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An energy performance assessment method for district heating substations based on energy disaggregation
2022, Energy and BuildingsA Meta Model Based Bayesian Approach for Building Energy Models Calibration
2017, Energy ProcediaA simultaneous calibration and parameter ranking method for building energy models
2017, Applied EnergyCitation Excerpt :In general, the calibration methods can be categorized as manual and automatic calibrations [12]. Manual calibration can be implemented through various ways, such as through the characterisation of the physical and operational properties of existing buildings [17], graphical representation of building data or statistical indices [18], parameter reductions [19,20], and data disaggregation [21]. The manual calibration methods are usually dependent on expert knowledge and judgement, which can be prone to error.
Evaluation of “Autotune” calibration against manual calibration of building energy models
2016, Applied EnergyCitation Excerpt :The relative development time of these models is short, although they can be brittle and require re-training to accommodate small changes in a building. Examples include models created by traditional regression [18–20], artificial neural networks [21–23], or support vector machines [24–26]. Grey-box models often use parameters identified from physical systems, can use decision tree [27,28] or Fourier series techniques [29–31], and can account for changes caused by simplified input parameters.
A bottom-up and procedural calibration method for building energy simulation models based on hourly electricity submetering data
2015, EnergyCitation Excerpt :1) Black-box models (data-driven models) are simple mathematical or statistical models resulting from long-term historical data training. These models have high requirements for the quality and quantity of data, but lack physical meaning, and they include traditional regression models [6–8], ANN (artificial neural network) models [9–11] and SVM (support vector machine) models [12–14]. 2) Grey-box models differ from black-box approaches in that they use certain parameters identified from physical system models, such as decision tree models [15,16] and Fourier series models [17–19].