scholarly journals Impacts of building geometry modeling methods on the simulation results of urban building energy models

2018 ◽  
Vol 215 ◽  
pp. 717-735 ◽  
Author(s):  
Yixing Chen ◽  
Tianzhen Hong
Keyword(s):  
Building Energy ◽  
Geometry Modeling ◽  
Modeling Methods ◽  
Energy Models ◽  
Building Geometry ◽  
Simulation Results

scholarly journals Building Energy Model for Citypulse Smart City Traffic using Trees.M5P

2020 ◽  
Vol 8 (5) ◽  
pp. 752-754
Keyword(s):  
Network Lifetime ◽  
Physical Condition ◽  
Building Energy ◽  
Energy Model ◽  
Target Variable ◽  
Average Speed ◽  
Energy Models ◽  
Measured Time ◽  
Time Average ◽  
Simulation Results

The main objective of WSN is to increase network lifetime. Energy models are required to achieve this objective[1] . These models reduces no of sensors in any physical condition and achieve higher accuracy. In this paper, we present regression model to identify a relationship between a target variable and attributes in the dataset. We also present statistical[2] relations between target variable and observed variable. Simulation results show that trees M5P builds energy model and classifies the dataset with the Average measured time, Average speed and vehicle count to extend the network lifetime .

Characterization Of Building Foundation In Building Energy Models

2020 ◽  
Author(s):  
Germán Ramos Ruiz ◽  
Vicente Gutierrez González ◽  
Eva Lucas Segarra ◽  
Germán Campos Gordillo ◽  
Carlos Fernandez Bandera
Keyword(s):  
Building Energy ◽  
Energy Models ◽  
Building Foundation

scholarly journals Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1187
Author(s):  
Vicente Gutiérrez González ◽  
Germán Ramos Ruiz ◽  
Carlos Fernández Bandera
Keyword(s):  
Control Experiment ◽  
Pearson Correlation ◽  
Building Energy ◽  
Third Party ◽  
Primary Position ◽  
Energy Models ◽  
Urgent Task ◽  
Calibration Study ◽  
Study Case ◽  
Reduce Energy Consumption

The need to reduce energy consumption in buildings is an urgent task. Increasing the use of calibrated building energy models (BEM) could accelerate this need. The calibration process of these models is a highly under-determined problem that normally yields multiple solutions. Among the uncertainties of calibration, the weather file has a primary position. The objective of this paper is to provide a methodology for selecting the optimal weather file when an on-site weather station with local sensors is available and what is the alternative option when it is not and a mathematically evaluation has to be done with sensors from nearby stations (third-party providers). We provide a quality assessment of models based on the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)) and the Square Pearson Correlation Coefficient (R2). The research was developed on a control experiment conducted by Annex 58 and a previous calibration study. This is based on the results obtained with the study case based on the data provided by their N2 house.

scholarly journals Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3299
Author(s):  
Eva Lucas Segarra ◽  
Germán Ramos Ruiz ◽  
Carlos Fernández Bandera
Keyword(s):  
Optimization Technique ◽  
Load Forecasting ◽  
Weather Forecast ◽  
Building Energy ◽  
Load Forecast ◽  
Energy Saving Potential ◽  
Energy Models ◽  
Building Monitoring ◽  
Probabilistic Load ◽  
Grid Operators

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.

scholarly journals ANALYSIS OF THE CURRENT STATE OF RESEARCHES OF THE DEPOSITION OF ASPHALT-RESINOUS SUBSTANCES, PARAFFIN, AND MODELING METHODS. REVIEW PART II: WAX DEPOSITION

2021 ◽  
pp. 13-23
Author(s):  
M.R. Manafov ◽  
◽  
G.S. Aliyev ◽  
A.I. Rustamova ◽  
V.I. Kerimli ◽  
...  
Keyword(s):  
Experimental Data ◽  
Kinetic Model ◽  
Deposition Process ◽  
Real Field ◽  
Wax Deposition ◽  
Scaling Effect ◽  
Modeling Methods ◽  
Current State ◽  
Simulation Results ◽  
Reliable Interpretation

The mechanism of paraffin formation in transport pipes is briefly discussed. A kinetic model of the formation and wax deposition from oil is proposed. Comparison of the model with the available experimental data gave satisfactory results. The review considers software tools for modeling the wax deposition process. It is noted that the simulation results are not always applicable to real field cases. For a more reliable interpretation, the scaling effect must be taken into account. In the work various technologies for wax removal are considered

Research on the Integration of Energy Economic Model and Urban Energy Model

2021 ◽  
Vol 3 (6) ◽  
pp. 15-17
Author(s):  
Yaozhong Zu ◽  
Keyword(s):  
Greenhouse Gas ◽  
Building Energy ◽  
Urban Environments ◽  
Interdisciplinary Studies ◽  
Energy Models ◽  
Energy Needs ◽  
Urban Energy ◽  
Energy Economic Model ◽  
High Level ◽  
Building Level

In order to explore the strategy on urban energy and reduction of greenhouse gas, a large number of energy models have been developed by interdisciplinary studies. Mixed patterns are particularly useful as a result that they incorporate more dynamics to simulate the relevant high-level decisions and the provided actual results by building-level factors. However, space and spatial energy models are not often linked, which ignores the spatial impact of energy and emission policies in urban environments. The application of this method shows how it can be used to assess how different policies interact with other and affect building energy needs and greenhouse gas emissions.

Sign in / Sign up

Export Citation Format

Share Document