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Tardioli, Giovanni
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Tardioli, Giovanni
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Tardioli, Giovanni
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Now showing 1 - 3 of 3
- PublicationA methodology for calibration of building energy models at district scale using clustering and surrogate techniques(Elsevier, 2020-11-01)
; ; ; ; ; Prediction of building energy use, when performed at urban scale, is influenced by the choice of modelling approach, as well as the quality of available data. In the case of data scarcity, one of the main limitations of current urban scale building energy simulation models, is the use of deterministic approaches to specify modelling inputs for entire classes of buildings. This modelling approach is characterised by three major shortcomings: first, data uncertainties are not comprehensively considered; second, a rigorous method of identification of groups of buildings to be populated by similar modelling parameters is not specified; and third, strategies to calibrate the developed energy models are missing. Considering these challenges, the current paper presents a non-deterministic calibration method for groups of buildings. The methodology utilises four techniques: (i) the use of clustering to identify building groups and associated representative buildings within the urban context; (ii) the application of an automatic building energy modelling approach to simulate buildings within these groups; (iii) the application of data-driven models, used as emulators of the dynamic simulation engine, in a computationally efficient manner; and (iv) the exploitation of a non-deterministic Bayesian calibration framework to identify sets of representative parameters for the building clusters. Datasets, based on 2646 buildings from the city of Geneva, Switzerland, are used as a case study. Validation with measured data, shows that energy consumption and energy intensity predictions when considered at an aggregated scale, are within +/– 5%. On an individual building basis, the validation error is within +/– 20% for approximately 70% of the buildings.319Scopus© Citations 32 - PublicationA Data-Driven Modelling Approach for Large Scale Demand Profiling of Residential Buildings(2017-08-01)
; ; ; ; In this paper the traditional use of data-driven models (DDM) as forecasting tools is coupled with parametric simulation to create a building modelling framework for demand profiling of a large number of buildings of the same typology. Most studies to date utilising DDM have been conducted on single buildings, with less evidence of the role that DDM may have as a modelling technique for application at scale. The proposed methodology is based on the use of a simulation-based building energy modelling tool and a parametric simulator to create a large dataset consisting of 4096 different building model scenarios. Three DDM techniques are utilised; Support Vector Machines, Neural Networks and Generalised Linear Models, these are trained and tested using the generated simulation dataset. Results, at an hourly resolution, show that DDM approaches can correctly emulate the outputs of the building simulation software with mean absolute error ranging from 4 to 9 percent for different DDM algorithms.144Scopus© Citations 2 - PublicationA Novel Hybrid Technique For Building Demand Forecasting Based On Data-driven And Urban Scale Simulation Approaches(IBPSA, 2019-09-04)
; ; ; ; This paper presents a novel feature engineering procedure to generate case study specific input variables for the training of data-driven models used to predict the heating demand of blocks of buildings. Traditionally, predictive model training is performed using sets of data from sensors (e.g. weather stations, metering systems). Feature engineering procedures such as the inclusion of innovative predictive variables in the forecasting framework are generally not considered. The method presented in this paper exploits results of calibrated physics-based building energy models that are included as an additional independent variable in combination with the traditional sets of predictors in an innovative forecasting framework. The method is tested on a district case study of the city of Geneva (CH) served by a district heating network. Results show that the presented approach improves the quality of the forecasting outcomes of state-of-the-art predictive algorithms. In this context, the accuracy of the simulation outputs affects the predictive capability of the presented forecasting procedure. In addition, normalised information derived from substation of the heating network of the district are informative for the predictive model.175