Uncertainty estimation and Monte Carlo simulation method

Abstract: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.

show abstract

“…. , X n ) are obtained by direct or indirect sampling, then the value y i of the function Y can be determined according to Formula (2) [26]:…”

Section: The Principle Of the MCMmentioning

confidence: 99%

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

2018

View full text Add to dashboard Cite

show abstract

“…To corroborate these measurements, we estimated µ s values using publicly-available Mie scattering code [23,24] and a Monte Carlo numerical approach to propagate uncertainty in the input parameters for Mie calculations [25]. Two parameters were treated as certain, wavelength (830 nm) and suspension concentration (1.97 × 10 9 particles/mL), and three parameters were treated as uncertain with Gaussian-distributed values: microsphere diameter (1.57 ± 0.18 µm), medium refractive index n medium , and microsphere refractive index n sphere .…”

Section: Characterization Of Suspensionsmentioning

confidence: 99%

Methods to assess sensitivity of optical coherence tomography systems

Agrawal

Pfefer

Woolliams

et al. 2017

Biomed. Opt. Express

View full text Add to dashboard Cite

Measuring the sensitivity of an optical coherence tomography (OCT) system determines the minimum sample reflectivity it can detect and provides a figure of merit for system optimization and comparison. The published literature lacks a detailed description of OCT sensitivity measurement procedures. Here we describe a commonly-used measurement method and introduce two new phantom-based methods, which also offer a means to directly visualize low reflectivity conditions relevant to biological tissue. We provide quantitative results for the three methods from different OCT system configurations and discuss the methods' advantages and disadvantages.

show abstract

“…4 was obtained using a Monte Carlo uncertainty simulation. 20 Table 2 lists the parameters used in the calculation of B t from Eq. (2) and their uncertainties.…”

Section: Uncertainty Analysismentioning

confidence: 99%

Measurement of low-frequency tissue response of the seagrass Posidonia oceanica

Johnson

Venegas

Wilson

et al. 2017

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

A one-dimensional acoustic resonator technique was used to study leaves of the Mediterranean seagrass species Posidonia oceanica collected from Crete and Sicily. The leaf blades were finely divided, mixed with artificial seawater, and degassed to create a suspension of tissue independent of leaf structure and free bubbles or internal voids. The low-frequency (1 to 8 kHz) bulk modulus of the leaf tissue was inferred from the acoustic measurements and independent density measurements. The measured density of the seagrass tissue was 960 ± 20 kg/m3 which agrees with previously published values. The inferred bulk modulus was 2.1 GPa with 90% confidence limits 1.0–5.0 GPa.

show abstract

Uncertainty estimation and Monte Carlo simulation method

Cited by 161 publications

References 1 publication

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

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

Methods to assess sensitivity of optical coherence tomography systems

Measurement of low-frequency tissue response of the seagrass Posidonia oceanica

Contact Info

Product

Resources

About