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 Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets

2020 ◽  
Vol 12 (17) ◽  
pp. 6788 ◽  
Author(s):  
Eva Lucas Segarra ◽  
Germán Ramos Ruiz ◽  
Vicente Gutiérrez González ◽  
Antonis Peppas ◽  
Carlos Fernández Bandera
Keyword(s):  
Energy Demand ◽  
Meteorological Data ◽  
Building Energy ◽  
Third Party ◽  
Weather Data ◽  
Data Sets ◽  
Weather Station ◽  
Energy Models ◽  
The Impact ◽  
Energy Strategies

The use of building energy models (BEMs) is becoming increasingly widespread for assessing the suitability of energy strategies in building environments. The accuracy of the results depends not only on the fit of the energy model used, but also on the required external files, and the weather file is one of the most important. One of the sources for obtaining meteorological data for a certain period of time is through an on-site weather station; however, this is not always available due to the high costs and maintenance. This paper shows a methodology to analyze the impact on the simulation results when using an on-site weather station and the weather data calculated by a third-party provider with the purpose of studying if the data provided by the third-party can be used instead of the measured weather data. The methodology consists of three comparison analyses: weather data, energy demand, and indoor temperature. It is applied to four actual test sites located in three different locations. The energy study is analyzed at six different temporal resolutions in order to quantify how the variation in the energy demand increases as the time resolution decreases. The results showed differences up to 38% between annual and hourly time resolutions. Thanks to a sensitivity analysis, the influence of each weather parameter on the energy demand is studied, and which sensors are worth installing in an on-site weather station are determined. In these test sites, the wind speed and outdoor temperature were the most influential weather parameters.

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 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 TCMacro: A Simple and Robust ImageJ-Based Method for Automated Measurement of Tail Coiling Activity in Zebrafish

Biomolecules ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1133
Author(s):  
Kevin Adi Kurnia ◽  
Fiorency Santoso ◽  
Bonifasius Putera Sampurna ◽  
Gilbert Audira ◽  
Jong-Chin Huang ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Dynamic Change ◽  
Current Method ◽  
Previous Method ◽  
Third Party ◽  
Wilcoxon Test ◽  
Average Intensity ◽  
Zebrafish Embryos ◽  
Dependent Manner ◽  
Pixel Intensity

Tail coiling is a reflection response in fish embryos that can be used as a model for neurotoxic analysis. The previous method to analyze fish tail coiling is largely based on third-party software. In this study, we aim to develop a simple and cost-effective method called TCMacro by using ImageJ macro to reduce the operational complexity. The basic principle of the current method is based on the dynamic change of pixel intensity in the region of interest (ROI). When the fish tail is moving, the average intensity is increasing. In time when the fish freeze, the peak of mean intensity is maintaining at a relatively low level. By using the optimized macro settings and excel VBA scripts, all the tail coiling measurement processes can be archived with few operation steps with high precision. Three major endpoints of tail coiling counts, tail coiling duration and tail coiling intervals can be obtained in batch. To validate this established method, we tested the potential neurotoxic activity of Tricaine (methanesulfonate, MS-222) and psychoactive compound of caffeine. Zebrafish embryos after Tricaine exposure displayed significantly less tail coiling activity in a dose-dependent manner, and were comparable to manual counting through the Wilcoxon test and Pearson correlation double validation. Zebrafish embryos after caffeine exposure displayed significantly high tail coiling activity. In conclusion, the TCMacro method presented in this study provides a simple and robust method that is able to measure the relative tail coiling activities in zebrafish embryos in a high-throughput manner.

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.

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