An Integrated Energy Simulation Model for Buildings

Kampelis, Nikos; Papayiannis, Georgios I.; Kolokotsa, Dionysia; Galanis, George; Isidori, Daniela; Cristalli, Cristina; Yannacopoulos, A. N.

doi:10.3390/en13051170

Cited by 17 publications

(8 citation statements)

References 42 publications

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“…With a proper balance between complexity and accuracy, simulations can be considered as a key practice to support the systems performance assessment and operation [33]. Moreover, the continuous calibration of dynamic energy models against monitoring data is a key point to minimize the performance gap [34][35][36][37] and to obtain reliable energy performance forecast to be used by energy management actions.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood Energy Modeling and Monitoring: A Case Study

et al. 2021

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Cities and nations worldwide are pledging to energy and carbon neutral objectives that imply a huge contribution from buildings. High-performance targets, either zero energy or zero carbon, are typically difficult to be reached by single buildings, but groups of properly-managed buildings might reach these ambitious goals. For this purpose we need tools and experiences to model, monitor, manage and optimize buildings and their neighborhood-level systems. The paper describes the activities pursued for the deployment of an advanced energy management system for a multi-carrier energy grid of an existing neighborhood in the area of Milan. The activities included: (i) development of a detailed monitoring plan, (ii) deployment of the monitoring plan, (iii) development of a virtual model of the neighborhood and simulation of the energy performance. Comparisons against early-stage energy monitoring data proved promising and the generation system showed high efficiency (EER equal to 5.84), to be further exploited.

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Section: Introductionmentioning

confidence: 99%

Neighborhood Energy Modeling and Monitoring: A Case Study

et al. 2021

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“…This type of statistical modeling have found several applications in practice so far, e.g., in image and signal processing Bigot et al (2009Bigot et al ( , 2011, in analysing point processes Panaretos et al (2016), in medicine , in electric energy prediction Kampelis et al (2020), etc. The reason behind the growing popularity of this approach is that although fully non-parametric approaches seem very tempting in theory, in practice they rarely can be used for statistical inference when comparisons are needed.…”

Section: Deformation Modelsmentioning

confidence: 99%

Statistical monitoring of functional data using the notion of Fréchet mean combined with the framework of the deformation models

Papayiannis¹,

Psarakis²,

Yannacopoulos³

2020

Preprint

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The aim of this paper is to investigate possible advances obtained by the implementation of the framework of Fréchet mean and the generalized sense of mean that it offers, in the field of statistical process monitoring and control. In particular, the case of non-linear profiles which are described by data in functional form is considered and a framework combining the notion of Fréchet mean and deformation models is developed. The proposed monitoring approach is implemented to the intra-day air pollution monitoring task in the city of Athens where the capabilities and advantages of the method are illustrated.

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“…The second category, white-box models, are models based on physical principles. They take into account all the variables that define the building, both its formal characteristics and its loads and environment [ 25 , 26 , 27 ]. They do not require any training process, although they need a complete definition of all the aspects that affect the building’s energy demand and its subsequent adjustment to obtain reliable results (calibration).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Segarra

Ruiz

Bandera

2021

Sensors

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Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.

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An Integrated Energy Simulation Model for Buildings

Cited by 17 publications

References 42 publications

Neighborhood Energy Modeling and Monitoring: A Case Study

Neighborhood Energy Modeling and Monitoring: A Case Study

Statistical monitoring of functional data using the notion of Fréchet mean combined with the framework of the deformation models

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

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