Automating Energy Demand Modeling and Forecasting Using Smart Meter Data

2019 ◽  
Author(s):  
Poojitha Amin ◽  
Ludmila Cherkasova ◽  
Rob Aitken ◽  
Vikas Kache
Keyword(s):  
Energy Demand ◽  
Smart Meter ◽  
Demand Modeling ◽  
Modeling And Forecasting

Tourism Demand Modeling and Forecasting for El Salvador

2017 ◽  
Vol 22 (2) ◽  
pp. 261-266 ◽  
Author(s):  
Sofia T. Melendez ◽  
Brijesh Thapa
Keyword(s):  
El Salvador ◽  
Tourism Demand ◽  
Demand Modeling ◽  
Modeling And Forecasting

scholarly journals Agricultural energy demand modeling in Iran: Approaching to a more sustainable situation

2018 ◽  
Vol 4 ◽  
pp. 260-265 ◽  
Author(s):  
Leila Farajian ◽  
Reza Moghaddasi ◽  
Safdar Hosseini
Keyword(s):  
Energy Demand ◽  
Demand Modeling

Modeling and Forecasting Energy Demand in TUCN Buildings

2019 ◽  
Author(s):  
M. Cretu ◽  
L. Czumbil ◽  
B. Bargauan ◽  
D. Stet. ◽  
A. Ceclan ◽  
...  
Keyword(s):  
Energy Demand ◽  
Modeling And Forecasting

Bagging in Tourism Demand Modeling and Forecasting

2017 ◽  
Vol 57 (1) ◽  
pp. 52-68 ◽  
Author(s):  
George Athanasopoulos ◽  
Haiyan Song ◽  
Jonathan A. Sun
Keyword(s):  
Variable Selection ◽  
Predictive Accuracy ◽  
Forecast Accuracy ◽  
Tourism Demand ◽  
Forecasting Accuracy ◽  
Demand Modeling ◽  
Predictive Regressions ◽  
Selection Processes ◽  
Modeling And Forecasting ◽  
Industry Applications

This study introduces bootstrap aggregation (bagging) in modeling and forecasting tourism demand. The aim is to improve the forecast accuracy of predictive regressions while considering fully automated variable selection processes which are particularly useful in industry applications. The procedures considered for variable selection is the general-to-specific (GETS) approach based on statistical inference and stepwise search procedures based on a measure of predictive accuracy (MPA). The evidence based on tourist arrivals from six source markets to Australia overwhelmingly suggests that bagging is effective for improving the forecasting accuracy of the models considered.

scholarly journals Use of group method of data handling for transport energy demand modeling

2017 ◽  
Vol 5 (5) ◽  
pp. 302-317 ◽  
Author(s):  
Geer Teng ◽  
Jin Xiao ◽  
Yue He ◽  
Tingting Zheng ◽  
Changzheng He
Keyword(s):  
Energy Demand ◽  
Group Method ◽  
Data Handling ◽  
Demand Modeling

Energy Demand Modeling with Noisy Input-Output Variables

1987 ◽  
Vol 8 (4) ◽  
Author(s):  
Lov Kumar Kher ◽  
Fereidoon P. Sioshansi ◽  
Soroosh Sorooshian
Keyword(s):  
Energy Demand ◽  
Input Output ◽  
Demand Modeling ◽  
Noisy Input
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