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The Use of Regression Method on Simple E for Estimating Electrical Energy Consumption

Arnawan Hasibuan, Widyana Verawaty Siregar, Muzamir Isa, Eddy Warman, Roby Finata, M. Mursalin

Abstract


The continuous increase in population growth has an impact on the electrical energy supply. Based on this increase, electric power producers serve customers using proper forecasts. Therefore, it is a necessity to select the right calculation method with easy implementation. In this study, the population forecasts and economic growth calculations using the GT (Growth Trend) regression method development on Simple E were obtained for the year 2028. Furthermore, electricity consumption estimation was carried out using the DL (Double Log) regression method with growth trend, R, AR, DW, and t values of 6.63%, 0.993, 0.992, 1.21, and 2.18, respectively. The results show that estimated energy consumption was 6.63% annually, with the achievable amount for 2028 being 19,839.83 GWh.

 

Doi: 10.28991/HIJ-SP2022-03-06

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Keywords


Forecast of Electricity Demand; Energy; Population; Economy and Regression.

References


Yuan, J. H., Kang, J. G., Zhao, C. H., & Hu, Z. G. (2008). Energy consumption and economic growth: Evidence from China at both aggregated and disaggregated levels. Energy Economics, 30(6), 3077–3094. doi:10.1016/j.eneco.2008.03.007.

Koirala, B. P., Koliou, E., Friege, J., Hakvoort, R. A., & Herder, P. M. (2016). Energetic communities for community energy: A review of key issues and trends shaping integrated community energy systems. Renewable and Sustainable Energy Reviews, 56, 722–744. doi:10.1016/j.rser.2015.11.080.

Xie, F., Liu, Y., Guan, F., & Wang, N. (2020). How to coordinate the relationship between renewable energy consumption and green economic development: from the perspective of technological advancement. Environmental Sciences Europe, 32(1), 1–15. doi:10.1186/s12302-020-00350-5.

Chenghu, Z., Arif, M., Shehzad, K., Ahmad, M., & Oláh, J. (2021). Modeling the dynamic linkage between tourism development, technological innovation, urbanization and environmental quality: Provincial data analysis of China. International Journal of Environmental Research and Public Health, 18(16), 8456. doi:10.3390/ijerph18168456.

PéRez-Arriaga, I. J., Jenkins, J. D., & Batlle, C. (2017). A regulatory framework for an evolving electricity sector: Highlights of the MIT utility of the future study. Economics of Energy and Environmental Policy, 6(1), 71–92. doi:10.5547/2160-5890.6.1.iper.

Taneja, J. (2019). If You Build It, Will They Consume? Key Challenges for Universal, Reliable, and Low-Cost Electricity Delivery in Kenya. SSRN Electronic Journal, 491. doi:10.2139/ssrn.3310479.

Ouedraogo, N. S. (2017). Modeling sustainable long-term electricity supply-demand in Africa. Applied Energy, 190, 1047–1067. doi:10.1016/j.apenergy.2016.12.162.

Raza, M. Y., & Lin, B. (2022). Analysis of Pakistan's electricity generation and CO2 emissions: Based on decomposition and decoupling approach. Journal of Cleaner Production, 359, 132074.doi:10.1016/j.jclepro.2022.132074.

Njeru, G., Gathiaka, J., & Kimuyu, P. (2020). Modelling and Forecasting Electricity Demand for Commercial and Industrial Consumers in Kenya to 2035. In European Scientific Journal ESJ, 16(13). doi:10.19044/esj.2020.v16n13p162.

Anand, A., & Suganthi, L. (2020). Forecasting of Electricity Demand by Hybrid ANN-PSO Models. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications, 865-882. IGI Global, Pennsylvania, United States. doi:10.4018/978-1-7998-0414-7.ch048.

Angelopoulos, D., Siskos, Y., & Psarras, J. (2019). Disaggregating time series on multiple criteria for robust forecasting: The case of long-term electricity demand in Greece. European Journal of Operational Research, 275(1), 252–265. doi:10.1016/j.ejor.2018.11.003.

Williams, S., & Short, M. (2020). Electricity demand forecasting for decentralised energy management. Energy and Built Environment, 1(2), 178–186. doi:10.1016/j.enbenv.2020.01.001.

Jesmeen, M. Z. H., Hossen, J., & Aziz, A. B. A. (2021). Unsupervised Anomaly Detection for Energy Consumption in Time Series using Clustering Approach. Emerging Science Journal, 5(6), 840-854. doi:10.28991/esj-2021-01314.

Zhang, X. M., Grolinger, K., Capretz, M. A. M., & Seewald, L. (2018). Forecasting Residential Energy Consumption: Single Household Perspective. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). doi:10.1109/icmla.2018.00024.

Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. A. (2018). Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies, 11(7), 1636. doi:10.3390/en11071636.

Tetlock, P. E., & Gardner, D. (2016). Superforecasting: The art and science of prediction. Random House, New York City, United States.

Garland, T., & Ives, A. R. (2000). Using the past to predict the present: Confidence intervals for regression equations in phylogenetic comparative methods. American Naturalist, 155(3), 346–364. doi:10.1086/303327.

Briscoe, N. J., Elith, J., Salguero‐Gómez, R., Lahoz‐Monfort, J. J., Camac, J. S., Giljohann, K. M., … Guillera‐Arroita, G. (2019). Forecasting species range dynamics with process‐explicit models: matching methods to applications. Ecology Letters, 22(11), 1940–1956. doi:10.1111/ele.13348.

Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben Taieb, S., … Boylan, J. E. (2022). Forecasting: theory and practice. International Journal of Forecasting, 38(3), 705–871. doi:10.1016/j.ijforecast.2021.11.001.

Jenset, G. B., & McGillivray, B. (2017). Quantitative historical linguistics: A corpus framework. Oxford University Press, Oxford, United Kingdom.

Casarrubea, M., Aiello, S., Di Giovanni, G., Santangelo, A., Palacino, M., & Crescimanno, G. (2019). Combining Quantitative and Qualitative Data in the Study of Feeding Behavior in Male Wistar Rats. Frontiers in Psychology, 10. doi:10.3389/fpsyg.2019.00881.

Feng, L., Wang, Q., Wang, J., & Lin, K.-Y. (2022). A Review of Technological Forecasting from the Perspective of Complex Systems. Entropy, 24(6), 787. doi:10.3390/e24060787.

Norouzian, R., Plonsky, L. (2018). Correlation and Simple Linear Regression in Applied Linguistics. The Palgrave Handbook of Applied Linguistics Research Methodology. Palgrave Macmillan, London, United Kingdom. doi:10.1057/978-1-137-59900-1_19.

Rahimi, R. (2017). Organizational Culture and Customer Relationship Management: A Simple Linear Regression Analysis. Journal of Hospitality Marketing and Management, 26(4), 443–449. doi:10.1080/19368623.2017.1254579.

Basha, L., & Hoxha, F. (2019). Kernel Estimation of the Baseline Function in the Cox Model. European Scientific Journal, 15(6), 105–118. doi:10.19044/esj.2019.v15n6p105.

Cénac, P., Godichon-Baggioni, A., & Portier, B. (2020). An efficient Averaged Stochastic Gauss-Newton algorithm for estimating parameters of nonlinear regressions models. arXiv preprint arXiv:2006.12920. doi:10.48550/arXiv.2006.12920.

Brown, S. H. (2009). Multiple linear regression analysis: a matrix approach with MATLAB. Alabama Journal of Mathematics, 34, 1-3.

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis (6th Ed.). John Wiley & Sons, Hoboken, United States.

Kumari, K., & Yadav, S. (2018). Linear regression analysis study. Journal of the Practice of Cardiovascular Sciences, 4(1), 33. doi:10.4103/jpcs.jpcs_8_18.

Wagner, W., E. (2019). Using IBM® SPSS® statistics for research methods and social science statistics. Sage Publications, Newbury, United States.

Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (2008). Forecasting methods and applications. John Wiley & Sons, Hoboken, United States.

Deluca, D., Gasson, S., & Kock, N. (2006). Adaptations that Virtual Teams Make so that Complex Tasks Can Be Performed Using Simple E-Collaboration Technologies. International Journal of E-Collaboration (IJeC), 2(3), 65–91. doi:10.4018/jec.2006070104.

Yamaguchi, K. (2000). Operation Manual of Simple E. JICA Study Team, the Institute of Energy Economics, Tokyo, Japan. Available online: http://open_jicareport.jica.go.jp/pdf/11617180_04.pdf (accessed on May 2022).

Kriswanto, S. (2014). Electricity Demand Forecast Using the Simple E Application. Jakarta, Indonesia. (In Indonesian).

Senior, V. L., Evans, L. C., Leather, S. R., Oliver, T. H., & Evans, K. L. (2020). Phenological responses in a sycamore–aphid–parasitoid system and consequences for aphid population dynamics: a 20 year case study. Global change biology, 26(5), 2814-2828. doi:10.1111/gcb.15015.

Smith, J., & Wallis, K. F. (2009). A simple explanation of the forecast combination puzzle. Oxford Bulletin of Economics and Statistics, 71(3), 331–355. doi:10.1111/j.1468-0084.2008.00541.x.

Pamuji, N. (2014). Business Plan for the Supply of Electricity (RUPTL 2014-2015) PT PLN (Persero), Jakarta, Indonesia. Available online: https://gatrik.esdm.go.id/assets/uploads/download_index/files/9e670-coffee-morning-ruptl-2015-2024.pdf (accessed on May 2022).


Full Text: PDF

DOI: 10.28991/HIJ-SP2022-03-06

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