PREDIKSI KONSUMSI LISTRIK BANGUNAN MENGGUNAKAN METODE STASTIK

Authors

  • Teuku Iqbal Faridiansyah Teknik Elektro Universitas Malikussaleh

DOI:

https://doi.org/10.29103/techsi.v8i1.116

Abstract

Persoalan untuk memperoleh prediksi yang akurat dari konsumsi listrik telah banyak dibahas oleh banyak karya-karya sebelumnya. Berbagai teknik telah digunakan seperti metode statistik, time-series, metode heuristik dan banyak lagi. Apapun teknik yang digunakan, akurasi prediksi tergantung pada ketersediaan data historis serta seleksi yang tepat dari data. Bahkan data yang lengkap, harus dipilih sehingga akurasi prediksi dapat ditingkatkan. Paper ini mempresentasikan metode prediksi konsumsi listrik dari data konsumsi listrik bangunan komersial yang dipilih. Dua metode prediksi yang berbeda digunakan (yaitu. Moving Average, MA, dan Exponential Smoothing, ES) untuk mengevaluasi akurasi prediksi dengan menggunakan kumpulan data yang direkomendasikan.  Hasil menunjukkan bahwa metode Moving Average (MA) lebih akurasi dibandingkan metode Exponential Smoothing (ES) dalam memprediksi konsumsi listrik bangunan dengan nilai error akurasi 0,2 dan 0,8.   

References

Tenaga, S. 2012. National Energy Balance 2012. Suruhanjaya Tenaga (Energy Commission): Putrajaya, Malaysia.

Ben-Nakhi AF, Mahmoud MA. Cooling load prediction for building using general regression neural networks. Energy Conversion and Management 2004;45(13-14):2127-41.

Wong SL, Wan KKW, Lam TNT. Artificial neural networks for energy analysis of office building with daylighting. Applied Energy 2010;87(2):551-7.

Aydinalp M, Urgusal VI, Fung AS. Modeling of the appliance, lighting, and space cooling energy consumption in the residential sector using neural networks. Applied Energy 2002;71(2):87-110.

Languang Z., Jing H., Jinxiang P., and Fengzhong Z., Electrical energy demand forecasting with GRNN for energy saving strategy, Applied Mechanics and Materials vols. 198-199 (2012), p. 639-643.

Victor M., Gareth A. Taylor, and Arthur E., A novel econometrics model for peak demand forecasting, IEEE, 2014.

Arindrajit Pal, Jyoti Prakash Singh, Paramartha Dutta, The Path Length Prediction of MANET Using Moving Average Model, Procedia Technology, Volume 10, 2013, Pages 882-889

Everette S. Gardner Jr., Exponential smoothing: The state of the art”Part II, International Journal of Forecasting, Volume 22, Issue 4, October-December 2006, Pages 637-666

Shalabh, A revisit to efficient forecasting in linear regression models, Journal of Multivariate Analysis, Volume 114, February 2013, Pages 161-170.

Downloads

Published

2019-03-21

Issue

Vol. 8 No. 1 (2016)

Section

Articles

License

Authors retain copyright and grant the journal right of first publication and this work is licensed under a Creative Commons Attribution-ShareAlike 4.0 that allows others to share the work with an acknowledgement of the works authorship and initial publication in this journal.

All articles in this journal may be disseminated by listing valid sources and the title of the article should not be omitted. The content of the article is liable to the author.

Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.

Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.

In the dissemination of articles by the author must declare the TECHSI Journal as the first party to publish the article.