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•Building energy demand assessment designed with high-energy performance.•Parametric simulation to develop an accurate energy database.•Sensitivity analysis to identify the main ...parameters of the building energy balance.•Forecasting of the building energy needs through the black box method.•Multiple Linear Regression to identify simple correlations with high reliable degree.
Different ways to evaluate the building energy balance can be found in literature, including comprehensive techniques, statistical and machine-learning methods and hybrid approaches. The identification of the most suitable approach is important to accelerate the preliminary energy assessment. In the first category, several numerical methods have been developed and implemented in specialised software using different mathematical languages. However, these tools require an expert user and a model calibration. The authors, in order to overcome these limitations, have developed an alternative, reliable linear regression model to determine building energy needs. Starting from a detailed and calibrated dynamic model, it was possible to implement a parametric simulation that solves the energy performance of 195 scenarios. The lack of general results led the authors to investigate a statistical method also capable of supporting an unskilled user in the estimation of the building energy demand. To guarantee high reliability and ease of use, a selection of the most suitable variables was conducted by careful sensitivity analysis using the Pearson coefficient. The Multiple Linear Regression method allowed the development of some simple relationships to determine the thermal heating or cooling energy demand of a generic building as a function of only a few, well-known parameters. Deep statistical analysis of the main error indices underlined the high reliability of the results. This approach is not targeted at replacing a dynamic simulation model, but it represents a simple decision support tool for the preliminary assessment of the energy demand related to any building and any weather condition.
A joint TOA/AOA estimator is proposed for UWB indoor ranging under LOS operating conditions. The estimator employs an array of antennas, each feeding a demodulator consisting in a squarer and a ...low-pass filter. Signal samples taken at Nyquist rate at the filter outputs are processed to produce TOA and AOA estimates. Performance is assessed with transmitted pulses with a bandwidth of either 1.5 GHz (type-1 pulses) or 0.5 GHz (type-2 pulses), which correspond to sampling rates of 3 GHz and 1 GHz, respectively. As expected, the estimation accuracy decreases with the pulse bandwidth. Ranging errors of about 10 cm and angular errors of about 1° are achieved at SNR of practical interest with type-1 pulses and two antennas at a distance of 50 cm. With type-2 pulses the errors increase to 35 cm and 3°. Comparisons are made with other schemes discussed in literature.
The remarkable promise of multiple-input multiple-output (MIMO) wireless channels has motivated an intense research activity to characterize the theoretical and practical issues associated with the ...design of transmit (source) and receive (destination) processing matrices under different operating conditions. This activity was primarily focused on point-to-point (single-hop) communications but more recently there has been an extensive work on two-hop or multi-hop settings in which single or multiple relays are used to deliver the information from the source to the destination. The aim of this tutorial is to provide an up-to-date overview of the fundamental results and practical implementation issues in designing amplify-and-forward MIMO relay systems.
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•Artificial intelligence to forecast the thermal loads of the building.•An alternative method to solve the traditional building thermal balance.•Identification of fundamental inputs ...to reduce the computation time.•The high reliability of the results guaranteed by a deep statistical analysis of errors.•Performance validation of the neural model according to ASHRAE guidelines.
The evaluation of the energy performance of existing or new buildings is a fundamental action to guarantee the feasibility of a project and the achievement of the minimum efficiency requirements. In general, the determination of the thermal loads of a building is carried out via software but their use requires adequate knowledge of physical phenomena and therefore the presence of an expert user. Furthermore, the resolution can be difficult to implement and can require high computational costs; all conditions that can influence the success of a project. Based on these considerations, this work proposes an alternative solution to traditional calculation tools, which in a simple and effective way, highly reliable and with low computational times, solves the complex problem of the heat balance of a building. The authors explore the possibility of using artificial neural networks for the development of a decision support tool, which, through the identification of a few and fundamental input data, simultaneously determines and predicts the heating and cooling loads of buildings. Through the case study of the Italian non-residential building stock, the networks were explored and validated by an in-depth error analysis and a selection of the most suitable variables was conducted by Pearson's analysis. In this way, knowing only a few well-known data, the instrument immediately determines the total thermal loads and can be easily accessed by any user; its high reliability is demonstrated by the performance analysis results according to the criteria and error indices evaluated by ASHRAE Guideline 14.
The power curve of a wind turbine describes the generated power versus instantaneous wind speed. Assessing wind turbine performance under laboratory ideal conditions will always tend to be optimistic ...and rarely reflects how the turbine actually behaves in a real situation. Occasionally, some aerogenerators produce significantly different from nominal power curve, causing economic losses to the promoters of the investment. Our research aims to model actual wind turbine power curve and its variation from nominal power curve. The study was carried out in three different phases starting from wind speed and related power production data of a Senvion MM92 aero-generator with a rated power of 2.05 MW. The first phase was focused on statistical analyses, using the most common and reliable probability density functions. The second phase was focused on the analysis and modelling of real power curves obtained on site during one year of operation by fitting processes on real production data. The third was focused on the development of a model based on the use of an Artificial Neural Networks that can predict the amount of delivered power. The actual power curve modelled with a multi-layered neural network was compared with nominal characteristics and the performances assessed by the turbine SCADA. For the studied device, deviations are below 1% for the producibility and below 0.5% for the actual power curves obtained with both methods. The model can be used for any wind turbine to verify real performances and to check fault conditions helping operators in understanding normal and abnormal behaviour.
•A neural network model of wind turbine power curve is developed from real data.•Accurate statistical analyses are carried out on wind and weather data.•Neural network model is based on several, often neglected, parameters.•Results demonstrate effectiveness of the proposed method.
A reliable preliminary forecast of heating energy demand of a building by using a detailed dynamic simulation software typically requires an in-depth knowledge of the thermal balance, several input ...data and a very skilled user. The authors will describe how to use Artificial Neural Networks to predict the demand for thermal energy linked to the winter climatization of non-residential buildings. To train the neural network it was necessary to develop an accurate energy database that represents the basis of the training of a specific Artificial Neural Networks. Data came from detailed dynamic simulations performed in the TRNSYS environment. The models were built according to the standards and laws of building energy requirements in seven different European countries, for 3 cities in each country and with 13 different shape factors, obtaining 2184 detailed dynamic simulations of non-residential buildings designed with high energy performances. The authors identified the best ANN topology developing a tool for determining, both quickly and simply, the heating energy demand of a non-residential building, knowing only 12 well-known thermo-physical parameters and without any computational cost or knowledge of the thermal balance. The reliability of this approach is demonstrated by the low standard deviation less than 5 kWh/(m2·year).
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•Alternative method to solve the building energy balance without any computational cost.•Development of an ANN to assess of the energy performance of non residential building.•Several dynamic models in different location with different shape factor were built TRNSYS.•Implementation and creation of an organised energy database set.
Approximately 40% of the European energy consumption and a large proportion of environmental impacts are related to the building sector. However, the selection of adequate and correct designs can ...provide considerable energy savings and reduce environmental impacts. To achieve this objective, a simultaneous energy and environmental assessment of a building's life cycle is necessary. To date, the resolution of this complex problem is entrusted to numerous software and calculation algorithms that are often complex to use. They involve long diagnosis phases and are characterised by the lack of a common language. Despite the efforts by the scientific community in the building sector, there is no simple and reliable tool that simultaneously solves the energy and environmental balance of buildings. In this work, the authors address this challenge by proposing the application of an Artificial Neural Network. Due to the high reliability of learning algorithms in the resolution of complex and non-linear problems, it was possible to simultaneously solve two different but strongly dependent aspects after a deep training phase. In previous researches, the authors applied several topologies of neural networks, which were trained on a large and representative database and developed for the Italian building stock. The database, characterised by several building models simulated in different climatic conditions, collects 29 inputs (13 energy data and 16 environmental data) and provides 7 outputs, 1 for heating energy demand and 6 of the most used indicators in life cycle assessment of buildings. A statistical analysis of the results confirmed that the proposed method is appropriate to achieve the goal of the study. The best artificial neural network for each output presented low Root Mean Square Error, Mean Absolute Error lower than 5%, and determination coefficient close to 1. The excellent results confirmed that this methodology can be extended in any context and to any condition (other countries and building stocks). Furthermore, the implementation of this solution algorithm in a software program can enable the development of a suitable decision support tool, which is simple, reliable, and easy to use even for a non-expert user. The possibility to use an instrument to predict a building's performance in its design and planning phase, represent an important result to support decision-making processes toward more sustainable choices.
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•Implementation of a representative energy and environmental building stock database.•Artificial Neural Network to forecast energy and environmental building performance.•A decision support tool to contemporary solve energy and environmental aspects.•A new tool easy to use and with high reliability degree.•Methodology proposal extendible for any building and climatic conditions.
Purpose
The aim of this paper is to examine the impact of layer thickness on irreversible thermal expansion, residual stress and mechanical properties of additively manufactured parts.
...Design/methodology/approach
Samples were printed at several layer thicknesses, and their irreversible thermal expansion, tensile strength and flexural strength were determined.
Findings
Irreversible thermal strain increases with decreasing layer thickness, up to 22 per cent strain. Tensile and flexural strengths exhibited a peak at a layer thickness of 200 μm although the maximum was not statistically significant at a 95 per cent confidence interval. Tensile strength was 54 to 97 per cent of reported values for injection molded acrylonitrile butadiene styrene (ABS) and 29 to 73 per cent of those reported for bulk ABS. Flexural strength was 18 to 41 per cent of reported flexural strength for bulk ABS.
Practical implications
The large irreversible thermal strain exhibited that corresponding residual stresses could lead to failure of additively manufactured parts over time. Additionally, the observed irreversible thermal strains could enable thermally responsive shape in additively manufactured parts. Variation in mechanical properties with layer thickness will also affect manufactured parts.
Originality/value
Tailorable irreversible thermal strain of this magnitude has not been previously reported for additively manufactured parts. This strain occurs in parts made with both high-end and consumer grade fused deposition modeling machines. Additionally, the impact of layer thickness on tensile and flexural properties of additively manufactured parts has received limited attention in the literature.
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