• Energy Research
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Abstract This study demonstrates the possibility to use energy-cost optimal building designs based on life cycle approach. There is a lack of studies that cover the comprehensive assessment of both embodied energy (EE) and operational energy (OE) in a single optimization problem. The primary goal of the current study is to compare the optimized results of using OE+EE together and OE only. The optimization is performed on a case study building (townhouse) in Finland with three structural alternatives (i.e., reinforced concrete (RC); cross-laminated timber (CLT) and Steel). Different options for insulation thickness of external wall, roof, floor and window types were considered as decision variables as the scope of the present study is on building envelope. The objectives of the optimization are to minimize life cycle energy (LCE) and life cycle costs (LCC). The LCE difference between the most and least energy efficient solution on the pareto front is greater in case of the OE optimization, compared to OE+EE optimization. For all studied structures, the EE of the optimal solutions from OE+EE optimization ranges 16%–23% of LCE. Many of the non-dominated optimal solutions obtained from the OE+EE optimization shows a higher U-value for the building envelope components compared to the optimal solutions from the OE optimization. A relationship between both OE and EE with the overall thermal resistance of the building envelope is discussed to obtain a deeper understanding of such differences in U-value for the optimal solutions obtained from the OE+EE and OE optimization.

The operation of a building connects the building, its performance and its end-users. When a mismatch occurs between users' expectations and operations processes, then complaints can arise. The use of enhanced complaint handling processes is investigated to ascertain whether this can help diagnose performance problems. Two LEED Platinum-rated office buildings form the basis for a case study of the components that make up an enhanced complaint handling process. Some of the social dynamics of complaints in buildings are consequently discussed. It is found that an enhanced complaint handling approach could contribute to a form of ongoing commissioning that goes beyond primarily reactive or dismissive treatments of complaints. Le fonctionnement d'un immeuble etablit des rapports entre l'immeuble, ses performances et ses utilisateurs finaux. Lorsqu'un decalage se produit entre les attentes des utilisateurs et les processus d'exploitation, des plaintes peuvent alors s'elever. L'utilisation de processus ameliore...

Global wind resources greatly exceed current electricity demand and the levelized cost of energy from wind turbines has shown precipitous declines. Accordingly, the installed capacity of wind turbines grew at an annualized rate of about 14% during the last two decades and wind turbines now provide ~6–7% of the global electricity supply. This renewable electricity generation source is thus already playing a role in reducing greenhouse gas emissions from the energy sector. Here we document trends within the industry, examine projections of future installed capacity increases and compute the associated climate change mitigation potential at the global and regional levels. Key countries (the USA, UK and China) and regions (e.g., EU27) have developed ambitious plans to expand wind energy penetration as core aspects of their net-zero emissions strategies. The projected climate change mitigation from wind energy by 2100 ranges from 0.3–0.8 °C depending on the precise socio-economic pathway and wind energy expansion scenario followed. The rapid expansion of annual increments to wind energy installed capacity by approximately two times current rates can greatly delay the passing of the 2 °C warming threshold relative to pre-industrial levels. To achieve the required expansion of this cost-effective, low-carbon energy source, there is a need for electrification of the energy system and for expansion of manufacturing and installation capacity.

We assessed the physical, chemical and toxicological characteristics of particulate emissions from four light-duty gasoline direct injection vehicles when operated over the LA92 driving cycle. Our results showed that particle mass and number emissions increased markedly during accelerations. For three of the four vehicles tested, particulate matter (PM) mass and particle number emissions were markedly higher during cold-start and the first few accelerations following the cold-start period than during the hot running and hot-start segments of the LA92 cycle. For one vehicle (which had the highest emissions overall) the hot-start and cold-start PM emissions were similar. Black carbon emissions were also much higher during the cold-start conditions, indicating severe fuel wetting leading to slow evaporation and pool burning, and subsequent soot formation. Particle number concentrations and black carbon emissions showed large reductions during the urban and hot-start phases of the test cycle. The oxidative potential of PM was quantified with both a chemical and a biological assay, and the gene expression impacts of the PM in a macrophage model with PCR (polymerase chain reaction) and ELISA (enzyme-linked immunosorbent assay) analyses. Inter- and intra-vehicle variability in oxidative potential per milligram of PM emitted was relatively low for both oxidative assays, suggesting that real-world emissions and exposure can be estimated with distance-normalized emission factors. The PCR response from signaling markers for oxidative stress (e.g., NOX1) was greater than from inflammatory, AhR (aryl hydrocarbon receptor), or MAPK (mitogen-activated protein kinase) signaling. Protein production associated with inflammation (tumor necrosis factor alpha-TNFα) and oxidative stress (HMOX-1) were quantified and displayed relatively high inter-vehicle variability, suggesting that these pathways may be activated by different PM components. Correlation of trace metal concentrations and oxidative potential suggests a role for small, insoluble particles in inducing oxidative stress.

A statement in this recently published paper makes a point that is largely at odds with the main point of the paper that is cited. Stating that higher air temperatures lead to greater evapotranspiration is an oversimplification; the true story is more complex. Although this is by no means central to the conclusions of the paper being commented on, we have demonstrated the danger in taking too literally the idea that air temperature determines potential evapotranspiration.

Improvements in energy efficiency and production of renewable energy hold significant potential for reducing greenhouse gas emissions of housing, which accounts for 14% of global greenhouse gas emissions. In our research, we focused on the willingness of owners of detached houses to adopt renewable energy production systems of their own, and we examined perceived barriers to adopting these systems. The research was conducted using a survey and a life cycle assessment model. The survey covered three residential areas in Lahti, Finland, and the potential reductions in greenhouse gas emissions were estimated using a life cycle assessment model based on the survey results. The barriers to transformation were identified as a lack of knowledge in the following three areas: (1) the possible annual savings attained; (2) the costs of implementing energy efficiency and renewable energy production solutions; and (3) the technologies used in renewable energy production. The greenhouse gas emission reductions in the residential areas surveyed would amount to approximately 15% if the consumers implemented the solutions they considered.

Abstract In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.

Abstract Kenya has made considerable policy efforts to expand its renewable energy portfolio to meet energy demand and mitigate greenhouse gas (GHG) emissions. Development of proper policies requires a robust framework for analyzing the benefits of renewable energy investments. Towards this end, this study applied a choice experiment analysis to determine how attributes (type of energy, ownership, impact on environment, distance and visibility, community job creation, and yearly renewable energy tax) impact the public willingness to pay for renewable energy development in Kenya. A nationwide survey of 1020 households was conducted in nine counties using conditional logit (MNL) and random parameter logit (RPL) frameworks. The results reveal that the Kenyan public places a high value on environmental impact, followed by type of renewable energy and community job creation, respectively. On the other hand, respondents do not place much emphasis on ownership or distance and visibility. Policy simulation suggests that while renewable energy adoption is highly valued by households, the total willingness to pay is not enough to cover the higher capital cost for the development of various renewable energy technologies.