scholarly journals Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms

2018 ◽  
Vol 148 ◽  
pp. 337-346 ◽  
Author(s):  
P. Shine ◽  
T. Scully ◽  
J. Upton ◽  
M.D. Murphy
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Electricity Consumption ◽  
Dairy Farms ◽  
Regression Modelling ◽  
On Farm

scholarly journals Analisis Faktor-Faktor Yang Mempengaruhi Permintaan Listrik Kabupaten Manokwari (Studi Kasus Kelurahan Amban)

2020 ◽  
Vol 3 (2) ◽  
pp. 18-26
Author(s):  
Suryani Goga ◽  
Lillyani M. Orisu ◽  
Marcus R. Maspaitella
Keyword(s):  
Linear Regression ◽  
Literature Review ◽  
Multiple Linear Regression ◽  
Electrical Power ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Analytical Tool ◽  
Consumption Demand

Purpose of this study was to analyze the effect of the number of electronic furniture, the number of lamps, the number of family dependents, income and electrical power on the electricity demand by households in Amban Village, Manokwari Regency. The data obtained comes from the results of interviews and literature review that supports this research. The analytical tool used in this research is multiple linear regression. The results showed that the number of electrical furniture, the number of lamps and the number of dependents did not affect household electricity consumption, while income and electrical power did not affect household electricity consumption demand.

scholarly journals Using multiple linear regression to disaggregate electricity consumption for cluster-metered academic buildings

2021 ◽  
Author(s):  
Nicholas So
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Electricity Consumption ◽  
Average Error ◽  
University Of Toronto ◽  
Ideal Tool ◽  
Building Characteristics ◽  
Academic Buildings ◽  
The University ◽  
Selection Of

Ryerson University does not have a means to gauge electricity consumption for half of their campus buildings. The installation of utility meters is outside of the University’s budget, a situation that may be similar across other academic institutions. A multiple linear regression approach to estimating consumption for academic buildings is an ideal tool that balances performance and utility. Using 80 buildings from Ryerson University and the University of Toronto, significant building characteristics were identified (from a selection of 18 variables) that show a strong linear relationship with electricity consumption. Four equations were created to represent the diversity in size of academic buildings. Tested using cross-validation, the coefficient of variation of the RMSE for all models was 33%, with a range of error between 20% and 43%. The models were highly successful at modeling electricity consumption at Ryerson University with an average error of 14.8% for five building clusters. Using metered data from each cluster, raw estimates for individual buildings were adjusted to improve accuracy.

scholarly journals Using multiple linear regression to disaggregate electricity consumption for cluster-metered academic buildings

2021 ◽  
Author(s):  
Nicholas So
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Electricity Consumption ◽  
Average Error ◽  
University Of Toronto ◽  
Ideal Tool ◽  
Building Characteristics ◽  
Academic Buildings ◽  
The University ◽  
Selection Of

Ryerson University does not have a means to gauge electricity consumption for half of their campus buildings. The installation of utility meters is outside of the University’s budget, a situation that may be similar across other academic institutions. A multiple linear regression approach to estimating consumption for academic buildings is an ideal tool that balances performance and utility. Using 80 buildings from Ryerson University and the University of Toronto, significant building characteristics were identified (from a selection of 18 variables) that show a strong linear relationship with electricity consumption. Four equations were created to represent the diversity in size of academic buildings. Tested using cross-validation, the coefficient of variation of the RMSE for all models was 33%, with a range of error between 20% and 43%. The models were highly successful at modeling electricity consumption at Ryerson University with an average error of 14.8% for five building clusters. Using metered data from each cluster, raw estimates for individual buildings were adjusted to improve accuracy.

Sign in / Sign up

Export Citation Format

Share Document