1,721,058 research outputs found
Building energy performance forecasting: A multiple linear regression approach
Full text linkDifferent 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
An intelligent way to predict the building thermal needs: ANNs and optimization
No full textThe 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
Temporal variation of organic carbon content in some vertisol in different climatic condition of western Sicily (Palermo-Italy)
No full textEnergy and economic analysis and feasibility of retrofit actions in Italian residential historical buildings
Full text linkThe application of retrofit actions to existing building stocks can improve the energy performance of the residential sector. In this context, particular attention should be given to historical buildings, which represent a large part of the Italian building stock. To improve the energy performance of them, adequate retrofit actions must be applied. Many studies and regulations have focused on identifying the best refurbishment measures. However, the selection of these measures is difficult due to restrictive regulations, which are dictated by the Ministry of Cultural Heritage and Activities, high retrofit costs, and variable climate zones. Thus, energy renovations in Italian buildings are not simple, and it is very difficult to find generic solutions that can be applied to buildings across the entire territory.
In this paper, the authors investigated the most common retrofit solutions used in Italy, focusing in particular on the energy performance of historical building envelopes. First, energy performance analyses were conducted for two typical base cases in four different Italian cities using TRNSYS software, and some common retrofit measures were analysed. In some cases, the results showed Primary Energy saving of 44.6% (sample A) and 56.7% (sample B). Furthermore, these were used to identify different energy and economic impacts associated with the same refurbishment measures in different climatic contexts, highlighting the non-existence of a generic solution suitable for all regions or countries
Teorie e strategie d'intervento con minori abusanti dell'USSM di Palermo
No full textIl contributo descrive il lavoro effettuato dagli operatori dell’USSM di Palermo relativamente ai minori sex offenders. Considerata la specificità dell’azione violenta posta in atto, da alcuni anni l’Ufficio si è dotato di una “task force” interna (gruppo E.O.S.) che sperimenta e implementa modalità di comprensione del fenomeno, presa in carico congiunta, creazione di un modello condiviso di lavoro sul target. Nello specifico, un focus verrà aperto sulle strategie operative principali adottate e sull’attività necessaria, quanto mai faticosa, di intervenire sui meccanismi di negazione dei rei e delle famiglie degli stessi. In questo senso l’approccio con le famiglie appare una “conditio sine qua non” per avviare una migliore rielaborazione dell’evento, il ripristino di una comunicazione più efficace sul background del reato nonché sulla vittima e i suoi vissuti
Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks
Full text linkThe 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
Exergoeconomic analysis as support in decision-making for the design and operation of multiple chiller systems in air conditioning applications
No full textMultiple-chillers systems represent viable solutions for medium/large-scale air conditioning applications characterized by variable cooling demand. The energy efficiency of such systems is influenced by the number of chillers, the combination of cooling capacities, and the load-sharing among the units. Large efforts have been devoted to developing efficient operation strategies for these systems, but rules of thumb are usually adopted for selecting cooling capacities thus leaving room for energy and economic savings. In this paper, exergoeconomic analysis is proposed as a promising method to identify near-optimal design and operation strategies, due to the capability of exergoeconomic indicators to account simultaneously for capital and operating costs. The potential of the method is illustrated for a hydronic system supplying an air-handling unit installed in an office building. Design alternatives are compared, with chillers of equal or different capacities operated in a parallel or series configuration, and the cost-effectiveness of different load sharing strategies is also investigated. A thermoeconomic model for multiple-chillers systems is formulated, considering the actual performance of chillers under full- and part-load conditions derived by a plant simulator. Results show that the exergoeconomic cost of chilled water reduced by about 7% and 30% when passing from evenly to unevenly sized systems in both series and parallel configurations. It is also found that the symmetric load sharing strategy leads to a 14–18% reduction in the cost of chiller water compared to the sequential one. The study confirms that this method may represent systematic and thermodynamically-sound support for engineers in this field
Multiple criteria assessment of methods for forecasting building thermal energy demand
Full text linkNowadays worldwide directives have focused the attention on improving energy efficiency in the building sector. The research of models able to predict the energy consumption from the first design and energy planning phase is conducted to improve building sustainability. Use of traditional forecasting tools for building thermal energy demand tends to encounter difficulties relevant to the amount of data required, implementation of the models, computational costs and inability to generalize the output. Therefore, many studies focused on the research and development of alternative resolution methods, but the choice of the most convenient is not clear and simple. Single comparison of statistical quality indexes does not allow an adequate identification of the most efficient method, as the necessary efforts for implementation of the methods from the initial data collection to the use phase are not considered. In this work, the authors propose to apply, for the first time, the multicriteria assessment to determine the most efficient alternative method, used for forecasting of building thermal energy demand. Three alternative “black-box” methods, previously investigated by the authors, were compared by the multiple criteria Complex Proportional Assessment Method. Such a procedure revealed ranking of the methods in four phases, namely Pre-processing, Implementation, Post-processing and Use, as well as overall assessment and selection of the most efficient method in terms of evaluated criteria. This first application could represent an incentive for future multi-criteria analyses involving a growing number of alternative forecasting methods
Multi-energy school system for seasonal use in the mediterranean area
Full text linkSchool buildings represent an energy-consuming sector of real estate where different efficiency actions are necessary. The literature shows how the design of a multi-energy system offers numerous advantages, however, there are problems related to the integration of cogeneration units with renewable energy sources due to the low flexibility of the first one and the high degree of uncertainty of the latter. The authors provide an alternative solution through the analysis of a case study consisting of a multiple energy system in three Sicilian schools, focusing on the system’s operational strategy, on the design and sizing of components and trying to exploit the energy needs complementarity of buildings instead of integrating the conventional energy storage systems. Not considering school activities in summer, it was decided to install a cogeneration unit sized on winter thermal loads, whereas the electricity demand not covered was reduced with photovoltaic systems designed to maximize production for seasonal use and with loads concentrated in the morning hours. The effectiveness of this idea, which can be replicated for similar users and areas, is proved by a payback time of less than 11 years and a reduction of 31.77% of the CO2 emissions
Regression analysis to design a solar thermal collector for occasional use
Full text linkOptimal design of a solar thermal system is necessary to minimize payback time and to diffuse renewable energy use for Domestic Hot Water production in residential areas. More accurate design is crucial in the case of seasonal or occasional use of the system; indeed, the standard criteria generally applied to a design system for continuous use, can lead to considerable over-sizing. To speed up the design phase and to help the planner in the identification of the best solution without any complex evaluation or long computational time, it would be interesting to have available a simpler method than the standard procedures, but one that is reliable and accurate for the evaluation of the best configuration, taking into account occasional use, seasonal and monthly domestic hot water demand, orientation and primary flow rate. To this end, the authors investigated a methodology for the identification of some empirical correlations based on the analysis of data coming from a parametric simulation; in this way the identified correlations can indicate, with high reliability, the optimal design knowing only well-known parameters. In detail, the data output was extracted and processed to evaluate the best design configurations under any operative conditions. Determination of the best configuration identifies the operative parameters that maximize the Solar Fraction of the plant and minimize the auxiliary energy. To highlight the reliability of this methodology, in this work, the authors describe a case study of the Sicilian region proposing a set of simple, reliable correlations that allow the determination of the best tilt angle for monthly or seasonal use. Following the same steps the procedure can be replicate in any context and in any conditions
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