependent electricity demand as air conditioning is adopted. A similar story is unfolding on the heating side, with subtropical cities adopting electric resistive heaters, potentially precipitating additional electricity generation and delivery concerns, particularly electric resistance heating is adopted instead of heat pumps.
Significance
The existing empirical literature on the impacts of climate change on the electricity sector has focused on changing electricity consumption patterns. In this paper, we show that incorporating impacts on the frequency and intensity of peak load consumption during hot days implies sizable required investments in peak generating capacity (or major advances in storage technology or the structure of electricity prices), which results in substantially larger impacts than those from just changes in overall consumption.
As access to residential energy use data becomes more widely available, it is possible to identify significant energy consumers and provide guidance on mitigating such large loads. In hotter climates, such as Texas, air-conditioning (AC) systems are important contributors to overall residential electricity demand. Providing a quick, simple and effective framework to describe and compare electricity demand patterns between different houses is valuable to identify potential candidates for peak load reduction and overall energy use mitigation. In this study, we evaluate the application of daily change-point models to describe the demand patterns of residential AC systems for 45 actual houses in Austin, TX during 2013. While previous research regarding change-point models has been focused on monthly data for commercial buildings, this study extends its application to daily residential energy use. The resulting models describe a behavior where energy consumption with relation to outdoor dry-bulb temperature is negligible up until a change-point, after which AC energy use increases linearly and results in an “energy slope.” An analysis of the neighborhood shows the distribution of the AC “energy slopes” is left-skewed and centered on 0.08 kW per °C dry bulb temperature. Energy audit information found eight house characteristics to be correlated with a higher energy slope. A subsequent parametric analysis using data from the energy simulation software BEopt confirmed the direction and magnitude of the correlation. This work provides a screening tool to compare energy demand patterns of houses and target houses with the largest magnitude of energy slopes for future energy audits.
This paper presents a new method for estimating the demand response potential of residential air conditioning (A/C), using hourly electricity consumption data ("smart meter" data) from 30,000 customer accounts in Northern California. We apply linear regression and unsupervised classification methods to hourly, whole-home consumption and outdoor air temperature data to determine the hours, if any, that each home's A/C is active, and the temperature dependence of consumption when it is active. When results from our sample are scaled up to the total population, we find a maximum of 270-360 MW (95% c.i.) of demand response potential over a 1-h duration with a 4 degrees F setpoint change, and up to 3.2-3.8 GW of short-term curtailment potential. The estimated resource correlates well with the evening decline of solar production on hot, summer afternoons, suggesting that demand response could potentially act as reserves for the grid during these periods in the near future with expected higher adoption rates of solar energy. Additionally, the top 5% of homes in the sample represent 40% of the total MW-hours of DR resource, suggesting that policies and programs to take advantage of this resource should target these high users to maximize cost-effectiveness.
This paper reports on the results of a questionnaire survey on sleeping thermal environment and bedroom air conditioning in high-rise residential buildings in Hong Kong. The survey aimed at investigating the current situation of sleeping thermal environment and bedroom air conditioning, in order to gather relevant background information to develop strategies for bedroom air conditioning in the subtropics. It focused on the use patterns and types of bedroom air conditioning systems used, human factors such as the use of bedding and sleepwear during sleep, preference for indoor air temperature settings in bedrooms, ventilation control at nighttime with room air conditioner (RAC) turned on, etc. The results of the survey showed that most of the respondents would prefer a relatively low indoor air temperature at below 24 °C. Most of the respondents might however not be satisfied with the indoor air quality (IAQ) in bedrooms in Hong Kong. On the other hand, 68% of the respondents did not use any ventilation control intentionally during their sleep with their RACs turned on. A lack of knowledge of the ventilation control devices provided on window type room air conditioners (WRACs) indicated an urgent need for user education.
This paper examines the influence of dwelling and occupant characteristics on domestic electricity consumption patterns by analysing data obtained from a smart metering survey of a representative cross section of approximately 4,200 domestic Irish dwellings. A multiple linear regression model was applied to four parameters: total electricity consumption, maximum demand, load factor and time of use (ToU) of maximum electricity demand for a number of different dwelling and occupant socio-economic variables. In particular, dwelling type, number of bedrooms, head of household (HoH) age, household composition, social class, water heating and cooking type all had a significant influence over total domestic electricity consumption. Maximum electricity demand was significantly influenced by household composition as well as water heating and cooking type. A strong relationship also existed between maximum demand and most household appliances but, in particular, tumble dryers, dishwashers and electric cookers had the greatest influence over this parameter. Time of use (ToU) for maximum electricity demand was found to be strongly influenced by occupant characteristics, HoH age and household composition. Younger head of households were more inclined to use electricity later in the evening than older occupants. The appliance that showed the greatest potential for shifting demand away from peak time use was the dishwasher.
For decision makers in the electricity sector, the decision process is complex with several different levels that have to be taken into consideration. These comprise for instance the planning of facilities and an optimal day-to-day operation of the power plant. These decisions address widely different time-horizons and aspects of the system. For accomplishing these tasks load forecasts are very important. Therefore, finding an appropriate approach and model is at core of the decision process. Due to the deregulation of energy markets, load forecasting has gained even more importance. In this article, we give an overview over the various models and methods used to predict future load demands.
Energy consumption of buildings takes up about a third of Singapore's total electricity production. In this paper, we present a pioneering study to investigate the energy performance of residential buildings. Beginning with an energy survey of households, we established the air-conditioning usage patterns and modelled residential buildings for computer simulations. An ETTV equation for residential buildings was developed. Employing this equation, we demonstrated how to achieve improved energy efficiency in residential buildings. Two types of residential buildings, namely, point block and slab block, were modelled and parametric runs performed. ETTV impacts the energy consumption of residential buildings and thus lowering the ETTV will result in reduced building heat load. Results from the developed equation showed that a unit decrease in ETTV resulted in 4% and 3.5% reduction in annual cooling energy for point block and slab block residential buildings, respectively. In addition, a set of simple energy and load estimating equations were developed using computer simulation and local climatic data. These equations provided a means of estimating the annual cooling energy consumption of residential buildings in Singapore.
The association between climate change and the frequency and intensity of extreme heat events is now well established. General circulation models of climate change predict that heatwaves will become more frequent and intense, especially in the higher latitudes, affecting large metropolitan areas that are not well adapted to them. Exposure to extreme heat is already a significant public health problem and the primary cause of weather-related mortality in the U.S. This article reviews major epidemiologic risk factors associated with mortality from extreme heat exposure and discusses future drivers of heat-related mortality, including a warming climate, the urban heat island effect, and an aging population. In addition, it considers critical areas of an effective public health response including heat response plans, the use of remote sensing and GIS methodologies, and the importance of effective communications strategies.
A global coupled climate model shows that there is a distinct geographic pattern to future changes in heat waves. Model results for areas of Europe and North America, associated with the severe heat waves in Chicago in 1995 and Paris in 2003, show that future heat waves in these areas will become more intense, more frequent, and longer lasting in the second half of the 21st century. Observations and the model show that present-day heat waves over Europe and North America coincide with a specific atmospheric circulation pattern that is intensified by ongoing increases in greenhouse gases, indicating that it will produce more severe heat waves in those regions in the future.
The parametric (or model-based) methods of signal processing often require not only the estimation of a vector of real-valued parameters but also the selection of one or several integer-valued parameters that are equally important for the specification of a data model. Examples of these integer-valued parameters of the model include the orders of an autoregressive moving average model, the number of sinusoidal components in a sinusoids-in-noise signal, and the number of source signals impinging on a sensor array. In each of these cases, the integer-valued parameters determine the dimension of the parameter vector of the data model, and they must be estimated from the data.
Adapting to climate change: the remarkable decline in the US temperature-mortality relationship over the twentieth century
- Barreca