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Aotearoa New Zealand (NZ) homes are cold by international standards, with many failing to achieve temperatures recommended by WHO housing and health guidelines. Despite strong evidence of seasonal exacerbations in Chronic Obstructive Pulmonary Disease (COPD), there has been little examination of the effect of household warmth, or housing quality on COPD outcomes. The Warm Homes for Elder New Zealanders (WHEZ) study aimed to see if subsidising electricity costs would improve the health outcomes of older people with COPD. Previous analysis showed a modest, typically 2-10% dependent on prior usage, increase in electricity use among the subsidised households. Participants aged over 55 with doctor-diagnosed COPD were recruited from three regional centres, and where possible their dwelling was insulated after enrolment. A single-blinded randomised controlled trial of the effect of an electricity voucher (NZ$500) on health care usage during winter was carried out in three locations across New Zealand. The primary outcome was exacerbations treated with antibiotics, and/or corticosteroids. The Clinical Trial Registration is NCT01627418. Of the 520 participants assigned to a wave, partial or better data was achieved for 424; 215 of those were randomised to the early intervention group, and 209 to receive the intervention later. Despite the modest increase in energy use by study households, reported previously, there was no significant difference between study arms in the frequency of exacerbation of COPD (0.089, p=0.5875, 95% CI -1.406-1.584) nor hospitalisations. An exploratory analysis suggested a lower mortality among participants assigned to receive the intervention first (OR 0.310, p=0.0175, 95% CI 0.118-0.815). This study showed little effect of winter electricity vouchers on exacerbations of COPD. However, such vouchers increased energy use and may have reduced overall mortality. A larger study, particularly with susceptible subpopulations, is recommended to examine this mortality impact further.

Estimates of global economic damage caused by carbon dioxide (CO 2 ) emissions can inform climate policy 1 – 3 . The social cost of carbon (SCC) quantifies these damages by characterizing how additional CO 2 emissions today impact future economic outcomes through altering the climate 4 – 6 . Previous estimates have suggested that large, warming-driven increases in energy expenditures could dominate the SCC 7 , 8 , but they rely on models 9 – 11 that are spatially coarse and not tightly linked to data 2 , 3 , 6 , 7 , 12 , 13 . Here we show that the release of one ton of CO 2 today is projected to reduce total future energy expenditures, with most estimates valued between −US$3 and −US$1, depending on discount rates. Our results are based on an architecture that integrates global data, econometrics and climate science to estimate local damages worldwide. Notably, we project that emerging economies in the tropics will dramatically increase electricity consumption owing to warming, which requires critical infrastructure planning. However, heating reductions in colder countries offset this increase globally. We estimate that 2099 annual global electricity consumption increases by about 4.5 exajoules (7 per cent of current global consumption) per one-degree-Celsius increase in global mean surface temperature (GMST), whereas direct consumption of other fuels declines by about 11.3 exajoules (7 per cent of current global consumption) per one-degree-Celsius increase in GMST. Our finding of net savings contradicts previous research 7 , 8 , because global data indicate that many populations will remain too poor for most of the twenty-first century to substantially increase energy consumption in response to warming. Importantly, damage estimates would differ if poorer populations were given greater weight 14 . Using global data, econometrics and climate science to estimate the damages induced by the emission of one ton of carbon dioxide, climate change is projected to increase electricity spending but reduce overall end-use energy expenditure.

Background Chronic Obstructive Pulmonary Disease (COPD) is of increasing importance with about one in four people estimated to be diagnosed with COPD during their lifetime. None of the existing medications for COPD has been shown to have much effect on the long-term decline in lung function and there have been few recent pharmacotherapeutic advances. Identifying preventive interventions that can reduce the frequency and severity of exacerbations could have important public health benefits. The Warm Homes for Elder New Zealanders study is a community-based trial, designed to test whether a NZ$500 electricity voucher paid into the electricity account of older people with COPD, with the expressed aim of enabling them to keep their homes warm, results in reduced exacerbations and hospitalisation rates. It will also examine whether these subsidies are cost-beneficial. Methods Participants had a clinician diagnosis of COPD and had either been hospitalised or taken steroids or antibiotics for COPD in the previous three years; their median age was 71 years. Participants were recruited from three communities between 2009 to early 2011. Where possible, participants’ houses were retrofitted with insulation. After baseline data were received, participants were randomised to either ‘early’ or ‘late’ intervention groups. The intervention was a voucher of $500 directly credited to the participants’ electricity company account. Early group participants received the voucher the first winter they were enrolled in the study, late participants during the second winter. Objective measures included spirometry and indoor temperatures and subjective measures included questions about participant health and wellbeing, heating, medication and visits to health professionals. Objective health care usage data included hospitalisation and primary care visits. Assessments of electricity use were obtained through electricity companies using unique customer numbers. Discussion This community trial has successfully enrolled 522 older people with COPD. Baseline data showed that, despite having a chronic respiratory illness, participants are frequently cold in their houses and economise on heating. Trial Registration The clinical trial registration is http://NCT01627418

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.

Providing optimal operating conditions is one of the major challenges for effective heating or cooling systems. Moreover, proper adjustment of the heat transfer fluid is also important from the viewpoint of the correct operation, maintenance, and cost efficiency of these systems. Therefore, in this paper, a detailed review of recent work on the subject of conventional and novel heat transfer fluid applications is presented. Particular attention is paid to the novel nanoparticle-based materials used as heat transfer fluids. In-depth comparison of environmental, technical, and economic characteristics is discussed. Thermophysical properties including thermal conductivity, specific heat, density, viscosity, and Prandtl number are compared. Furthermore, the possible benefits and limitations of various transfer fluids in the fields of application are taken into account.

To improve the energy economic efficiency of Data Centers (DCs) in steel enterprises, a centralized heating scheme for waste heat recovery based on the Co-ah cycle is proposed. This scheme establishes a thermoelectric connection between the self-owned power plant of the steel enterprise and the DC, creating a waste heat recovery centralized heating system for a 15 MW DC. The energy efficiency indicators, environmental benefits, economic feasibility, and adaptability of the system are evaluated. The results show that the system can effectively recover waste heat from the DC, significantly reducing cooling electricity consumption during the heating season and decreasing original heating steam consumption by about 25%. Compared to DC using free cooling, the annual operating cost is reduced by 9.7%, with a dynamic payback period for equipment of 6–7 years. The system saves 3,671.5 tons of standard coal and reduces CO 2 emissions by 1,615 tons annually compared to DC using isolated free cooling and traditional heating systems. The Improved Power Usage Effectiveness (PUE’) of the system is 1.195, and the Energy Reuse Effectiveness (ERE) is 0.769, outperforming the free cooling’s index of 1.341, although Exergy Reuse Effectiveness (ExRE) is slightly higher than that of free cooling. This system offers mutual benefits for self-owned power plant, DC, and heating companies, achieving a win-win operational state through suitable energy trading prices. The research conclusion provides valuable reference for the future investment and operation of DCs in steel enterprises.

The energy-exergy and environ-economic (4E) analysis was conducted on a solar still with and without a hybrid thermal energy storage system (TESS) and a solar air heater. The proposed solar still was modified by integrating a rectangular aluminium box filled with paraffin wax and black gravel as the TESS and coupled with a solar air heater. Paraffin wax was selected due to its widespread availability and proven effectiveness in accelerating desalination, improving process uniformity, and maintaining optimal temperature levels. Throughout the experiments, meticulous data on mass loss, air velocity, and temperature were recorded for both conditions. The daily energy efficiency varies from 40.80% to 31.72%, showing a reduction rate with increased water depth. Estimates were made on the average exergy efficiency, losses, outflow, and inflow for the solar still. These were done for both setups. The analysis revealed that CO 2 mitigation and credit were more favorable with the TESS. Furthermore, the Energy Payback Time (EPBT) for the hybrid heat storage-based single-slope solar still coupled with a solar air heater is 1.87 years. On the other hand, EPBT values for the hybrid heat storage single-slope solar still and the conventional single-slope solar still were 1.65 years and 0.95 years, respectively. Integrating a thermal energy storage system and solar air heater significantly improved the performance and sustainability of the solar still for desalination, making it a more efficient and environmentally friendly option for freshwater production.

Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for “virtual” energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings’ time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building’s daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

Smart grid is one of the main applications of the Internet of Things (IoT) paradigm. Within this context, this paper addresses the efficient energy consumption managementof heating, ventilation, and air conditioning (HVAC) systems in smart grids with variable energy price. To that end, first, we propose an energy scheduling method that minimizes the energy consumption cost for a particular time interval, taking into account the energy price and a set of comfort constraints, that is, a range of temperatures according to user’s preferences for a given room. Then, we propose an energy scheduler where the user may select to relax the temperature constraints to save more energy. Moreover, thanks to the IoT paradigm, the user may interact remotely with the HVAC control system. In particular, the user may decide remotely the temperature of comfort, while the temperature and energy consumption information is sent through Internet and displayed at the end user’s device. The proposed algorithms have been implemented in a real testbed, highlighting the potential gains that can be achieved in terms of both energy and cost.