scholarly journals An Integrated Sensitivity Analysis Method for Energy and Comfort Performance of an Office Building along the Chinese Coastline

Buildings ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 371
Author(s):  
Ruijun Chen ◽  
Yaw-Shyan Tsay
Keyword(s):  
Sensitivity Analysis ◽  
Random Sampling ◽  
Energy Demand ◽  
Sampling Methods ◽  
Latin Hypercube Sampling ◽  
Building Energy ◽  
Assessment Method ◽  
Solar Absorptance ◽  
Sensitivity Assessment ◽  
Input Parameters

This study aimed to evaluate the comprehensive percentage influence of input parameters on building energy and comfort performance by a new approach of sensitivity analysis (SA) and explore the most reliable and neutral sampling and sensitivity assessment method. The research combined 7 sampling methods with 13 SA methods to comprehensively integrate the percentage influence of 25 input parameters on building energy and comfort performance in 24 coastal cities of China. The results have found that the percentage influence of many important input parameters is affected by geographical position. Considering both energy and comfort performance of the building, the key parameters are heating setpoint, infiltration rate, cooling setpoint, roof U value, roof solar absorptance, window solar heat gain coefficient, equipment, and occupant density, all of which could comprehensively impact 70% of energy demand and comfort performance along the Chinese coastline. This is of great significance for policymakers to formulate relative building regulations. After comparing the F-test and the exceed percentage test, we recommended the Pearson with Quasi-random sampling method as the most reliable SA assessment method in building simulation, followed by the standardized regression coefficient in random sampling and Latin hypercube sampling methods, which can achieve data closest to the average value.

Uncertainty and Sensitivity Analysis of Building Energy Demand Model Based on Occupant Behaviors

2011 ◽  
Vol 71-78 ◽  
pp. 411-415
Author(s):  
Zhi Bin Zhao ◽  
Wei Sheng Xu ◽  
Da Zhang Cheng
Keyword(s):  
Sensitivity Analysis ◽  
Real Time ◽  
Energy Demand ◽  
Dynamic Network ◽  
Building Energy ◽  
Demand Model ◽  
Model Framework ◽  
Basic Model ◽  
Dynamic Relation ◽  
Input Parameters

This paper attempts to illustrate Uncertainty Analysis (UA) and Sensitivity Analysis (SA) of real-time building energy demand model, which is derived from the dynamic relation of occupant behaviors and building space. UA and SA are indispensable sections of system development to insure the efficiency and accuracy of the model while there are three essential stage of UA and SA we followed. In terms of UA and SA, it is possible to structure a rational framework of complex dynamic network model, and discover the mapping between building energy demand and particular relation network patterns. We assume, firstly, a multi-mode dynamic relation networks model of occupant behavior, building space and temporal unit tends to be developed, and the definitions of basic model framework are given. Then, in the cases of the definitions in the basic model framework assumption, the propagation of uncertainty is taken into consideration according to the sampling based methods mapping the input parameters patterns onto the predictable results. Finally, we discuss the determination of sensitivity analysis with Morris method and Variance-based methods. In this paper, via UA and SA, our goal is to optimize the mapping procedure of the Dynamic Network Analysis (DNA) in building energy demand model, explore the essential input parameters pattern, and improve the precision of real-time model prediction.

scholarly journals Sensitivity analysis on the pollutant trapping efficiencies of a novel sewerage overflow screening device

2016 ◽  
Vol 18 (6) ◽  
pp. 1007-1018
Author(s):  
M. A. Aziz ◽  
M. A. Imteaz ◽  
H. M. Rasel ◽  
M. Samsuzzoha
Keyword(s):  
Sensitivity Analysis ◽  
Sampling Technique ◽  
Latin Hypercube Sampling ◽  
Capture Efficiency ◽  
Industry Standard ◽  
Informed Decisions ◽  
Input Parameters ◽  
Perform Sensitivity Analysis ◽  
Hydro Jet ◽  
Screening Device

A novel ‘Comb Separator’ was developed and tested with the aim of improving sewer solids capture efficiency and reducing blockages on the screen. Experimental results were compared against the industry standard ‘Hydro-Jet™’ screen. Analysing the parameter sensitivity of a hydraulic screen is a standard practice to get better understanding of the device performance. In order to understand the uncertainties of the Comb Separator's input parameters, it is necessary to undertake sensitivity analysis; this will assist in making informed decisions regarding the use of this device. Such analysis will validate the device's performance in urban sewerage overflow scenarios. The methodology includes multiple linear regression and sampling using the standard Latin hypercube sampling technique to perform sensitivity analysis on different experimental parameters, such as flowrate, effective comb spacing, device runtime, weir opening and comb layers. The input parameters ‘weir opening’ and ‘comb layers’ have an insignificant influence on capture efficiency; hence, they were omitted from further analysis. Among the input parameters, ‘effective spacing’ was the most influential, followed by ‘inflow’ and ‘runtime’. These analyses provide better insights about the sensitivities of the parameters for practical application. This will assist device managers and operators to make informed decisions.

scholarly journals Sensitivity analysis of data-driven building energy demand forecasts

2019 ◽  
Vol 1343 ◽  
pp. 012062
Author(s):  
Felix Bünning ◽  
Philipp Heer ◽  
Roy S. Smith ◽  
John Lygeros
Keyword(s):  
Sensitivity Analysis ◽  
Energy Demand ◽  
Building Energy ◽  
Data Driven ◽  
Demand Forecasts

scholarly journals Sensitivity analysis and parameter selection for detecting aggregations in acoustic data

2006 ◽  
Vol 64 (1) ◽  
pp. 160-168 ◽  
Author(s):  
Julian M. Burgos ◽  
John K. Horne
Keyword(s):  
Sensitivity Analysis ◽  
Latin Hypercube Sampling ◽  
Detection Algorithm ◽  
Design Sensitivity ◽  
Walleye Pollock ◽  
Parameter Selection ◽  
Minimum Size ◽  
Acoustic Data ◽  
Input Parameters ◽  
Selection For

Abstract Burgos, J. M., and Horne, J. K. 2007. Sensitivity analysis and parameter selection for detecting aggregations in acoustic data. ICES Journal of Marine Science, 64: 160–168. A global sensitivity analysis was conducted on the algorithm implemented in the Echoview ® software to detect and describe aggregations in acoustic backscatter. Multiple aggregation detections were performed using walleye pollock (Theragra chalcogramma) data from the eastern Bering Sea. Walleye pollock form distinct aggregations and dense and diffuse layers. In each aggregation detection, input parameters defining minimum size, density, and distance to other aggregations were selected at random using a Latin hypercube sampling design. Sensitivity was quantified by testing for correlation among input parameters and a series of aggregation descriptors. In all, 336 correlation tests were performed, corresponding to a combination of seven detection input parameters, eight aggregation descriptors, and six transects. Among these, 181 tests were significant, indicating sensitivity between input parameters and aggregation descriptors. The aggregation-detection algorithm is sensitive to changes in threshold and minimum size, but less sensitive to changes in the connectivity criterion among aggregations.

scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

scholarly journals Interdependencies of Infrastructure Investment Decisions in Multi-Energy Systems—A Sensitivity Analysis for Urban Residential Areas

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 112-145
Author(s):  
Daniel Then ◽  
Johannes Bauer ◽  
Tanja Kneiske ◽  
Martin Braun
Keyword(s):  
Sensitivity Analysis ◽  
Energy Demand ◽  
Business Models ◽  
Linear Optimization ◽  
Investment Decisions ◽  
Energy System ◽  
Mixed Integer ◽  
The European Union ◽  
Residential Areas ◽  
Mixed Integer Linear Optimization

Considering the European Union (EU) climate targets, the heating sector should be decarbonized by 80 to 95% up to 2050. Thus, the macro-trends forecast increasing energy efficiency and focus on the use of renewable gas or the electrification of heat generation. This has implications for the business models of urban electricity and in particular natural gas distribution network operators (DNOs): When the energy demand decreases, a disproportionately long grid is operated, which can cause a rise of grid charges and thus the gas price. This creates a situation in which a self-reinforcing feedback loop starts, which increases the risk of gas grid defection. We present a mixed integer linear optimization model to analyze the interdependencies between the electricity and gas DNOs’ and the building owners’ investment decisions during the transformation path. The results of the investigation in a real grid area are used to validate the simulation setup of a sensitivity analysis of 27 types of building collectives and five grid topologies, which provides a systematic insight into the interrelated system. Therefore, it is possible to identify building and grid configurations that increase the risk of a complete gas grid shutdown and those that should be operated as a flexibility option in a future renewable energy system.

A Simulation Model for Multi-Factor Sensitivity Analysis on Energy Demand

2012 ◽  
Vol 621 ◽  
pp. 352-355
Author(s):  
Zhong Fu Tan ◽  
Shu Xiang Wang ◽  
Chen Zhang ◽  
Li Qiong Lin ◽  
Yin Hui Zhao
Keyword(s):  
Sensitivity Analysis ◽  
Energy Efficiency ◽  
Regression Model ◽  
Simulation Model ◽  
Influencing Factors ◽  
Energy Demand ◽  
Analysis Model ◽  
Demand Forecast ◽  
Population Urbanization

This paper analyses multi influencing factors of energy demand, using energy demand forecast regression model reveals inner relations between each factor and energy demand. Establish simulation model of the relation between GDP, energy intense and energy demand. Under the change in population, urbanization and energy efficiency, this paper gives analysis model of energy demand change.

Comparison of sparse-grid geometric and random sampling methods in nonlinear inverse solution uncertainty estimation

2012 ◽  
Vol 61 (1) ◽  
pp. 28-41 ◽  
Author(s):  
Michael J. Tompkins ◽  
Juan Luis Fernández Martínez ◽  
Zulima Fernández Muñiz
Keyword(s):  
Random Sampling ◽  
Sampling Methods ◽  
Uncertainty Estimation ◽  
Inverse Solution ◽  
Sparse Grid
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