Using a personal computer to collect and analyse energy audit data

1987 ◽  
Vol 10 (1) ◽  
pp. 11-18
Author(s):  
Darshan S. Teji ◽  
Ronald J. Balon
Keyword(s):  
Personal Computer ◽  
Energy Audit ◽  
Audit Data

scholarly journals BuildingSync: A schema for commercial building energy audit data exchange

2021 ◽  
pp. 100054
Author(s):  
Nicholas Long ◽  
Katherine Fleming ◽  
Chris CaraDonna ◽  
Cory Mosiman
Keyword(s):  
Data Exchange ◽  
Building Energy ◽  
Commercial Building ◽  
Energy Audit ◽  
Audit Data

scholarly journals Energy optimization of a wastewater treatment plant based on energy audit data: small investment with high return

2020 ◽  
Vol 27 (15) ◽  
pp. 17972-17985 ◽  
Author(s):  
Sina Borzooei ◽  
Youri Amerlinck ◽  
Deborah Panepinto ◽  
Soroush Abolfathi ◽  
Ingmar Nopens ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Energy Optimization ◽  
Treatment Plant ◽  
Energy Audit ◽  
Audit Data ◽  
High Return

scholarly journals Energy audit data for a resort island in the South China Sea

Data in Brief ◽  
2016 ◽  
Vol 6 ◽  
pp. 489-491 ◽  
Author(s):  
M. Reyasudin Basir Khan ◽  
Razali Jidin ◽  
Jagadeesh Pasupuleti
Keyword(s):  
South China Sea ◽  
South China ◽  
The South China Sea ◽  
Energy Audit ◽  
The South ◽  
China Sea ◽  
Audit Data

scholarly journals Multi-Kernel Learning based Sugar Industry Load Forecasting

2021 ◽  
Vol 9 (5) ◽  
pp. 275-278
Author(s):  
Yamanappa. N. Doddamani
Keyword(s):  
Support Vector Regression ◽  
Sugar Industry ◽  
Load Forecasting ◽  
Absolute Error ◽  
Power Delivery ◽  
Percentage Error ◽  
Support Vector ◽  
Energy Audit ◽  
Load Demand ◽  
Audit Data

Sugar industry which plans for power usage from Bagasse also needs the load forecasting carried out using the energy audit data. The stochastic nature of the load demand of the sugar industry needs to be forecasted in advance for the assuring uninterrupted power delivery to the industry. The manual energy audit data obtained from the sugar industry for a period of time is obtained and trained on a regression based on MultiKernel Learning (MKL). The Support Vector Regression (SVR) formulation is applied with the MultiKernel topology and the performance parameters including the Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) is observed in the implementation. The algorithm is the Multi Kernel Support Vector Regression algorithm using the Python based toolbox.

Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1202-1203
Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Personal Computer ◽  
Systematic Study ◽  
Present Author ◽  
Chemical Elements ◽  
Detection Limits ◽  
Research Groups ◽  
Quantitative Performance ◽  
Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

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