Improving cooling load prediction reliability for HVAC system using Monte-Carlo simulation to deal with uncertainties in input variables

2020 ◽  
Vol 226 ◽  
pp. 110372 ◽  
Author(s):  
Chengliang Fan ◽  
Yundan Liao ◽  
Guang Zhou ◽  
Xiaoqing Zhou ◽  
Yunfei Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cooling Load ◽  
Hvac System ◽  
Load Prediction ◽  
Input Variables ◽  
Prediction Reliability

scholarly journals Practical Risk Assessment of Ground Vibrations Resulting from Blasting, Using Gene Expression Programming and Monte Carlo Simulation Techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 472 ◽  
Author(s):  
Amir Mahdiyar ◽  
Danial Jahed Armaghani ◽  
Mohammadreza Koopialipoor ◽  
Ahmadreza Hedayat ◽  
Arham Abdullah ◽  
...  
Keyword(s):  
Gene Expression ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Gene Expression Programming ◽  
Sensitivity Analyses ◽  
Accurate Estimation ◽  
Blasting Operation ◽  
Simulation Techniques ◽  
Input Variables ◽  
The Impact

Peak particle velocity (PPV) is a critical parameter for the evaluation of the impact of blasting operations on nearby structures and buildings. Accurate estimation of the amount of PPV resulting from a blasting operation and its comparison with the allowable ranges is an integral part of blasting design. In this study, four quarry sites in Malaysia were considered, and the PPV was simulated using gene expression programming (GEP) and Monte Carlo simulation techniques. Data from 149 blasting operations were gathered, and as a result of this study, a PPV predictive model was developed using GEP to be used in the simulation. In order to ensure that all of the combinations of input variables were considered, 10,000 iterations were performed, considering the correlations among the input variables. The simulation results demonstrate that the minimum and maximum PPV amounts were 1.13 mm/s and 34.58 mm/s, respectively. Two types of sensitivity analyses were performed to determine the sensitivity of the PPV results based on the effective variables. In addition, this study proposes a method specific to the four case studies, and presents an approach which could be readily applied to similar applications with different conditions.

scholarly journals Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

2020 ◽  
Vol 10 (4) ◽  
pp. 1403 ◽  
Author(s):  
Zhi Yu ◽  
Xiuzhi Shi ◽  
Jian Zhou ◽  
Xin Chen ◽  
Xianyang Qiu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Rock Mass ◽  
Random Forest ◽  
Particle Velocity ◽  
Ground Vibration ◽  
Accurate Method ◽  
Peak Particle Velocity ◽  
Simulation Approach ◽  
Input Variables

Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control the blast-induced ground vibration, the random forest (RF) model, Harris hawks optimization (HHO) algorithm and Monte Carlo simulation approach were utilized. A database consisting of 137 datasets was collected at different locations around the Tonglvshan open-cast mine, China. Seven variables were selected and collected as the input variables, and peak particle velocity was chosen as the output variable. At first, an RF model and a hybrid model, namely a HHO-RF model, were developed, and the prediction results checked by 3 performance indices to show that the proposed HHO-RF model can provide higher prediction performance. Then blast-induced ground vibration was simulated by using the Monte Carlo simulation approach and the developed HHO-RF model. After analyzing, the mean peak particle velocity value was 0.98 cm/s, and the peak particle velocity value did not exceed 1.95 cm/s with a probability of 90%. The research results of this study provided a simple, accurate method and basis for predicting, evaluating blast-induced ground vibration and optimizing the blast design before blast operation.

Review and Evaluation of Using R-C Thermal Modeling of Cooling Load Prediction for HVAC System Control Purpose

2012 ◽  
Author(s):  
Oluwaseyi T. Ogunsola ◽  
Li Song
Keyword(s):  
Thermal Modeling ◽  
Model Performance ◽  
Hvac Systems ◽  
Measured Data ◽  
System Control ◽  
Cooling Load ◽  
Hvac System ◽  
Monitoring And Control ◽  
Load Prediction ◽  
Total Energy Consumption

Heating and cooling loads which are compensated by heating, ventilation, and air-conditioning (HVAC) systems, are the main reason for energy uses in buildings. Energy utilized by HVAC system accounts for two-thirds of a building’s total energy consumption. Excessive energy is consumed when HVAC systems fail to operate as intended. This is often due to several factors such as inappropriate monitoring and control strategy, lack of understanding of the dynamics of thermal loads, and system complexity. Amidst several models, estimation of cooling load using Resistance Capacitance (RC) models have proved to provide more robust and accurate estimates of the building load based on measured data but the use of this method is not without challenges. This study aims to highlight common challenges associated with implementation of the RC method for thermal modeling of cooling load. Past and current research have handled some of the challenges by introducing simplifying assumptions which if not adequately selected can lead to significant deviation between model performance and measured data. Without proper understanding of the challenges, engineers may not be able to place a high degree of confidence in load calculation methods and the computer implementations that they use.

scholarly journals Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1900 ◽  
Author(s):  
Jing Zhao ◽  
Yaoqi Duan ◽  
Xiaojuan Liu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Load Forecasting ◽  
Weather Forecast ◽  
Weather Data ◽  
Support Vector ◽  
Cooling Load ◽  
Load Prediction ◽  
The Monte Carlo Method ◽  
Forecast Data

Recently, the cooling load forecasting for the short-term has received increasing attention in the field of heating, ventilation and air conditioning (HVAC), which is conducive to the HVAC system operation control. The load forecasting based on weather forecast data is an effective approach. The meteorological parameters are used as the key inputs of the prediction model, of which the accuracy has a great influence on the prediction loads. Obviously, there are errors between the weather forecast data and the actual weather data, but most of the existing studies ignored this issue. In order to deal with the uncertainty of weather forecast data scientifically, this paper proposes an effective approach based on the Monte Carlo Method (MCM) to process weather forecast data by using the 24-h-ahead Support Vector Machine (SVM) model for load prediction as an example. The data-preprocessing method based on MCM makes the forecasting results closer to the actual load than those without process, which reduces the Mean Absolute Percentage Error (MAPE) of load prediction from 11.54% to 10.92%. Furthermore, through sensitivity analysis, it was found that among the selected weather parameters, the factor that had the greatest impact on the prediction results was the 1-h-ahead temperature T(h–1) at the prediction moment.

Influence of the dispersion number on the estimation of coliform removal in ponds

2003 ◽  
Vol 48 (2) ◽  
pp. 181-188 ◽  
Author(s):  
M. von Sperling
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Input Data ◽  
Flow Model ◽  
The Other ◽  
Empirical Equations ◽  
Input Variables ◽  
High Level ◽  
Dispersion Number

The paper uses the technique of Monte Carlo simulation in order to investigate the sensitivity to the Dispersion Number (d) of the effluent coliform concentration from facultative and maturation ponds. The Dispersion Number is one of the coefficients used in the dispersed-flow model for ponds. Initially, four empirical equations available in the literature for the estimation of d were compared, using the results from 1,000 runs of the Monte Carlo simulation. A second different set of Monte Carlo simulations (1,000 runs) was undertaken, allowing a sensitivity analysis of d, in conjunction with other coefficients and input data used in the design of facultative and maturation ponds (e.g., population, wastewater flow, coliform removal coefficient and others). The results of the simulations suggest that, when considering the high level of uncertainty in all input variables used in the design of the ponds for coliform removal, the Dispersion Number d does not present a greater influence in the model prediction, compared with the other input variables. Based on these considerations, it is likely that, for design purposes, simple models for the prediction of d can be used, without significantly affecting the estimation of the effluent coliform concentration, considering the existing uncertainty in all other input variables.

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

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