Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe

Escandón, Rocío; Ascione, Fabrizio; Bianco, Nicola; Mauro, Gerardo Maria; Suárez, Rafael; Sendra, Juan J.

doi:10.1016/j.applthermaleng.2019.01.013

Cited by 78 publications

(44 citation statements)

References 40 publications

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“…The starting point for the methodology of this work is a building stock model previously developed for the environmental characterization of the entire building category: linear multi-family social housing developed in southern Spain between the 1950s and the 1980s. The development and calibration of this model through in-situ measurements is described in depth in a previous publication [37].…”

Section: Methodsmentioning

confidence: 99%

Predicting the Impact of Climate Change on Thermal Comfort in A Building Category: The Case of Linear-type Social Housing Stock in Southern Spain

et al. 2019

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The Climate Change scenario projected by the IPCC for the year 2050 predicts noticeable increases in temperature. In severe summer climates, such as the Mediterranean area, this would have very negative effects on thermal comfort in the existing housing stock, given the current high percentage of dwellings which are obsolete in energy terms and house a population at serious risk of energy poverty. The main aim of this paper is to generate a predictive model in order to assess the impact of this future climate scenario on thermal comfort conditions in an entire building category. To do so, calibrated models representing linear-type social multi-family buildings, dating from the post-war period and located in southern Spain, will be simulated extensively using transient energy analyses performed by EnergyPlus. In addition, a sensitivity analysis will be performed to identify the most influential parameters on thermal discomfort. The main results predict a generalized deterioration in indoor thermal comfort conditions due to global warming, increasing the average percentage of discomfort hours during the summer by more than 35%. This characterization of the future thermal behaviour of the residential stock in southern Spain could be a trustworthy tool for decision-making in energy retrofitting projects which are so badly needed. To do so, further work is required on some limitations of this model so that different user profiles and typologies can be represented in detail and an economic assessment can be included.

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Section: Methodsmentioning

confidence: 99%

Predicting the Impact of Climate Change on Thermal Comfort in A Building Category: The Case of Linear-type Social Housing Stock in Southern Spain

et al. 2019

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“…There are many aspects influencing real building energy performance; occupant behaviour is one of the most significant [11]. Occupants influence, for example, ventilation rates that are generally lower in winter and higher in warm days and months.…”

Section: Discussionmentioning

confidence: 99%

Selection of Favourable Concept of Energy Retrofitting Solution for Social Housing in the Czech Republic Based on Economic Parameters, Greenhouse Gases, and Primary Energy Consumption

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Energy retrofitting of existing building stock has significant potential for the reduction of energy consumption and greenhouse gas emissions. Roughly half of the CO2 emissions from Czech building stock are estimated to be allocated to residential buildings. Approximately one-third of the Czech residential building stock have already been retrofitted, but retrofitting mostly takes place in large cities due to greater income. A favourable concept for the mass retrofitting of residential building stock, affordable even in low-income regions, was of interest. For a reference building, multi-criteria assessment of numerous retrofitting measures was performed. The calculation involved different building elements, materials, solutions, and energy-efficiency levels in combination with various heating systems. The assessment comprised environmental impact, represented by operational and embodied primary energy consumption and greenhouse gas emissions, and investment and operational costs using the annuity method. Analysis resulted in the identification of favourable retrofitting measures and showed that complex building retrofitting is advantageous from both a cost and an environmental point of view. The environmental burden could be decreased by approximately 10–30% even without photovoltaic installation, and costs per year could be decreased by around 40%.

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“…Among various types of AI techniques, artificial neural network (ANN) is a well-known technique that is widely employed for many simulation problems [20,21]. Moreover, ANN has emerged as an effective tool in building energy management [22,23]. Mejías et al [24] used linear regression models and artificial neural networks to estimate three concepts of heating and cooling energy demands, energy consumptions and CO 2 emissions in office buildings.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models

et al. 2019

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Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing the weights and biases of the artificial neural network (ANN) in the estimation of heating load (HL) and cooling load (CL) of the energy-efficient residential buildings. To this end, a proper dataset was provided composed of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, as the HL and CL influential factors. The optimal structure of each model was achieved through a trial and error process and to evaluate the accuracy of the designed networks, we used three well-known accuracy criterions. As the result of applying GA and ICA, the performance error of ANN decreased respectively by 17.92% and 23.22% for the HL, and 21.13% and 24.53% for CL in the training phase, and 20.84% and 23.74% for HL, and 27.57% and 29.10% for CL in the testing phase. The mentioned results demonstrate the superiority of the ICA-ANN model compared to GA-ANN and ANN.

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Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe

Cited by 78 publications

References 40 publications

Predicting the Impact of Climate Change on Thermal Comfort in A Building Category: The Case of Linear-type Social Housing Stock in Southern Spain

Predicting the Impact of Climate Change on Thermal Comfort in A Building Category: The Case of Linear-type Social Housing Stock in Southern Spain

Selection of Favourable Concept of Energy Retrofitting Solution for Social Housing in the Czech Republic Based on Economic Parameters, Greenhouse Gases, and Primary Energy Consumption

Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models

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