scholarly journals Dimension reduction of preference profile for aggregation of energy audit data

2021 ◽  
Vol 1730 (1) ◽  
pp. 012057
Author(s):  
S V Muravyov ◽  
M A Borisova
Keyword(s):  
Dimension Reduction ◽  
Preference Profile ◽  
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.

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