1,720,974 research outputs found
Tool for supporting local energy strategies: forecasting energy plans with Artificial Neural Network in Umbria Region
Full text linkIn order to reduce the greenhouse gas emission and to improve the energy efficiency of buildings, European Member States have to plan medium-to-long term strategies as reliable as possible. In this context, the present work aims to discuss the potentiality of Artificial Neural Network (ANN) as a support tool for medium-to-long term forecasting analysis of energy efficiency strategies in Umbria Region (central Italy) chosen as case study. Parametric energy simulations of several archetypes buildings were carried out in compliance with the current Italian regulations by changing the form, thermal properties, boundary conditions, and technical building systems. An ANN able to forecast primary energy need was trained to forecast the energy need of building-stock of Umbria Region and to evaluate the effectiveness of several potential energy actions (such as thermal coat or technical building systems replacement) over the years. Results confirm the potential of use of ANN as a support tool in energy forecasting analysis for local Authorities. ANN is capable of forecasting different future scenarios allowing correctly planning energy actions to be implemented as well as their priority. The results open to several scenarios of interest, such as the application of the same approach at national level
An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks
No full textThe Energy Performance Buildings Directive (EPBD) was issued to provide a common strategy for all
European countries and to implement several actions for improving energy efficiency of buildings,
responsible for 40% of energy consumption. Energy Performance Certificates are provided as a tool to
evaluate the energy performance of buildings; however, costly and time-consuming controls are necessary
to verify the accuracy of the set and declared data.
Useful tools could be the Artificial Neural Networks (ANN), whereby it is possible to estimate the
energy consumptions from specific parameters, to evaluate the accuracy of data in the energy certificates,
and to identify the certificates needing accurate control.
In this study, an Artificial Neural Network was developed based on approximately 6500 energy certificates
(2700 are self-declaration) received by the Umbria Region (central Italy), in order to evaluate the
global energy consumption of buildings from several and specific parameters reported in certificates.
Data was checked in compliance with energy standards and only the correct certificates were used to
train the Neural Network.
The implemented Neural Network was tested with database data and a good correlation was found; in
particular the energy performance calculated with the Neural Network presents an error greater than
15 kW h/m2 year with respect to the real value of global energy performance index in only 3.6% of cases.
Finally, a Neural Energy Performance Index (N.E.P.I.) was defined, in order to verify the accuracy of the
energy certificates; the study reported in this paper shows how the new defined index could be an important
tool to identify which energy certificates require controls. A refinement of the Neural Network would
allow to minimize the error and to define a N.E.P.I. index that could be used by European public administrations
as a tool to perform an initial check of certificates
Evolutive Housing System: refurbishment with new technologies and unsteady simulations of energy performance
No full texttThe aim of the present paper is to evaluate the energy performance in unsteady-state conditions ofan Evolutive House. The original design was presented by two important architects, Renzo Piano andPeter Rice, in 1978. The house has two large glass walls in the east and west fac ̧ ades. Experimentalinvestigation and numerical analysis were carried out in a prototype of the house realized in Perugia. Theair temperature, the surface temperature of floors, the global solar radiation, the relative humidity weremeasured. Simulations were performed using both Energy Plus and TRNSYS software. Simulation modelswere tested and validated with experimental data considering a new weather database compiled forPerugia. The analysis compares different scenarios in terms of energy demand, such as the substitutionof the glazing and the use of innovative packaged solutions. Innovative glazing systems filled with silicaaerogel were investigated as a solution for energy saving in buildings. Results show that an importantenergy saving was obtained for all the proposed glazings (about 60–70%). The simulation codes’ resultsare in good agreement, but some differences are due to the different approach in the evaluation of thesolar irradiance on tilted surfaces and to the transient heat conduction model
Approaching the validation of building energy models: billing vs indoor environmental data
Full text linkTo understand the real building energy consumption, and to identify the best energy improvements (due to energy refurbishment and/or HVAC replacement), the energy audit is recommended, but it generally entails the realization of an energy model of the building itself. The main issue of this approach is the collection of data, needed to verify the reliability of the energy audit results. Currently, to validate buildings energy model retrieved by hourly semi-stationary software, is it possible to refer to: (i) bills related to energy carrier (i.e. natural gas and/or electricity); (ii) monitored indoor parameters. The aim of the paper is to understand which of the two is more suitable for the purpose. In this work, the two options are investigated considering, as case study, a family house located in central Italy, whose envelope thermophysical properties and HVAC systems are known, and whose bills and indoor environmental conditions (air temperature and relative humidity) were archived since December 2019. Results from the two validation methods are very close to each other; particularly, results show that energy audit performed with indoor environmental parameters better fits the real consumption, but it entails more complex validation procedure
Integration of Energy Simulations and Life Cycle Assessment in Building Refurbishment: An Affordability Comparison of Thermal Insulation Materials through a New Sustainability Index
Full text linkEnergy efficiency and greenhouse gas reduction have become two of the most important issues to address in fighting climate change. Focused strategies have been implemented aiming at reducing the energy consumption of buildings since it is one of the most energy-intensive sectors, but they are mainly concerned with energy reduction without considering their environmental impact. The present work therefore aims at assessing the energy and environmental impacts of the use of insulation materials for building envelope refurbishment as the thermal coating. Reference buildings were used to perform energy simulations in representative cities of Italy and energy and environmental impacts of the most common and sustainable insulation materials were thus evaluated. Relevant outcomes have been focused on defining a new Economic and Environmental Sustainability Index (EESI) capable of considering both economic and environmental aspects; particularly, sustainable materials (such as cellulose fiber) can have the same affordability as traditional ones (such as polystyrene foam slab, glass wool, or stone wool) if environmental impact is also taken into account, despite their higher cost. However, according to EESI, the affordability of traditional insulation materials remains evident in the warmest climatic zones because of the lower energy needs of buildings
Thermal behavior of a ventilated wall: experimental data and CFD model validation.
No full textVentilated walls are an interesting technology to protect buildings from
moisture and weathering. In this paper a ventilated wall made of traditional materials
was analysed and improved by using CFD simulations; in particular a CFD model was
implemented and validated by means of an experimental campaign.
A test masonry wall was built at the Laboratory of Department of Engineering -
University of Perugia; the two test chambers were respectively heated and cooled and the
surveys were carried out until the stationary conditions were reached.
The experimental campaign was carried out for about one month; the thermal behaviour
of the masonry wall was monitored (temperature, air velocity, and heat flux) with and
without ventilated air gap.
Then, a CFD model was implemented and validated considering all the data monitored
during an experimental campaign.
In order to reproduce the monitored thermal behaviour of the masonry wall, the CFD
model was implemented by applying different turbulent and heat exchange models;
besides, a sensitivity analysis of mesh size was carried out, in order to evaluate the best
configuration which allowed to correctly simulate the thermal behaviour of the wall.
In this preliminary study, the CFD model was implemented and validated considering the
stationary conditions and it represented the starting point for the development of the
ventilated wall in unsteady state conditions
Comparison of the Energy Performance of Existing Buildings by Means of Dynamic Simulations and Artificial Neural Networks
No full textThe energy efficiency of buildings can be evaluated by using the methodology provided by European regulations; however, this method required a lot of information which is generally not available for the existing buildings. In this paper an alternative method for the energy efficiency investigation of buildings is proposed and tested by means of Artificial Neural Networks (ANNs); an existing building built in 1990 and located in Perugia was chosen as case study and it was investigated by adopting both the mentioned approaches. An experimental campaign was also carried out in order to implement and validate the 3D model developed in TRNSYS. Results showed that the indoor air temperature trend simulated with ANN is closer to the measured data than the one simulated with TRNSYS, with lower mean error and MSE values. The energy consumption simulated with ANN is slightly higher than the one returned by using TRNSYS code of about 20 kWh/m2year (difference lower than 7%). In agreement with the results, the proposed method can be considered as an alternative tool that can be used for the thermal-energy investigation of existing buildings, with important money and time saving
Application of hourly dynamic method for nZEB buildings in Italian context: analysis and comparisons in national calculation procedure framework
Full text linkThe Energy Performance of Buildings Directive (EPBD 2018/844/EU) requires to Member States to upgrade the methodology for the energy performance assessment of buildings. The current calculation method, based on the monthly quasi steady state calculation procedure, could be replaced in the next years by an hourly dynamic calculation procedure (EN ISO 52016), in which a resistance-capacity (RC) model is implemented to consider with more accuracy the heat exchange through the building envelope. In this framework, the present work aims at analysing and comparing the energy needs of three reference case studies of nearly Zero Energy Buildings (nZEB), applying both calculation procedures in order to investigate the main difference of the two approaches. Two residential buildings and one office, compliant with Italian minimum requirements for nZEB, were defined, and several energy simulations were carried out for all different climatic zones of Italian territory. Preliminary results highlighted significant differences of energy need mainly due to different weight of heat loss and heat gains obtained with the two considered calculation methods. This paper represents a preliminary study, but further analysis are recommended in order to evaluate the overall energy use for different type and different operation profile of buildings
Thermal Comfort Evaluation Within Non-residential Environments: Development of Artificial Neural Network by Using the Adaptive Approach Data
No full textAbstractA new algorithm for the PMV calculation was developed using Artificial Neural Networks. Several experimental investigations were carried out in two classrooms using both Fanger static model and adaptive approaches for the PMV evaluation. The Artificial Neural Network was trained considering a few input parameters; specifically for the network development only the air temperature and relative humidity were considered as experimental data. This algorithm allows to correlate the thermal sensation to both indoor and outdoor factors and it is a useful tool for predicting the PMV index, replacing the traditional methods with less time and cost demanding
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