Search

• The paper has a focus on energy consumption habits and trends using statistical methods. • Occupants' behavior is monitored using smart metering system and awareness surveys. • The impact of a serious game on occupant's behavior is evaluated. The paper has a focus on energy consumption habits, trends and intervention strategies in residential buildings, mainly through the serious game approach with a combination of direct consumer feedback through smart metering. More than 150 homes in France and Spain have been involved in the research experiment and the consumption habits of approximately 50 homes were deeply analyzed. The applied methods, processes, results and findings of the monitoring data analysis are presented in the paper with two aims. First, consumption profiles and trends were determined for apartment homes with regard to heating, domestic hot water and electric consumption. Second, the impact of a serious game experiment was assessed comparing energy consumption, indoor air temperature and users' habits (based on questionnaires) before and after launching the experiment. [ABSTRACT FROM AUTHOR]

• Overheating regulations and calculation methods in 26 European countries were compared. • Most of the existing calculation methods are outdated and do not fit climate-proof buildings. • France requires a mixed-mode operation of naturally ventilated households. • The UK developed a heatwave-based calculation approach. • Comfort-based, multi-zonal, and time-integrated calculation approaches are needed. With the ongoing significance of overheating calculations in the residential building sector, building codes such as the European Energy Performance of Building Directive (EPBD) are essential for harmonizing the indicators and performance thresholds. This paper investigates Europe's overheating calculation methods, indicators, and thresholds and evaluates their ability to address climate change and heat events. e study aims to identify the suitability of existing overheating calculation methods and propose recommendations for the EPBD. The study results provide a cross-sectional overview of twenty-six European countries. The most influential overheating calculation criteria are listed the best approaches are ranked. The paper provides a thorough comparative assessment and recommendations to align current calculations with climate-sensitive metrics. The results suggest a framework and key performance indicators that are comfort-based, multi-zonal, and time-integrated to calculate overheating and modify the EU's next building energy efficiency regulations. The results can help policymakers and building professionals to develop the next overheating calculation framework and approach for the future development of climate-proof and resilient residential buildings. [ABSTRACT FROM AUTHOR]

Meeting energy needs and ensuring ecological sustainability are important tasks for policymakers. In particular, changing the energy mix used in daily electricity generation can have a major effect on ecological conditions, and this is a previously neglected research topic. Therefore, this paper aims to investigate the influence of daily electricity generation from nuclear power and renewables on the achievement of carbon neutrality targets in the four largest nuclear power countries (i.e., the U.S., China, France, and Russia). The study applies the wavelet-transform coherence (WTC), quantile-on-quantile regression (QQR), and Granger causality in quantiles (GCQ) approaches for the period from January 2, 2019 to December 29, 2022. The outcomes present that electricity generation from nuclear energy reduces carbon (CO 2) emissions only in Russia, while hydropower generation does not contribute to CO 2 emission reduction in any country. Solar electricity generation decreases CO 2 emissions in different quantiles in the U.S. and Russia, while electricity generation from wind power reduces CO 2 emissions in the U.S. and France. The empirical outcomes also reveal that expanding solar, wind, and nuclear power in the energy mix can enhance the potential of the U.S., France, and Russia to achieve carbon neutrality and secure a sustainable future. However, China is using nuclear and renewables inefficiently regarding carbon neutrality. Therefore, the empirical evidence for China is highly controversial compared to former studies. [Display omitted] • The study examines renewable and nuclear electricity effect on carbon neutrality. • The study focuses on top four major nuclear power countries. • The study applies nonlinear approaches by using high-frequency (daily) data. • Renewable and nuclear electricity have time, frequency, & quantile-varying effect. • The best electricity source change according to each country. [ABSTRACT FROM AUTHOR]

Abstract To the best of our knowledge, there is no econometric study on the causal relationship between renewable or fossil energies and terrorism, nor on terrorism in France. This paper tries to fill this gap and considers other variables as economic growth and trade openness. The autoregressive distributed lag approach and Granger causality are used for annual data on France covering the period 1980–2015. The Wald test confirms the existence of long-run relationships between the considered variables. Granger causality reveals long-run bidirectional causalities between all the variables. Interestingly, in the short-run, there are unidirectional causalities running from renewable energy to terrorism, and from terrorism to trade openness, and there is bidirectional causality between terrorism and economic growth. Long-run parameter estimates show that more renewable energy consumption and more trade increase both terrorism and economic growth. However, economic growth reduces terrorism in the long-run and appears as a powerful tool to combat terrorism in France. In addition, France is recommended to continue encouraging the use of renewable energies, and at the same time, to make more diplomatic efforts to resolve political and military conflicts in the world, and particularly in the Middle East. Highlights • There is long-run bidirectional causality between renewable energy and terrorism. • There is long-run bidirectional causality between fossil energy and terrorism. • There is short-run unidirectional causality running from renewable energy to terrorism. • In the long run, renewables increase terrorism and economic growth. • In the long run, economic growth reduces terrorism and increases renewable energy. [ABSTRACT FROM AUTHOR]

Highlights • We tackle the real-time Energy Consumption Minimization Problem. • It minimizes train energy consumption and delay, with given routing and precedences. • We propose an algorithm based on mixed-integer linear programming. • We test it on a critical French control area where traffic is dense. • The results show that good solutions can be often found in real-time. Abstract A new timetable must be calculated in real-time when train operations are perturbed. Although energy consumption is becoming a central issue both from the environmental and economic perspective, it is usually neglected in the timetable recalculation. In this paper, we formalize the real-time Energy Consumption Minimization Problem (rtECMP). It finds in real-time the driving regime combination for each train that minimizes energy consumption, respecting given routing and precedences between trains. In the possible driving regime combinations, train routes are split in subsections for which one of the regimes resulting from the Pontryagin's Maximum Principle is to be chosen. We model the trade-off between minimizing energy consumption and total delay by considering as objective function their weighted sum. We propose an algorithm to solve the rtECMP, based on the solution of a mixed-integer linear programming model. We test this algorithm on the Pierrefitte-Gonesse control area, which is a critical area in France with dense mixed traffic. The results show that the problem is tractable and an optimal solution of the model tackled can often be found in real-time for most instances. [ABSTRACT FROM AUTHOR]

The accelerating depletion of fossil fuel reserves and the growing awareness about climate issues have put forth a plethora of interesting approaches that attempt to tackle the crucial problem of energy saving, specifically in buildings, known as a major energy consumer. Existing approaches tackle the problem of energy efficiency in buildings by proposing model-based approaches such as knowledge models (thermal, CO 2 , cost, etc.) and regressive models. However, different factors make the building of knowledge models particularly challenging, including the very complicated interaction between several heterogeneous phenomena that can impact the use of energy in buildings like the buildings envelope characteristics, their positions, the weather conditions, but also the occupant's behavior is a critical issue in the process. More Recently, techniques from machine learning (ML) to support energy saving in buildings gained increased interest. They learn from collected historical data a model that forecasts the future energy behavior of the building. Although occupant's behavior to save energy in the building is far from trivial, has received less attention from these studies. This paper takes on this challenge and proposes an energy management system based on historical data thanks to case-based reasoning approach. We guide the occupant by proposing an action plan (opening/closing of doors/windows, etc.) to help him in the process of improving his indoor comfort (thermal, air quality, luminosity, etc.) without using more energy if not using less. To encourage the occupant to trust the inference mechanism learnt and cooperate with the energy management system, this approach generates explanations arguing the proposed action plan. We assess the performance of our energy management technique on real-word data collected from a research building at the University of Grenoble, France. • Model-based systems are difficult to develop. • Case-based reasoning models building behavior without the need for a building model. • Explanation fosters cooperation between the user and the energy management system. [ABSTRACT FROM AUTHOR]

Peak power consumption is becoming an increasing problem worldwide and particularly in France during the late afternoon winter periods. This paper describes the experimental and numerical studies carried out to investigate the potential of a PCM (phase change material) – Air heat exchanger, conceived for load shifting purposes. The objective is to possess the necessary tools for the development of advanced control strategies taking into account various factors: peak power reduction, thermal comfort and indoor air quality. The experimental approach involved the construction of a heat exchanger coupled to an experimental cell. The heat storage system is composed of a set of PCM (paraffin) plates, embedded in the exchanger and integrated in a ventilation system. In parallel, a numerical model was developed using the apparent heat capacity approach and the finite differences method. Furthermore, a series of characterization tests was conducted and the experimental data were used for model calibration and validation. The model was then coupled with a building simulation program, presenting good agreement between the integrated model prediction and the experimental data. Finally, a preliminary control strategy of the test cell is presented along with results demonstrating the capabilities of such system for peak load management. [ABSTRACT FROM AUTHOR]

Saving energy is crucial for all sectors following the new framework presented by the European Commission to drive continued progress towards a low-carbon economy. Many studies focus on the residential sector, transport and energy-intensive industries, but there is a lack of tools to help decision makers in non-energy intensive industry (NEI). This paper presents the first bottom-up energy model developed for this sector. This prospective modeling enables us to analyze the impact of heat recovery using heat pumps (HP) in industrial processes up to 2020 in the French food and drink industry (F&D), the biggest NEI sector. The technology has high potential in this sector and may be eligible for Energy Saving Certificates. Our model determines the differentiated cost for energy savings in response to incentive policies under the ESC mechanism at a 4-digit level of NACE classification. Sensitivity analyses also show how gas prices and electricity carbon footprints impact on HP penetration. Our study of this particular sector shows that the model could be a useful decision-making tool for assessing potential energy savings and could be extended to other sectors of NEI industry for more efficient subsectoral screening. [ABSTRACT FROM AUTHOR]

In both the US and French building sectors, significant efforts have been undertaken to reduce the environmental impacts of new constructions notably their fossil energy consumption and related carbon emissions. This paper summarizes the results of a comparative evaluation of optimal design specifications for office buildings for selected locations in both US and France using life cycle cost analysis approach. First, an overview of both the US and French approaches for developing building energy efficiency standards and design of high performance buildings is outlined. Then, a general approach for design optimization of energy efficient office buildings is presented. Finally, selected results of the optimal analysis are discussed. In particular, it is found that optimizing life cycle costs (LCC) resulted on average in 30% in primary energy savings for office buildings located in the US and 40% savings in France. However, it is found that LCC-based optimal building designs do not meet the French energy efficiency RT 2012 standard. When accounting for typical operating conditions, photovoltaic (PV) panels are needed in order to comply with the RT 2012 required thresholds for the total primary energy consumption of the office buildings located in the French cities considered in the analysis. [ABSTRACT FROM AUTHOR]

This paper details a manual calibration of a 132 zones, highly monitored 5 434 m2 office building located in France. The calibration is conducted over one year of simulation at a five minute time-step. The model is mostly fed with input files containing measured data for each simulation time-step. First simulation results show that the model cannot be considered as being calibrated considering usual guidelines based on statistical indicators calculated on monthly energy consumption. Instead of going through a classical tuning process to match measured and simulated monthly energy consumption data, the choice was made to focus on explaining those discrepancies using available building information and monitoring data. A first dynamical heating and cooling powers analysis has been made, resulting in good correlation with measured values and explaining most of the discrepancies previously noticed, followed by a dynamical temperature analysis performed at the zone scale, and completed by a global statistical analysis at the building scale for the 132 zones of the building. Results showed very good agreement between measured and simulated temperature values with 90% of data points ranging between −1.72 °C and 2.02 °C of error. This demonstrate that a model that at first glance does not perform well considering particular statistical indicators can show an accurate dynamical behavior and a good performance when other variables and performance criteria are considered. This is followed by a discussion of the possible model improvements along with the limitations of statistical analysis compared to dynamic physical analysis for calibration performance assessment. The paper concludes with several proposals to improve the guidelines for calibration procedures involving the use of detailed monitoring data. [ABSTRACT FROM AUTHOR]

The EU27 countries exert significant influence on the global patterns of the CO 2 -Emissions-Water-Energy (CWE) nexus. However, whether the associated benefits are similar for all countries is unclear. In this paper, an EU27 multiregional input-output model, at a sector level to identify the inter-regional and -sectoral CWE flows, and clarify the regional, sectoral and worldwide patterns of EU27 CWE network is presented. The results revealed an environmental inequality across the EU27 and impacts on the rest of the world. The EU27 countries contributed 1.4 Gt less CO 2 emissions, 64.5 Gm3 less water utilisation and 49 EJ less energy consumption, compared to the rest of the world while generating the equivalent economic output in 2014. This has dramatic effects upon the global environment. Germany, France and Italy are the biggest beneficiaries in the CWE nexus in the EU27. It is recommended that the EU27 provide more technical support to upstream countries in the EU and elsewhere to strive to improve their resource utilisation efficiencies. • The critical Carbon-Water-Energy (CWE) flows across the EU27 are analysed. • The regional, sectoral and worldwide patterns of EU27 CWE network are clarified. • An environmental inequality across the EU27 and impacts on the rest of the world. • Germany, France and Italy are the biggest beneficiaries in the CWE network of EU27. [ABSTRACT FROM AUTHOR]

This article develops a quantile regression model to measure the magnitude of the direct rebound effect for residential electricity demand based on micro-level data from an original survey of a representative sample of 2356 French households. We explore the rebound effect for different groups of residents. Traditionally, debate has focused on decreased prices for energy services due to efficiency improvements and their effects on energy demand. The methodological innovation provided by this paper is that it implements two theoretical estimation strategies: (i) energy efficiency elasticity of the demand for energy services, and (ii) price elasticity of the demand for energy services. Our findings reject the hypothesis of a backfire effect in the context of residential electricity use. Specifically, based on energy efficiency elasticity, we provide evidence of a rebound effect of between 72% and 86% but with substantial heterogeneity among consumption quantiles. Our results show that the direct rebound effect has critical relevance for energy efficiency policy in the face of energy invisibility in France. • The rebound effect of residential electricity demand is estimated • The recent French PHEBUS survey and quantile regression are used • The results provide evidence of a rebound effect of between 38% and 86% • Findings reveal substantial heterogeneity among consumption quantile • The analysis suggests that the rebound effect has critical relevance for energy efficiency policymaking [ABSTRACT FROM AUTHOR]

The aim of this paper is to provide an overview about the distribution of the environmental impacts arising from different domestic functions (i.e. storing and preparing food, washing dishes, watching television, reading, personal cleaning, washing, drying and ironing clothes, home cleaning, heating, cooling, lighting and mobility) typically performed within a common family home. The method has general validity but for reasons related to the availability of data in the literature it has been applied by way of example only in three EU countries: Italy, Germany and France. The study was performed by using Life Cycle Assessment (LCA) in accordance with international standard ISO 14067 for determining the carbon footprint of different alternative domestic components, mainly appliances, for each function, by exclusively exploiting data from scientific literature. The functional unit is defined comprising all most common referred domestic activities of a family of three members within a house of 100 m2. The study identified an optimal configuration and a worse one of the domestic components in terms of carbon footprint, showing how a wise choice of these can greatly affect the overall impact by reducing it compared to the worst by more than 22% in Italy, 45% in Germany and 56% in France. The average impacts between the optimal and the worst configurations of Germany are higher than Italy (+27%) and France (+44%). Considering the impacts among the domestic functions in the average configuration, mobility was the most impactful in all the three countries (35–48%), followed by heating (17–26%), personal cleaning (10–13%) and washing dishes (8–13%), while cooling is consistent only in Italy (13%), against 5% in Germany and 2% in France. The study also allowed to identify some generic criteria for defining the optimal configuration: the increasing in energy efficiency, the choice of the least impacting energy source depending on the geographical location, ensuring water savings and the early replacement of older domestic components. Finally, by comparing some common measures for improving the domestic sustainability, these criteria proved to be more effective than solar systems and improved electricity mix. The provided outcomes may be used by manufacturers for improving their product in a more sustainable way as well as by legislator and end user, respectively for boosting and choosing the greener domestic components. Image 1 • LCA for estimating domestic environmental impacts based on scientific literature. • Application to three EU countries: Italy, Germany and France. • Assessing Carbon Footprint of different alternative domestic components for each domestic function. • Determining the optimal and the worst configuration of domestic components to minimize the impacts. • Comparing possible improvements for minimizing carbon footprint. [ABSTRACT FROM AUTHOR]