Research On Prediction Of Electricity Consumption In Smart Parks Based On Multiple Linear Regression

2020 ◽  
Author(s):  
Zhiyang Zhao ◽  
Yue Peng ◽  
Xianxun Zhu ◽  
Xiong Wei ◽  
Xu Wang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Electricity Consumption

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.

Load Forecasting of Zhejiang Province Based on Combined Optimization Methods

2014 ◽  
Vol 998-999 ◽  
pp. 1046-1051
Author(s):  
Qian Xu ◽  
Zhou Lan ◽  
Jin Hua Huang ◽  
Hong Hao Qin ◽  
Xiao Min Xu
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Prediction Accuracy ◽  
Load Forecasting ◽  
Electricity Consumption ◽  
Optimization Methods ◽  
Exponential Smoothing ◽  
Zhejiang Province ◽  
Fitting Method

On the basis of analysis in Zhejiang, this paper uses the trend fitting method, the quadratic exponential smoothing model and multiple linear regression and grey GM (1, 1) portfolio model to forecast electricity consumption in 2012-2020 in Zhejiang, and compare the various methods of prediction accuracy.

Pengaruh Motivasi Kerja Terhadap Kinerja Pegawai Kantor Sekretariat Daerah Kabupaten Gunungkidul

2017 ◽  
Vol 23 (2) ◽  
pp. 121-137
Author(s):  
Ary Sutrischastini ◽  
Agus Riyanto
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Sampling Method ◽  
Work Motivation ◽  
Probability Sampling ◽  
Random Probability

This paper will discuss the effect of work motivation (incentives, motives and expectations) on the performance of the staff of the Regional Secretariat Gunungkidul. The purpose of this paper is: 1) Determine the effect of incentives on the performance of the staff of the Regional Secretariat Gunungkidul, 2) Determine the effect of motive on the performance of the staff of the Regional Secretariat Gunungkidul, 3) To know the effect of expectations on the performance of the staff of the Regional Secretariat Gunungkidul, 4)To know the effect of incentives, motives and expectations on the performance of the staff of the Regional Secretariat Gunungkidul.Research sites in the Regional Secretariat Gunungkidul and the population is 162entire employee in the Regional Secretariat Gunungkidul. Samples amounted to 116 respondents taken with simple random probability sampling method. Data were analyzed using multiple linear regression. Results obtained: (1) incentives positive and significant effect on the performance of, (2) motif positive and significant effect on the performance of, (3) expectations positive and significant impact on the performance of , and (4) incentives, motives and expectations of positive and significant impact on the performance of the staff of the Regional Secretariat Gunungkidul.

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