Application of neural networks for evaluating energy performance certificates of residential buildings

Khayatian, Fazel; Sarto, Luca; Dall’O’, Giuliano

doi:10.1016/j.enbuild.2016.04.067

Cited by 104 publications

(41 citation statements)

References 23 publications

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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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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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“…These features were extracted from previously defined energy certificates issued by a municipality in Italy. Although most benchmarking methods are concerned with overall building energy consumption, Khayatian et al (2016) focused specifically on heating energy consumption. Again, time slices and algorithm selection were not considered.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, unlike our approach, Capozzoli et al (2016) did not address separate benchmarking for different time periods. Khayatian et al (2016) evaluated annual energy performance of residential buildings based on neural networks. They focused on determining the optimal subset of input features, the number of NN layers, and the number of neurons.…”

Section: Related Workmentioning

confidence: 99%

Energy slices: benchmarking with time slicing

et al. 2017

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Benchmarking makes it possible to identify low-performing buildings, establishes a baseline for measuring performance improvements, enables setting of energy conservation targets, and encourages energy savings by creating a competitive environment. Statistical approaches evaluate building energy efficiency by comparing measured energy consumption to other similar buildings typically using annual measurements. However, it is important to consider different time periods in benchmarking because of differences in their consumption patterns. For example, an office can be efficient during the night, but inefficient during operating hours due to occupants' wasteful behavior. Moreover, benchmarking studies often use a single regression model for different building categories. Selecting the regression model based on actual data would ensure that the model fits the data well. Consequently, this paper proposes Energy Slices, an energy benchmarking approach with time slicing for existing buildings. Time slicing enables separation of time periods with different consumption patterns. The regression model suited for the specific scenario is selected using cross validation, which ensures that the model performs well on previously unseen data. The evaluation is carried out on a case study involving two sports arenas; event energy efficiency is benchmarked to identify low-performing events. The case study demonstrates the Energy Slice procedure and shows the importance of model selection.

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“…It allows the construction of relationships between input parameters and output parameters using artificial neurons, which are arranged in an input layer, an output layer and one or more hidden layers [18]. Analyses were conducted using the multilayer back-propagation neural network.…”

Section: Ann Approachmentioning

confidence: 99%

Smart Building: Use of the ANN Approach for Indoor Temperature Forecasting

Attoue¹,

Shahrour²,

Younès³

2018

Preprint

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Smart buildings concept aims at the use of the smart technology to reduce energy consumption as well as improvement of the comfort conditions and users' satisfaction. It is based on the use of smart sensors to follow both outdoor and indoor conditions as well as software for the control of comfort and security devices. The optimal control of the energy devices requires software for indoor temperature forecasting. This paper presents an ANN -based model for the indoor temperature forecasting. The model is developed using data recoded in an old building of the engineering school Polytech'Lille. Data covered both indoor and outdoor conditions. Analysis of the relevance of the input parameters allowed to develop a simplified forecasting model of the indoor temperature that uses only the outdoor temperature as well as the history of the façade temperature as input parameters. The paper presents successively, data collection, the ANN concept used in the temperature forecasting, and finally the ANN model developed for the façade and indoor forecasting. It shows that an ANN-based model using outdoor and façade temperature sensors provides a good forecasting of the indoor temperature. This model could be used for the optimal control of buildings energy devices.

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Application of neural networks for evaluating energy performance certificates of residential buildings

Cited by 104 publications

References 23 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

Energy slices: benchmarking with time slicing

Smart Building: Use of the ANN Approach for Indoor Temperature Forecasting

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