scholarly journals Stochastic energy-demand analyses with random input parameters for the single-family house

2021 ◽  
Author(s):  
Marcin Koniorczyk ◽  
Witold Grymin ◽  
Marcin Zygmunt ◽  
Dalia Bednarska ◽  
Alicja Wieczorek ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Energy Consumption ◽  
Monte Carlo Method ◽  
Energy Demand ◽  
Stochastic Perturbation ◽  
Random Variable ◽  
Single Family ◽  
The Monte Carlo Method ◽  
Stochastic Perturbation Method ◽  
Family House

AbstractIn the analyses of the uncertainty propagation of buildings’ energy-demand, the Monte Carlo method is commonly used. In this study we present two alternative approaches: the stochastic perturbation method and the transformed random variable method. The energy-demand analysis is performed for the representative single-family house in Poland. The investigation is focused on two independent variables, considered as uncertain, the expanded polystyrene thermal conductivity and external temperature; however the generalization on any countable number of parameters is possible. Afterwards, the propagation of the uncertainty in the calculations of the energy consumption has been investigated using two aforementioned approaches. The stochastic perturbation method is used to determine the expected value and central moments of the energy consumption, while the transformed random variable method allows to obtain the explicit form of energy consumption probability density function and further characteristic parameters like quantiles of energy consumption. The calculated data evinces a high accordance with the results obtained by means of the Monte Carlo method. The most important conclusions are related to the computational cost reduction, simplicity of the application and the appropriateness of the proposed approaches for the buildings’ energy-demand calculations.

scholarly journals Analysis of the Influence Subjective Human Parameters in the Calculation of Thermal Comfort and Energy Consumption of Buildings

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1531 ◽  
Author(s):  
Roberto Robledo-Fava ◽  
Mónica C. Hernández-Luna ◽  
Pedro Fernández-de-Córdoba ◽  
Humberto Michinel ◽  
Sonia Zaragoza ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Energy Consumption ◽  
Monte Carlo Method ◽  
Numerical Model ◽  
Thermal Comfort ◽  
Energy Demand ◽  
Measured Data ◽  
Point Temperature ◽  
Set Point ◽  
Indoor Comfort

In the present work, we analyze the influence of the designer’s choice of values for the human metabolic index (met) and insulation by clothing (clo) that can be selected within the ISO 7730 for the calculation of the energy demand of buildings. To this aim, we first numerically modeled, using TRNSYS, two buildings in different countries and climatologies. Then, we consistently validated our simulations by predicting indoor temperatures and comparing them with measured data. After that, the energy demand of both buildings was obtained. Subsequently, the variability of the set-point temperature concerning the choice of clo and met, within limits prescribed in ISO 7730, was analyzed using a Monte Carlo method. This variability of the interior comfort conditions has been finally used in the numerical model previously validated, to calculate the changes in the energy demand of the two buildings. Therefore, this work demonstrated that the diversity of possibilities offered by ISO 7730 for the choice of clo and met results, depending on the values chosen by the designer, in significant differences in indoor comfort conditions, leading to non-negligible changes in the calculations of energy consumption, especially in the case of big buildings.

Exit Condition for Probabilistic Assessment Using Monte Carlo Method

2010 ◽  
Vol 10 (1) ◽  
pp. 1-9
Author(s):  
Jakub Valihrach ◽  
Petr Konečný
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Optimization Design ◽  
Random Variable ◽  
Probability Of Failure ◽  
Probabilistic Assessment ◽  
Theoretical Derivation ◽  
The Monte Carlo Method ◽  
The Relationship

Exit Condition for Probabilistic Assessment Using Monte Carlo Method This paper introduces a condition used to exit a probabilistic assessment using the Monte Carlo simulation, and to evaluate it with regard to the relationship between the computed estimate of the probability of failure and the target design probability. The estimation of probability of failure is treated as a random variable, considering its variance that is dependent on the number of performed Monte Carlo simulation steps. After theoretical derivation of the decision condition, it is tested numerically with regard to its accuracy and computational efficiency. The condition is suitable for optimization design using the Monte Carlo method.

Estimation of Random Variable Distribution Parameters by the Monte Carlo Method

2013 ◽  
Vol 20 (2) ◽  
pp. 249-262 ◽  
Author(s):  
Sergiusz Sienkowski
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Random Variable ◽  
Statistical Parameters ◽  
The Monte Carlo Method ◽  
Variable Distribution ◽  
Distribution Parameters ◽  
The Subject ◽  
The Mean ◽  
Typical Measurement

Abstract The paper is concerned with issues of the estimation of random variable distribution parameters by the Monte Carlo method. Such quantities can correspond to statistical parameters computed based on the data obtained in typical measurement situations. The subject of the research is the mean, the mean square and the variance of random variables with uniform, Gaussian, Student, Simpson, trapezoidal, exponential, gamma and arcsine distributions.

scholarly journals The impact of using batteries in micro PV system on profitability of the investment

2018 ◽  
Vol 49 ◽  
pp. 00013 ◽  
Author(s):  
Bartosz Chwieduk ◽  
Michał Chwieduk
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Electricity Consumption ◽  
Photovoltaic System ◽  
Single Family ◽  
Pv System ◽  
Pv Systems ◽  
Pv Modules ◽  
The Impact ◽  
Family House

The paper presents the results of calculations of energy consumption and economic analysis of the operation of micro photovoltaic installations. Calculations have been made for a single-family house with an energy demand based on real electricity consumption. Two cases have been considered. In the first one, the photovoltaic system contains only PV modules and an inverter. Energy produced is sent to the power grid. In the second case, the PV system also contains batteries. Because of existing regulation conditions, it is better to accumulate produced energy than to sell it to the grid. Costs of construction of the PV systems and money savings during operation of the PV systems have been compared. Conclusions of profitability of analyzed systems have been presented.

scholarly journals Error propagation in the computation of volumes in 3D city models with the Monte Carlo method

2014 ◽  
Vol II-2 ◽  
pp. 31-39 ◽  
Author(s):  
F. Biljecki ◽  
H. Ledoux ◽  
J. Stoter
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Error Propagation ◽  
Energy Demand ◽  
3D Gis ◽  
3D City Models ◽  
The Monte Carlo Method ◽  
Propagation Analysis ◽  
Error Propagation Analysis ◽  
City Models

This paper describes the analysis of the propagation of positional uncertainty in 3D city models to the uncertainty in the computation of their volumes. Current work related to error propagation in GIS is limited to 2D data and 2D GIS operations, especially of rasters. In this research we have (1) developed two engines, one that generates random 3D buildings in CityGML in multiple LODs, and one that simulates acquisition errors to the geometry; (2) performed an error propagation analysis on volume computation based on the Monte Carlo method; and (3) worked towards establishing a framework for investigating error propagation in 3D GIS. The results of the experiments show that a comparatively small error in the geometry of a 3D city model may cause significant discrepancies in the computation of its volume. This has consequences for several applications, such as in estimation of energy demand and property taxes. The contribution of this work is twofold: this is the first error propagation analysis in 3D city modelling, and the novel approach and the engines that we have created can be used for analysing most of 3D GIS operations, supporting related research efforts in the future.

scholarly journals IMPROVEMENT OF THE DESIGN ALGORITHM OF SWITCHED-RELUCTANCE MACHINES

2021 ◽  
pp. 140-155
Author(s):  
Anastasiya V. Shevkunova ◽  
Alexandr V. Kashuba
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Optimization Algorithm ◽  
Random Variable ◽  
Switched Reluctance Machine ◽  
Design Algorithm ◽  
Switched Reluctance ◽  
Reluctance Machine ◽  
Switched Reluctance Machines ◽  
The Monte Carlo Method

The issue of improving the technical and economic indicators of switched-reluctance machines at the stage of their design has a significant degree of relevance. This study is devoted to improving the optimization algorithm for designing electric machines of the valve-inductor type. Parametric, single-criteria optimization was subject to consideration. The task of designing a magnetic system of a switched-reluctance machine is to find the optimal combination of values of geometric parameters, at which the value of the objective function reaches an extremum. Within the framework of this work, optimization was considered by the criterion of the minimum pulsations of the electromagnetic moment at low rotational speeds. The stochastic method – the Monte-Carlo method – was used as the basis for making changes to improve the efficiency of the optimization algorithm. The essence of the changes is to apply a normal distribution of a random variable with decreasing variance and with a variable value of the mathematical expectation instead of using a uniform distribution. For this study, methods of mathematical modeling were used, namely the Monte-Carlo method and methods of probability theory. Calculations of the magnetic field of the switched-reluctance machine were performed using the FEMM 4.2 program based on the finite element method. Due to the changes made to the basic optimization algorithm, the effectiveness of such a criterion as the time to achieve the final result with a given calculation accuracy has become higher. The obtained data can be practically useful in the development of manufacturing technology for the object of optimization.

scholarly journals Statistical analyses of the energy demand and thermal comfort for multiple uncertain input parameters, performed using transformed variable and perturbation method

2021 ◽  
Vol 2069 (1) ◽  
pp. 012219
Author(s):  
Witold Grymin ◽  
Marcin Koniorczyk ◽  
Marcin Zygmunt ◽  
Dariusz Gawin
Keyword(s):  
Thermal Comfort ◽  
Perturbation Method ◽  
Energy Demand ◽  
Polynomial Regression ◽  
Stochastic Perturbation ◽  
Random Variables ◽  
Random Variable ◽  
Response Functions ◽  
Input Parameters ◽  
Stochastic Perturbation Method

Abstract In the calculations of buildings’ thermal comfort, the input parameters are usually considered as strictly determined values. Numerous of them may be characterized by certain probability density functions. In the energy related problems, the uncertainty analyses are usually performed using the Monte Carlo method. However, this method requires multiple calculations and, therefore, may be very time-consuming. In the proposed work, two approaches are applied for the probabilistic studies: the stochastic perturbation method and the transformed random variables method. The stochastic analysis is based on the response functions and their derivatives with respect to all random input parameters. The relation between the thermal comfort and the input (random) variables have been calculated using the Energy Plus software. Afterwards, the response functions were estimated using the polynomial regression. The expected value and central moments of the response functions were calculated by means of the perturbation method and the transformed random variable theorem. The latter method allowed to obtain, using the same response functions, the implicit form of probability distributions function of the output parameter.

UGR CALCULATION BASED ON THE LUMINANCE SPATIAL-ANGULAR DISTRIBUTION

2020 ◽  
Vol 2020 (4) ◽  
pp. 25-32
Author(s):  
Viktor Zheltov ◽  
Viktor Chembaev
Keyword(s):  
Monte Carlo ◽  
Angular Distribution ◽  
Monte Carlo Method ◽  
Visual Task ◽  
New Method ◽  
Lighting System ◽  
Preliminary Assessment ◽  
System A ◽  
The Monte Carlo Method

The article has considered the calculation of the unified glare rating (UGR) based on the luminance spatial-angular distribution (LSAD). The method of local estimations of the Monte Carlo method is proposed as a method for modeling LSAD. On the basis of LSAD, it becomes possible to evaluate the quality of lighting by many criteria, including the generally accepted UGR. UGR allows preliminary assessment of the level of comfort for performing a visual task in a lighting system. A new method of "pixel-by-pixel" calculation of UGR based on LSAD is proposed.

PSHA software review based on the Monte Carlo method

2020 ◽  
pp. 14-24
Author(s):  
V.A. Mironov ◽  
S.A. Peretokin ◽  
K.V. Simonov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Seismic Hazard ◽  
Hazard Analysis ◽  
Software Review ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Test Calculation ◽  
Seismic Surveys ◽  
The Monte Carlo Method

The article is a continuation of the software research to perform probabilistic seismic hazard analysis (PSHA) as one of the main stages in engineering seismic surveys. The article provides an overview of modern software for PSHA based on the Monte Carlo method, describes in detail the work of foreign programs OpenQuake Engine and EqHaz. A test calculation of seismic hazard was carried out to compare the functionality of domestic and foreign software.

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