Energy performance certificates — New opportunities for data-enabled urban energy policy instruments?

Pasichnyi, Oleksii; Wallin, Jörgen; Levihn, Fabian; Shahrokni, Hossein; Kordas, Olga

doi:10.1016/j.enpol.2018.11.051

Cited by 124 publications

(77 citation statements)

References 91 publications

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“…Since EPC databases are one of the main sources of information about the energy use for various types of buildings, the quality of stored data is of vital importance [26]. By 2014, some kind of quality control processes of the EPCs were established in all EU member states [27]. In Greece, the first level data quality controls are performed by the responsible authority [28].…”

Section: Data Cleansingmentioning

confidence: 99%

Baselines for Energy Use and Carbon Emission Intensities in Hellenic Nonresidential Buildings

Droutsa

Balaras

Lykoudis³

et al. 2020

Energies

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This work exploits data from 30,000 energy performance certificates of whole nonresidential (NR) buildings in Greece. The available information is analyzed for 30 different NR building uses (e.g., hotels, schools, sports facilities, hospitals, retails, offices) and four main services (space heating, space cooling, domestic hot water and lighting). Data are screened in order to exclude outliers and checked for consistency with the Hellenic NR building stock. The average energy use and CO 2 emission intensities for all building uses are calculated, as well as the respective energy ratings in order to gain a better understanding of the NR sector. Finally, in an attempt to determine whether these values are representative for the various Hellenic NR building uses, their temporal evolution is investigated. The average primary energy use intensity is 448.0 kWh/m 2 for all NR buildings, while the CO 2 emissions reach 147.5 kgCO 2 /m 2 . The derived energy baselines reveal that indoor sports halls/swimming pools have the highest energy use, while private cram schools/conservatories have the lowest, due to their operational patterns. Generally, from the four services taken into account, lighting is the most energy consuming, followed by cooling, heating and finally domestic hot water. For a total of 11 building uses, more data from the certificates will be necessary for deriving representative baselines, but, when it comes to buildings categories, more data are required.

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Section: Data Cleansingmentioning

confidence: 99%

Baselines for Energy Use and Carbon Emission Intensities in Hellenic Nonresidential Buildings

Droutsa

Balaras

Lykoudis³

et al. 2020

Energies

View full text Add to dashboard Cite

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“…The analysis of real data from EPC databases has been performed in various countries [11]. The authors in Fabbri et al [40] discuss about the effects of EPBD Directive and Italian EPC system on the real estate market prospective.…”

Section: Related Workmentioning

confidence: 99%

“…The data cleaning phase is crucial in order to ensure the robustness of the analysis. In fact, EPCs datasets can be characterized low quality (in terms of attribute inconsistencies) [11]. However, the domain expertise in the energy and buildings field can prevent or at least limit inconsistency issues.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…However, the domain expertise in the energy and buildings field can prevent or at least limit inconsistency issues. According to [11] the consistency checks considered in this study are:…”

Section: Data Preprocessingmentioning

confidence: 99%

“…However, the performance gap, i.e., the difference between estimated and actual energy performance could be significant. For instance, in [11] is stated that for the Swedish EPCs dataset the assessed performance gap is about the 20% for energy consumption assessments. An EPC is therefore not fully representative of the actual performance during operation but makes it possible to perform comparisons and benchmarking analysis between buildings.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates

et al. 2019

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Energy performance certification is an important tool for the assessment and improvement of energy efficiency in buildings. In this context, estimating building energy demand also in a quick and reliable way, for different combinations of building features, is a key issue for architects and engineers who wish, for example, to benchmark the performance of a stock of buildings or optimise a refurbishment strategy. This paper proposes a methodology for (i) the automatic estimation of the building Primary Energy Demand for space heating (PED h ) and (ii) the characterization of the relationship between the PED h value and the main building features reported by Energy Performance Certificates (EPCs). The proposed methodology relies on a two-layer approach and was developed on a database of almost 90,000 EPCs of flats in the Piedmont region of Italy. First, the classification layer estimates the segment of energy demand for a flat. Then, the regression layer estimates the PED h value for the same flat. A different regression model is built for each segment of energy demand. Four different machine learning algorithms (Decision Tree, Support Vector Machine, Random Forest, Artificial Neural Network) are used and compared in both layers. Compared to the current state-of-the-art, this paper brings a contribution in the use of data mining techniques for the asset rating of building performance, introducing a novel approach based on the use of independent data-driven models. Such configuration makes the methodology flexible and adaptable to different EPCs datasets. Experimental results demonstrate that the proposed methodology can estimate the energy demand with reasonable errors, using a small set of building features. Moreover, the use of Decision Tree algorithm enables a concise interpretation of the quantitative rules used for the estimation of the energy demand. The methodology can be useful during both designing and refurbishment of buildings, to quickly estimate the expected building energy demand and set credible targets for improving performance.

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Household Energy Poverty and the “Just Transition”

Faiella

Lavecchia²,

Miniaci³

et al. 2022

Handbook of Labor, Human Resources and Population Economics

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Energy performance certificates — New opportunities for data-enabled urban energy policy instruments?

Cited by 124 publications

References 91 publications

Baselines for Energy Use and Carbon Emission Intensities in Hellenic Nonresidential Buildings

Baselines for Energy Use and Carbon Emission Intensities in Hellenic Nonresidential Buildings

Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates

Household Energy Poverty and the “Just Transition”

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