On the use of questionnaire in residential buildings. A review of collected data, methodologies and objectives

Carpino, Cristina; Mora, Dafni; Simone, Marilena De

doi:10.1016/j.enbuild.2018.12.021

Cited by 49 publications

(20 citation statements)

References 140 publications

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“…In this paper, the data collection to investigate on the energy consumptions in residential buildings [44,45] have been done by means of a questionnaire surveys.…”

Section: Methodsmentioning

confidence: 99%

Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey

2019

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This work shows the outcomes of a research activity aimed at the energy characterization of residential users. Specifically, by data analysis related to the real energy consumption of sample buildings, the flexible loads amount has been identified so as to investigate on the opportunity to implement a demand/response (DR) program. The most meaningful input data have been collected by an on-line questionnaire created within an Excel spreadsheet allowing one to simulate and compare the calculations with the actual dwellings’ consumption; 412 questionnaires have been used as statistical sample and simulations have been performed based on single-zone dynamic model. Additionally, once the energy consumptions have been sorted by the different services, reference key performance indicators (KPIs) have been also calculated normalising those ones by people and house floor surface. From data analysis, it emerges how the Italian residential users are not very electrified. Furthermore, the flexible loads are low and, implementing minor maintenance interventions, the potential of flexibility can decrease up to 20%. For that reason, the current research can be further developed by investigating on suitable flexibility extensions as well as on the automation system requirements which is needed managing the flexible loads.

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“…In this paper, the data collection to investigate on the energy consumptions in residential buildings [44,45] have been done by means of a questionnaire surveys.…”

Section: Methodsmentioning

confidence: 99%

Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey

2019

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“…The building stock is extremely diverse (Bourdeau et al 2019), and the data sources are not standardized, which requires extensive questionnaires and tools for data collection. In addition, parameters and terms are often interpreted differently, making it difficult to align datasets (Carpino et al 2019). With our study we directly address this research gap, focusing on residential buildings, using real-world data, and predicting annual heating energy performance.…”

Section: Energy Quantification Methodsmentioning

confidence: 99%

Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany

Wenninger

Wiethe

2021

Bus Inf Syst Eng

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To achieve ambitious climate goals, it is necessary to increase the rate of purposeful retrofit measures in the building sector. As a result, Energy Performance Certificates have been designed as important evaluation and rating criterion to increase the retrofit rate in the EU and Germany. Yet, today’s most frequently used and legally required methods to quantify building energy performance show low prediction accuracy, as recent research reveals. To enhance prediction accuracy, the research community introduced data-driven methods which obtained promising results. However, there are no insights in how far Energy Quantification Methods are particularly suited for energy performance prediction. In this research article the data-driven methods Artificial Neural Network, D-vine copula quantile regression, Extreme Gradient Boosting, Random Forest, and Support Vector Regression are compared with and validated by real-world Energy Performance Certificates of German residential buildings issued by qualified auditors using the engineering method required by law. The results, tested for robustness and systematic bias, show that all data-driven methods exceed the engineering method by almost 50% in terms of prediction accuracy. In contrast to existing literature favoring Artificial Neural Networks and Support Vector Regression, all tested methods show similar prediction accuracy with marginal advantages for Extreme Gradient Boosting and Support Vector Regression in terms of prediction accuracy. Given the higher prediction accuracy of data-driven methods, it seems appropriate to revise the current legislation prescribing engineering methods. In addition, data-driven methods could support different organizations, e.g., asset management, in decision-making in order to reduce financial risk and to cut expenses.

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“…Furthermore, the tenants' representative provided anonymised metering data for all of the 154 apartments. The questionnaires were not anonymised, and all of the participants were required to give their consent for the participation in this scientific research based on the EU General Data Protection Regulation [20]. The consents obtained were presented to the local district heating company with the request for access to the relevant billing data for the 51 apartments participating in the interviews ("participating apartments" in the following text).…”

Section: Analysed Datasetmentioning

confidence: 99%

“…Previously, a great deal of research has been conducted in order to describe and understand the energy consumption in the residential sector. Most of the accessed categories of objectives are identified in these three categories: (i) energy, (ii) occupants' behaviour and (iii) guidelines (in energy policies or simulation and design) [20]. In this paper an emphasis is placed on occupant behaviour related data, and the questionnaire is made with the focus on this area.…”

Section: Introductionmentioning

confidence: 99%

Modelling Influential Factors of Consumption in Buildings Connected to District Heating Systems

Maljković

2019

Energies

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Assessing the influential factors on measured (or allocated) heat consumption in district heating systems is often limited by the available data. Within a project of modelling consumption in district heating systems in Croatia for the Ministry of Environmental Protection and Environment, an access to a complete billing database of the largest Croatian district heating company was granted. The company supplies approximately 126,400 final consumers (both households and businesses) over 375 km of distribution network. The billing database has 40 vectors in a few million single inputs. In addition to these data, a questionnaire is distributed to the final consumers in several buildings labelled as “model buildings”, gathering behavioural and demographic data of final consumers (such as occupancy, mode of space usage, heat comfort level, age of occupants, etc.). The two sets of data are then merged, and a correlation analysis is performed. Furthermore, a two-step regression analysis is performed based on variables from billing database in the first step, with added behavioural and demographic variables obtained from the questionnaires in the second step. The models from two steps are compared, tested and interpreted. Results of the most influential factors on heat consumption in district heating systems are given and the influence of the behavioural/demographic variables on the prediction accuracy of heating consumption is interpreted.

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On the use of questionnaire in residential buildings. A review of collected data, methodologies and objectives

Cited by 49 publications

References 140 publications

Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey

Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey

Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany

Modelling Influential Factors of Consumption in Buildings Connected to District Heating Systems

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