On the use of an energy certification database to create indicators for energy planning purposes: Application in northern Italy

Dall’O’, Giuliano; Sarto, Luca; Sanna, Nicola; Tonetti, Valeria; Ventura, Martina

doi:10.1016/j.enpol.2015.06.015

Cited by 51 publications

(21 citation statements)

References 21 publications

(20 reference statements)

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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%

“…Several works have proposed a benchmarking of different types of buildings. Dall'O' et al [25] analyse a real data set of energy certificates to assess the energy performance, to detect anomalies in the registered certificates and to quantify the energy retrofit potential in existing buildings. Chung et al [26] developed a benchmarking process for energy efficiency of commercial buildings by means of Multiple Regression Analysis (MRA).…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

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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“…Among the most successful applications is the consumption profile forecast in buildings [1], which can have a number of advantages, including failure predictions and the optimization of energy management systems [2]. The energy characteristics of buildings are often included in energy certifications, and a proper data analysis on large datasets can provide useful insights for energy planning at an urban scale [3]. Heating and cooling in buildings is often provided by distributed generators, which are generally tested every This paper presents an application of data analysis of the electricity production at the country level, to calculate relevant performance indicators (i.e., PEFs, CO 2 emission factors and share of RES) of the electricity supplied by the National Power Grid.…”

Section: Introductionmentioning

confidence: 99%

Performance Indicators of Electricity Generation at Country Level—The Case of Italy

2018

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Abstract:Power Grids face significant variability in their operation, especially where there are high proportions of non-programmable renewable energy sources constituting the electricity mix. An accurate and up-to-date knowledge of operational data is essential to guaranteeing the optimal management of the network, and this aspect will be even more crucial for the full deployment of Smart Grids. This work presents a data analysis of the electricity production at the country level, by considering some performance indicators based on primary energy consumption, the share of renewable energy sources, and CO 2 emissions. The results show a significant variability of the indicators, highlighting the need of an accurate knowledge of operational parameters as a support for future Smart Grid management algorithms based on multi-objective optimization of power generation. The renewable share of electricity production has a positive impact, both on the primary energy factor and on the CO 2 emission factor. However, a strong increase of the renewable share requires that the supply/demand mismatching issues be dealt with through appropriate measures.

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“…Energy performance certificate; Building; GIS; Energy efficiency; Geographic Information System applied by Dall'O' et al (2015) who linked a large quantity of energy performance certificates with the buildings' geographic location, and so the results can be aggregated to undertake city-scale, or sub-city-scale (e.g. districts), energy analysis.…”

Section: Introductionmentioning

confidence: 99%

“…districts), energy analysis. Dall'O' et al (2015) also emphasised the importance of managing this ever increasing database, so such data can be associated with other national or regional datasets based on their geographic correlations.…”

Section: Introductionmentioning

confidence: 99%

City-wide Building Energy Efficiency Assessment Using EPC Data

Wu¹,

Blunden²,

Bahaj³

2018

Future Cities and Environment

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On the use of an energy certification database to create indicators for energy planning purposes: Application in northern Italy

Cited by 51 publications

References 21 publications

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

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

Performance Indicators of Electricity Generation at Country Level—The Case of Italy

City-wide Building Energy Efficiency Assessment Using EPC Data

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