A review on the prediction of building energy consumption

Zhao, Hai‐Xiang; Magoulès, Frédéric

doi:10.1016/j.rser.2012.02.049

Cited by 1,596 publications

(675 citation statements)

References 62 publications

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“…The application of a multivariate linear regression model, to foresee possible energy savings from different retrofits actions, results in a very interesting solution, particularly when the energy simulation is not cost or time feasible. Statistical methods are currently used for the prediction of the energy performance of buildings [22].…”

Section: Introductionmentioning

confidence: 99%

Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting

et al. 2018

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A large part of the stock of Italian educational buildings have undertaken energy retrofit interventions, thanks to European funds allocated by complex technical-administrative calls. In these projects, the suggested retrofit strategies are often selected based on the common best practices (considering average energy savings) but are not supported by proper energy investigations. In this paper, Italian school buildings' stock was analyzed by cluster analysis with the aim of providing a methodology able to identify the best energy retrofit interventions from the perspective of cost-benefit, and to correlate them with the specific characteristics of the educational buildings. This research is based on the analysis of about 80 school buildings located in central Italy and characterized by different features and construction technologies. The refurbished buildings were classified in homogeneous clusters and, for each of them, the most representative building was identified. Furthermore, for each representative building a validating procedure based on dynamic simulations and a comparison with actual energy use was performed. The two buildings thus singled out provide a model that could be developed into a useful tool for Public Administrations to suggest priorities in the planning of new energy retrofits of existing school building stocks.

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Section: Introductionmentioning

confidence: 99%

Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting

et al. 2018

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“…Due to the complex nature of building energy system, it is quite difficult to achieve such an accurate prediction regardless of the used approach (Zhao, Magoulès 2012). Traditionally, lifecycle energy consumption is often projected with deterministic linear-average approach which ignores the longitudinal variability of ambient temperature and the residential thermal condition.…”

Section: Resultsmentioning

confidence: 99%

“…building external envelope U-value, ambient climate, building area, and so forth) were proposed. They included regression analysis (Catalina et al 2008), Fourier series models (Dhar et al 1998), decision tree (Tso, Yau 2007), support vector machine and neural network (Zhao, Magoulès 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Among of them, the neural network based artificial intelligence technique appears to be more accurate (Zhao, Magoulès 2012) due to its capability in adapting itself to the unforeseen pattern changes in the new available data (Catalina et al 2008;Yalcintas, Akkurt 2005). Yalcintas and Akkurt (2005) applied neural network to predict the chiller energy consumption in a tropical climate using both climatic and chiller data.…”

Section: Introductionmentioning

confidence: 99%

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Lifecycle Energy Consumption Prediction of Residential Buildings by Incorporating Longitudinal Uncertainties

Wang

Shen

2014

Journal of Civil Engineering and Management

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Abstract. Accurate prediction of buildings' lifecycle energy consumption is a critical part in lifecycle assessment of residential buildings. Longitudinal variations in building conditions, weather conditions and building's service life can cause significant deviation of the prediction from the real lifecycle energy consumption. The objective is to improve the accuracy of lifecycle energy consumption prediction by properly modelling the longitudinal variations in residential energy consumption model using Markov chain based stochastic approach. A stochastic Markov model considering longitudinal uncertainties in building condition, degree days, and service life is developed: 1) Building's service life is estimated through Markov deterioration curve derived from actual building condition data; 2) Neural Network is used to project periodic energy consumption distribution for each joint energy state of building condition and temperature state; 3) Lifecycle energy consumption is aggregated based on Markov process and the state probability. A case study on predicting lifecycle energy consumption of a residential building is presented using the proposed model and the result is compared to that of a traditional deterministic model and three years' measured annual energy consumptions. It shows that the former model generates much narrower distribution than the latter model when compared to the measured data, which indicates improved result.

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“…The Swedish government has set an additional target of reducing the energy usage in the building sector by 50% by 2050 [3]. Having an overview of energy usage in the building stock is necessary for creating a refurbishment strategy, as engineering-based models are used in refurbishment strategies to predict energy savings for buildings after the application of renovation measures [4][5][6]. The difficulties with using engineering-based models on a city scale is that reliable data on energy usage on the building level is limited [7].…”

Section: Introductionmentioning

confidence: 99%

Handling data uncertainties when using Swedish energy performance certificate data to describe energy usage in the building stock

Mangold

Österbring

Wallbaum

2015

Energy and Buildings

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a b s t r a c tThe EU directive to create Energy Performance Certificates (EPC) for all buildings was implemented in Sweden as a tool to advise building owners on possible improvements and to give energy efficiency visible market value. The Swedish EPCs include measured energy usage. Currently 82% of the buildings have EPCs; this database makes it possible to create overview and to validate models of the building stock in an unprecedented high detail.However, the process of issuing Swedish EPCs has received criticism from real estate agents, real estate owners, Energy Experts, and Boverket, the national agency responsible for EPC data collection. In order to use the EPC data for describing the building stock it is necessary to assess and remediate the data quality. This has been done by merging the EPC data with databases of the Housing and Urban Development office and one of the larger real estate companies in Sweden, Riksbyggen. The Swedish EPC specific area measurement, Atemp, is found to vary according to methods of derivation. The method of estimating Atemp is improved using a stepwise regression model (R 2 = 0.979). This method can be applied to subsets of EPCs depending on the intended way of describing the building stock.

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A review on the prediction of building energy consumption

Cited by 1,596 publications

References 62 publications

Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting

Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting

Lifecycle Energy Consumption Prediction of Residential Buildings by Incorporating Longitudinal Uncertainties

Handling data uncertainties when using Swedish energy performance certificate data to describe energy usage in the building stock

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