Search results

Low flow bikini solar combisystems and high flow tank‐in‐tank solar combisystems have been studied theoretically. The aim of this paper is to study which of these two solar combisystem designs is suitable for different houses. The thermal performance of solar combisystems based on the two different heat storage types is compared.

The thermal performance of Low flow bikini solar combisystems and high flow tank‐in‐tank solar combisystems is calculated with the simulation program TRNSYS. Two different TRNSYS models based on measurements were developed and used.

Based on the calculations it is concluded that low flow solar combisystems based on bikini tanks are promising for low energy buildings, while solar combisystems based on tank‐in‐tank stores are attractive for the houses with medium heating demand and old houses with high heating demand.

Many different Solar Combisystem designs have been commercialized over the years. In the IEA‐SHC Task 26, twenty one solar combisystems have been described and analyzed. Maybe the mantle tank approach also for solar combisystems can be used with advantage? This might be possible if the solar heating system is based on a so‐called bikini tank. Therefore, the new developed solar combisystems based on bikini tanks is compared to the tank‐in‐tank solar combisystems to elucidate which one is suitable for three different houses with low energy heating demand, medium and high heating demand.

An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS).

Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data.

The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine.

The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.

This chapter tests theoretically and empirically the existence of a stable relationship between energy consumption and CO2 emissions. Based on microeconomics and physics, a model has been specified and applied to annual data for twenty countries, which representing 61 percent of the world’s population in 2018, over the period 1995–2015. The data are from the International Energy Agency (2019) and econometric techniques including panel data and causality tests have been used. The results indicate that there is a causal relationship between energy consumption and CO2 emissions. In general, consumers cannot directly change emissions caused by production processes, but they can act on emissions caused by their own domestic energy consumption. Approximately three quarters of domestic energy consumption is due to heating and domestic hot water consumption. Taking into account the lower emissions and the lower economic cost of the initial investment, four potential energy systems have been selected for use in heating and domestic hot water. Their social returns have been assessed across nine of the twenty countries in the sample over a lifecycle of 25 years from 2018: France, Portugal, Ireland, Spain, Iceland, Germany, United Kingdom, Morocco and the United States. Cost-benefit analysis techniques have been used for this purpose and the results indicate that the use of thermal water, where applicable, is the most socially profitable system among the proposed systems, followed by natural gas. The least socially profitable systems are those using electricity.

This paper aims to develop a forecasting tool for the automatic assessment of both environmental and economic benefits resulting from low-carbon investments in the real estate sector, especially when applied in large building stocks. A set of four artificial neural networks (NNs) is created to provide a fast and reliable estimate of the energy consumption in buildings due to heating, hot water, cooling and electricity, depending on some specific buildings’ characteristics, such as geometry, orientation, climate or technologies.

The assessment of the building’s energy demand is performed comparing the as-is status (pre-retrofit) against the design option (post-retrofit). The authors associate with the retrofit investment the energy saved per year, and the net monetary saving obtained over the whole cost after a predetermined timeframe. The authors used a NN approach, which is able to forecast the buildings’ energy demand due to heating, hot water, cooling and electricity, both in the as-is and in the design stages. The design stage is the result of a multiple attribute optimization process.

The approach here developed offers the opportunity to manage energy retrofit interventions on wide property portfolios, where it is necessary to handle simultaneously a large number of buildings without it being technically feasible to achieve a very detailed level of analysis for every property of a large portfolio.

Among the major accomplishments of this research, there is the creation of a methodology that is not excessively data demanding: the collection of data for building energy simulations is, in fact, extremely time-consuming and expensive, and this NN model may help in overcoming this problem. Another important result achieved in this study is the flexibility of the model developed. The case study the authors analysed was referred to one specific stock, but the results obtained have a more widespread importance because it ends up being only a matter of input-data entering, while the model is perfectly exportable in other contexts.

The purpose of this paper is to identify the impact of traditionally unmonitored energy sources and sinks on assessment of the as-built thermal performance of occupied homes. The analysis aims to demonstrate the potential scale of uncertainties introduced in a heat balance estimation of the heat transfer coefficient (HTC) when using in-use monitored data.

Energy flows for two UK homes – one a 1930s dwelling with high heat loss, the second a higher-performing 2014-built home – are predicted using the UK Government’s standard assessment procedure (SAP) and visualised using Sankey diagrams. Selected modelled energy flows are used as inputs in a quasi-steady state heat balance to calculate in-use HTCs as if from measured data sets gathered in occupied homes. The estimated in-use HTCs are compared against SAP-calculated values to illustrate the impact of including or omitting various heat sources and sinks.

The results demonstrate that for dwellings with low heat loss, the increased proportion of heating demand met by unmetered internal and solar gains informs a greater sensitivity of a heat balance estimation of the HTC to their omission. While simple quasi-steady state heat balance methods may be appropriate for dwellings with very high heat loss, alternative approaches are likely to be required for those with lower heat loss.

A need to understand the impacts of unmetered heat flows on the accuracy with which a building’s thermal performance may be inferred from in-use monitored data is identified: this paper illustrates the scale of these impacts for two homes at opposite ends of the energy performance scale.

Residential buildings in Greece constitute an important portion of the existing building stock. Furthermore, most of these buildings were built prior to the first Thermal Insulation Code of 1981. The article focuses on existing, typical residences built after 1920, which are found mostly in suburban areas and settlements all around Greece. The purpose of the research is to evaluate the effect of simple bioclimatic interventions focused on the improvement of their diurnal, inter-seasonal and annual thermal performance.

The applied strategies include application of thermal insulation in the building shell and openings, passive solar systems for the heating period and shading and natural ventilation for the summer period. The effect of the strategies is analysed with the use of building energy analysis. The simulation method was selected because it provides the possibility of parametric analysis and comparisons for different proposals in different orientations.

The results show that the increased thermal mass of the construction is the most decisive parameter of the thermal behaviour throughout the year.

The typical residences under investigation are often found in urban and/or suburban surroundings. These mostly refer to free-standing buildings situated, which, in many cases, do not have the disadvantages and limitations that the geometrical characteristics of densely built urban locations impose on incident solar radiation (e.g. overshadowing during the winter) and air circulation (e.g. reduce natural ventilation during the summer). Nevertheless, even in these cases, the surrounding built environment may also have relevant negative effects, which were not taken under consideration and could be included in further, future research that will include the effect of various orientations, as well as of neighbouring buildings.

Existing residences built prior to the first Thermal Insulation Code (1981) form an important part of the building stock. Consequently their energy upgrade could contribute to significant conventional energy savings for heating and cooling, along with the inter-seasonal improvement of interior thermal comfort conditions.

The proposed interventions can improve thermal comfort conditions and lead to a reduction of energy consumption for heating and cooling, which is an important step against energy poverty and the on-going energy crisis.

The proposed interventions only involve the building envelope and are simple with relatively low cost.

The cost-optimal analysis is not able to address the multi-dimensionality of the decision according to the new European objectives and International sustainable development goals in the field of the nearly-zero energy building (NZEB) design. The purpose of this paper is to study the role of multi-criteria decision analysis (MCDA) for guiding energy investment decisions.

The paper explores the Preference ranking organization method for enrichment of evaluations II (PROMETHEE II) application to support the project of transforming a rural building into a NZEB. The evaluation provides an estimate of the effects of alternative energy efficiency measures, involving energy consumption, life cycle costs, carbon emissions, property value and indoor comfort criteria. The study performs a multi-actors analysis in order to understand how different consumers' point of views can influence the final choice of the best investment. Furthermore, a multi-site analysis explores the spatial variation of NZEB building appreciation in the real estate market.

The PROMETHEE II-based model ranks 16 alternative solutions for the NZEB according to energy, economic and extra-economic criteria. The multi-actors analysis highlights the configuration of the NZEB building that best meets the needs of different end-users, respecting the European directives and national standards. The multi-site analysis concludes that location does not change users' appreciation and not influence the output for the best solution.

The MCDA occurs as a support tool that helps to optimize the preliminary design phase of NZEB through the exploration of the optimal solution considering crucial criteria in the energy and environmental and real estate market rules.

A simplified general purpose analytical finite element model has been developed to analyze the thermal performance of a continuous flow polymerase chain reaction (CPCR) microdevice. The corresponding governing differential equations along with the appropriate boundary conditions have been solved using a self‐developed code in Matlab®. Results obtained from the finite element simulations have been validated with available published results and also showed good agreement with those obtained from commercial FEA package, ANSYS®. The present methodology has an added advantage due to its flexibility where the unit cell of the finite element model can be arranged into different orientation for analyses of different CPCR microdevice configuration. In microchannel heat sinks, the results obtained agree well with the published result which demonstrates the flexibility and robustness of present methodology to be used for various applications.

The inclusion of heritage dwellings in the UK decarbonization policies can contribute to cut operational carbon emissions from the building stock; this needs to be made a priority if net zero carbon targets are to be achieved. However, the energy and carbon savings potential of suitable retrofit interventions on this part of the stock is extremely variable and strictly intertwined with the range of baseline conditions of such dwellings. This study aims to propose a framework for interventions in traditional listed dwellings (TLDs) to improve their energy performance utilizing dynamic energy simulation (DES) of selected case studies (CSs) in the city of Brighton and Hove (South-East England).

To achieve this aim, the study established a baseline scenario which provides a basis for the assessment of energy performance and thermo-hygrometric behaviour pre- and post-interventions and allows for comparison between different CSs under comparable conditions.

Presenting a brief overview of the methodology adopted in this study, the paper describes the approach devised to generate such baseline scenario. The paper then compares the results obtained from simulation of normalized and baseline models with the status-quo energy consumption of the dwellings investigated (based on meter readings).

This analysis finally allows to highlight some key physical determinants of the baseline HEC which, in the following stage of research, proved to have a considerable effect also on the amount of energy and carbon savings achievable post retrofit interventions.

CasaZeroEnergy is the prototype for a building that does not use energy produced from non-renewable sources, but produces its require energy by using alternative energetic systems. Designed according to the principles of bioclimatic architecture, the building was integrated with passive systems for optimizing the site's climatic conditions for heating in winter and for cooling and ventilation in summer. The house was constructed with natural, renewable, recycled and recyclable materials. For this reason it can be classified as a “natural building”. Its main feature is the integration between the building and the alternative systems in order to produce energy from renewable sources: sunspace, solar collectors, photovoltaic panels, a geothermal system and a pellet boiler system. Home automation manages all the mechanical systems to ensure comfort and reduced energy consumption at the same time. The sunspace is a passive solar system used mainly for heating indoor spaces during the winter season. The building's cooling system is based on natural ventilation strategies and on geothermal heat pumps. The building is provided with shading systems. A smart system was devised to guarantee user safety and security. This kind of system can be controlled remotely and provides constant security for the building.