Abstract:
A regional analysis of factors influencing electricity consumption is performed using panel dataset. It was identified that climate factors and an economic factor of hote...Show MoreMetadata
Abstract:
A regional analysis of factors influencing electricity consumption is performed using panel dataset. It was identified that climate factors and an economic factor of hotel occupancy could be used to characterise the regional monthly electricity consumption. This study selects Jakarta, Indonesia as the case study, covering the monthly time period from 2006-2012. Multiple linear regression approach is applied to analyse the association between the continues variables. The computation and visualisation is executed in R. One of the explanatory variables, namely hotel room occupancy, is interesting to be analysed in this case because the case study is the capital of the country. Many national events are being held centrally in Jakarta. In practice, limited studies have used this variable to model electricity consumption. The finding shows that the simplest model is conducted by excluding the variable day of rain and variable rainfall that slightly contribute to the model. The result identifies that the relationship between electricity consumption and hotel occupancy is statistically significant, as well as the relationship between electricity consumption and temperature.
Date of Conference: 03-04 December 2018
Date Added to IEEE Xplore: 09 April 2019
ISBN Information:
References is not available for this document.
Select All
1.
M. E. Wijaya and T. Tezuka, “A comparative study of households’ electricity consumption characteristics in Indonesia: A techno-socioeconomic analysis,” Energy Sustain. Dev., vol. 17, no. 6, pp. 596–604, 2013.
2.
“Indonesia Overview.” [Online]. Available: http://www.worldbank.org/en/country/indonesia/overview. [ Accessed: 09-Oct-2018 ].
3.
S. Mujiyanto and G. Tiess, “Secure energy supply in 2025: Indonesia’s need for an energy policy strategy,” Energy Policy, vol. 61, no. 5, pp. 31–41, 2013.
4.
Sekretariat Perusahaan PT. PLN, “Statistik PLN 2014,” no. 02701, p. iv, 2015.
5.
T. D. Harsono and M. Kuncoro, “Economic geography of electricity consumption : do regional characteristics matter in Indonesia, 1993-2010?,” Bus. Manag. Rev., vol. 5, no. 1, pp. 265–278, 2014.
6.
K. ESDM, “Program Strategis EBTKE dan Ketenagalistrikan,” J. Energi, vol. 02, p. 100, 2016.
7.
Demographia, “Demographia World Urban Areas Population Projections,” Demographia, no. January, p. 132, 2015.
8.
A. Kewo, R. Munir, and A. K. Lapu, “IntelligEnSia based electricity consumption prediction analytics using regression method,” in Proceedings - 5th International Conference on Electrical Engineering and Informatics: Bridging the Knowledge between Academic, Industry, and Community, ICEEI 2015, 2015.
9.
J. A. Pulido-Arcas, A. Pérez-Fargallo, and C. Rubio-Bellido, “Multivariable regression analysis to assess energy consumption and CO2emissions in the early stages of offices design in Chile,” Energy Build., vol. 133, pp. 738–753, 2016.
10.
I. Laicane, D. Blumberga, A. Blumberga, and M. Rosa, “Comparative Multiple Regression Analysis of Household Electricity use in Latvia: Using Smart Meter Data to Examine the Effect of Different Household Characteristics,” Energy Procedia, vol. 72, pp. 49–56, 2015.
11.
S. C. Yip, K. S. Wong, W. P. Hew, M. T. Gan, R. C. W. Phan, and S. W. Tan, “Detection of energy theft and defective smart meters in smart grids using linear regression,” Int. J. Electr. Power Energy Syst., vol. 91, pp. 230–240, 2017.
12.
C. Robinson et al., “Machine learning approaches for estimating commercial building energy consumption,” Appl. Energy, vol. 208, no. May, pp. 889–904, 2017.
13.
L. Wei, W. Tian, E. A. Silva, R. Choudhary, Q. Meng, and S. Yang, “Comparative Study on Machine Learning for Urban Building Energy Analysis,” Procedia Eng., vol. 121, pp. 285–292, 2015.
14.
E. Mocanu, P. H. Nguyen, M. Gibescu, and W. L. Kling, “Deep learning for estimating building energy consumption,” Sustain. Energy, Grids Networks, vol. 6, pp. 91–99, 2016.
15.
C. Zhang, L. Cao, and A. Romagnoli, “On the feature engineering of building energy data mining,” Sustain. Cities Soc., vol. 39, no. February, pp. 508–518, 2018.
16.
F. Wang et al., “Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns,” Energy Convers. Manag., vol. 171, no. June, pp. 839–854, 2018.
17.
H. Fan, I. F. MacGill, and A. B. Sproul, “Statistical analysis of drivers of residential peak electricity demand,” Energy Build., vol. 141, pp. 205–217, 2017.
18.
M. Shahbaz, M. K. Mahalik, S. H. Shah, and J. R. Sato, “Time-varying analysis of CO2emissions, energy consumption, and economic growth nexus: Statistical experience in next 11 countries,” Energy Policy, vol. 98, pp. 33–48, 2016.
19.
J. An, D. Yan, and T. Hong, “Clustering and statistical analyses of air-conditioning intensity and use patterns in residential buildings,” Energy Build., 2018.
20.
M. Azam, A. Q. Khan, K. Zaman, and M. Ahmad, “Factors determining energy consumption: Evidence from Indonesia, Malaysia and Thailand,” Renew. Sustain. Energy Rev., vol. 42, pp. 1123–1131, 2015.
21.
M. J. Crawley, “Regression,” Statistics (Ber)., vol. 1, pp. 125–154, 2005.
22.
D. A. Guerin, B. L. Yust, and J. G. Coopet, “Occupant Predictors of Household Energy Behavior and Consumption Change as Found in Energy Studies Since 1975,” vol. 29, no. 0035304, 2000.
23.
K. Genjo, S. I. Tanabe, S. I. Matsumoto, K. I. Hasegawa, and H. Yoshino, “Relationship between possession of electric appliances and electricity for lighting and others in Japanese households,” Energy Build., vol. 37, no. 3, pp. 259–272, 2005.
24.
K. Wangpattarapong, S. Maneewan, N. Ketjoy, and W. Rakwichian, “The impacts of climatic and economic factors on residential electricity consumption of Bangkok Metropolis,” Energy Build., vol. 40, no. 8, pp. 1419–1425, 2008.
25.
P. Manembu, “Architecture Design of Smart Meter Controlling System for Dynamic IP Environments,” 2018 Int. Conf. Intell. Auton. Syst., pp. 175–179, 2018.
26.
A. Kipping and E. Trømborg, “Hourly electricity consumption in Norwegian households - Assessing the impacts of different heating systems,” Energy, vol. 93, pp. 655–671, 2015.
27.
H. Batih and C. Sorapipatana, “Characteristics of urban households’ electrical energy consumption in Indonesia and its saving potentials,” Renew. Sustain. Energy Rev., vol. 57, pp. 1160–1173, 2016.
28.
B. E. Psiloglou, C. Giannakopoulos, S. Majithia, and M. Petrakis, “Factors affecting electricity demand in Athens, Greece and London, UK: A comparative assessment,” Energy, vol. 34, no. 11, pp. 1855–1863, 2009.
29.
K. Ito, “CO2 emissions, renewable and non-renewable energy consumption, and economic growth: Evidence from panel data for developing countries,” Int. Econ., vol. 151, no. xxxx, pp. 1–6, 2017.
30.
C. Shuai, L. Shen, L. Jiao, Y. Wu, and Y. Tan, “Identifying key impact factors on carbon emission: Evidences from panel and time-series data of 125 countries from 1990 to 2011,” Appl. Energy, vol. 187, pp. 310–325, 2017.
