Mapping the energy performance of hellenic residential buildings from EPC (energy performance certificate) data

Droutsa, Kalliopi G.; Kontoyiannidis, Simon; Dascalaki, Elena G.; Balaras, Constantinos A.

doi:10.1016/j.energy.2015.12.137

Cited by 104 publications

(57 citation statements)

References 25 publications

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“…The data analysis confirmed the relatively low energy performance of existing NR buildings in Greece (Figure 6a). About 30% of the buildings are rated at class D; yet, this is better than the Hellenic residential buildings for which about 50% are rated at class G [36]. Similar results are reported in Spain [18] where NR buildings have a slightly better energy performance than residential buildings, with 26.4% of NR buildings rated at class D, followed by class E (22.8%) and class C (19.8%).…”

Section: Energy Performancesupporting

confidence: 73%

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: Energy Performancesupporting

confidence: 73%

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

Droutsa

Balaras

Lykoudis³

et al. 2020

Energies

Self Cite

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“…In the context of these kind of analyses, EPCs have been used for: (1) energy planning at different scales (3) mapping the energy performance of buildings (Droutsa et al 2016, Gangolells et al 2016, Hjortling et al 2017; (4) evaluating regional appropriateness of the EPC methodology, accuracy of measurement or calculating uncertainties pertaining to the use of EPC to describe energy usage in the building stock (respectively Tronchin and Fabbri 2012, Mangold et al 2015, Abela et al 2016; (5) testing the impact of EPCs on the retrofit of buildings (Charalambides et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Harvesting big data from residential building energy performance certificates: retrofitting and climate change mitigation insights at a regional scale

Gouveia

Palma

2019

Environ. Res. Lett.

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The reduction of energy consumption and the increase in energy efficiency is currently an important cornerstone of EU policy. Energy performance certificates (EPCs) were implemented as one of the tools to promote this agenda, and are used for the energy performance assessment of buildings. In this study, the characteristics of the Portuguese dwelling stock are regionally analysed using data from approximately 523,000 Portuguese residential EPCs. Furthermore, a bottom-up building typology approach is used to assess the regional energy needs impact of retrofitting actions and to estimate the heating and cooling energy performance gaps of the whole dwelling stock, as well as the potential CO 2 emissions resulting from the gaps' potential offset due to increase thermal comfort. The results show that Portuguese residential buildings have very low energy performance, with windows and roofs being identified as the most energy inefficient elements. Roof retrofitting has the highest potential for the reduction of energy needs. The estimated heating and cooling energy performance gap amount to very significant percentages, due to the poor performing building stock but also very low energy consumption levels, with probable consequences for the thermal comfort of occupants. Assuming the current energy mix, carbon emissions would be 9.8 and 20.2 times higher associated with heating and cooling, respectively, if the actual final energy consumption were to match the estimated theoretical values derived from building regulation. This study demonstrates several application cases and leverages the potential of the individual EPC, increasing the detail in the dwelling stock characterization and energy performance estimation, revealing its value for energy retrofit and climate change mitigation assessments, as well as establishing the ground for future work related to building retrofits, energy efficiency measure implementation, climate change mitigation, thermal comfort, and energy poverty studies.

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“…Within the scientific context, several research activities have been carried out on buildings energy performance assessment, for: (i) predicting energy demand [7,10,23] and energy class [24], (ii) rating and benchmarking [25][26][27][28], (iii) individuating representative buildings for different classes of energy performance [29][30][31], (iv) characterizing the relationship between energy demand and relevant building features [32][33][34], and (v) improving existing methods, also using new model based on data mining algorithms like regression models, decision trees, neural networks, and clustering [24,32,[35][36][37][38].…”

Section: Related Workmentioning

confidence: 99%

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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Mapping the energy performance of hellenic residential buildings from EPC (energy performance certificate) data

Cited by 104 publications

References 25 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

Harvesting big data from residential building energy performance certificates: retrofitting and climate change mitigation insights at a regional scale

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

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