Open Access Open Access  Restricted Access Subscription or Fee Access

Levinson’s Recursion for Forecasting Electrical Energy Consumption in the Household Sector in Indonesia


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/iree.v18i5.22869

Abstract


Forecasting energy consumption is essential for planning electricity provision by both central and local governments, as well as electricity providers. This research sought to adapt Levinson recursion, previously utilized in speech signal prediction, for predicting electrical energy consumption in the household sector. By employing Levinson’s algorithm to derive autocorrelation from historical data, the study contrasted the method's efficacy across various orders of Levinson Recursion. The 22nd order emerged as the most optimal, with an approximate error rate of 3.67%. Although the method showed a pronounced sensitivity to fluctuations in historical data, making it suitable for short-term forecasts, the noticeable fluctuation in the energy consumption prediction graph warrants further research, especially given that the current method has mainly been applied to concise signal predictions.
Copyright © 2023 Praise Worthy Prize - All rights reserved.

Keywords


Forecasting; Levinson Recursion; Energy Consumption; Autocorrelation; Household Sector; Electrical Energy; Levinson Algorithm

Full Text:

PDF


References


Bou-Rabee, M., Bilal, M., Bashir, M., Ali, A., Forecasting the Solar Panels Power Output Based on Air Pollution and Weather in the Gulf Countries by Using Machine Learning, (2022) International Review of Electrical Engineering (IREE), 17 (6), pp. 570-577.
https://doi.org/10.15866/iree.v17i6.22714

Karlov, D., Prokazov, I., Bakshtanin, A., Matveeva, T., Kondratenko, L., Optimizing Neural Network Model Performance for Wind Energy Forecasting, (2021) International Review on Modelling and Simulations (IREMOS), 14 (3), pp. 185-193.
https://doi.org/10.15866/iremos.v14i3.19890

Chandrakala, K., Rajan, P., A Hybrid Machine Learning Approach for Price Forecasting in Electricity Market with Smart Bidding Strategies and Wind Energy Influence, (2022) International Review on Modelling and Simulations (IREMOS), 15 (6), pp. 414-424.
https://doi.org/10.15866/iremos.v15i6.22653

Simolin, T., Järventausta, P., Rautiainen, A., Assessment of Prediction Uncertainties in EV Charging Management, (2020) International Review of Electrical Engineering (IREE), 15 (4), pp. 262-271.
https://doi.org/10.15866/iree.v15i4.18679

Pardo García, C., Pabon, J., Fonseca Vigoya, M., Development of a Multiphysics Model for the Prediction of the Electrical Characteristics of Thermoelectric Modules, (2022) International Journal on Energy Conversion (IRECON), 10 (6), pp. 188-197.
https://doi.org/10.15866/irecon.v10i6.22077

D.A. Adebayo, Long Term Electric Load Forecasting for Bonny and Finima Towns using Linear Regression Method, International Journal of Engineering Research and, Vol. V8, No. 11, 2019.
https://doi.org/10.17577/IJERTV8IS110248

O. Ahmia, and N. Farah, Multi-model approach for electrical load forecasting, 2015 SAI Intelligent Systems Conference (IntelliSys), 2015, pp. 87-92.
https://doi.org/10.1109/IntelliSys.2015.7361089

S.R. Khan, and S. Noor, Short Term Load Forecasting using SVM based PUK kernel, 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2020, pp. 1-9.
https://doi.org/10.1109/iCoMET48670.2020.9074068

S. Salahat, Short-Term Forecasting of Electricity Consumption in Palestine Using Artificial Neural Networks, International Journal of Artificial Intelligence & Applications, Vol. 8, No. 2, 2017, pp. 11-21.
https://doi.org/10.5121/ijaia.2017.8202

C. Kuster., Y. Rezgui, and M. Mourshed, Electrical load forecasting models: A critical systematic review, Sustainable Cities and Society, Vol. 35, 2017, pp. 257-270.
https://doi.org/10.1016/j.scs.2017.08.009

L.C.P. Velasco, D. Lou, G. Paolo, M. Bryan, Load Forecasting using Autoregressive Integrated Moving Average and Artificial Neural Network, International Journal of Advanced Computer Science and Applications, Vol. 9, No. 7, 2018.
https://doi.org/10.14569/IJACSA.2018.090704

Lemuel Clark P. Velasco, Daisy Lou L. Polestico, Gary PaoloO. Macasieb, Michael Bryan V. Reyes, and Felicisimo B. Vasquez Jr., Load Forecasting using Autoregressive Integrated Moving Average and Artificial Neural Network, International Journal of Advanced Computer Science and Applications, Vol. 9, No. 7, 2018, pp. 23-29.
https://doi.org/10.14569/IJACSA.2018.090704

Lemuel Clark P. Velasco, Daisy Lou L. Polestico, Dominique Michelle M. Abella, Genesis T. Alegata, Gabrielle C. Luna Day-Ahead Load Forecasting using Support Vector Regression Machines, International Journal of Advanced Computer Science and Applications, vol. 9, no. 3, 2018.
https://doi.org/10.14569/IJACSA.2018.090305

Li, Y., Gu, X., Application of Online SVR in Very Short-Term Load Forecasting, (2013) International Review of Electrical Engineering (IREE), 8 (1), pp. 277-283.

S.E. Sim, K.G. Tay, A. Huong, and W.K. Tiong, Forecasting Electricity Consumption Using SARIMA Method in IBM SPSS Software, Universal Journal of Electrical and Electronic Engineering, Vol. 6, No. 5B, 2019, pp. 103-114.
https://doi.org/10.13189/ujeee.2019.061614

Ö. Semih, S. Yusuf, M. Ahmet, and Ö. Veysel, Parameters estimation of Linear, Quadratic and Models for Energy Demand Forecasting by Using ABC Algorithm, Proceedings of 113th IASTEM International Conference, pp. 13-16.

Souhe, F. G. Y., Mbey, C. F., Boum, A. T., & Ele, P. (2021). Forecasting of Electrical Energy Consumption of Households in a Smart Grid. International Journal of Energy Economics and Policy, 11(6), 221-233.
https://doi.org/10.32479/ijeep.11761

D. Solyali, A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus, Sustainability, Vol. 12, No. 9, 2020, pp. 3612.
https://doi.org/10.3390/su12093612

Raharjo, J., Budiharjo, S., Short-Term Electric Load Forecasting Using Auto Regressive-Candidates Area Shifting Technique, (2021) International Journal on Engineering Applications (IREA), 9 (5), pp. 286-294.
https://doi.org/10.15866/irea.v9i5.20668

Indra Ardhanayudha Aditya, Arionmaro Asi Simaremare, Jangkung Rahatjo, Suyanto, Inung Wijayanto, Daily Power Plant Load Prediction using Grammatical Evolution, 2022 International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT) | 978-1-6654-9352-9/22/$31.00 ©2022 IEEE.
https://doi.org/10.1109/ICEECIT55908.2022.10030558

Y. Liu, M.Y. Zhai, L.P. Lu, and Y.X. Liu, An Improved Method for Short-Term Power Load Forecasting Based on Fractal Extrapolation, Applied Mechanics and Materials, Vol. 229-231, 2012, pp. 1077-1080.
https://doi.org/10.4028/www.scientific.net/AMM.229-231.1077

M.-Y. Zhai, A new method for short-term load forecasting based on fractal interpretation and wavelet analysis, International Journal of Electrical Power & Energy Systems, Vol. 69, 2015, pp. 241-245.
https://doi.org/10.1016/j.ijepes.2014.12.087

H. Boubaker, S. ben Amor, and H. Rezgui, A New Hybrid Wavelet-Neural Network Approach for Forecasting Electricity, Energy Studies Review, Vol. 24, No. 1, 2020.
https://doi.org/10.15173/esr.v24i1.4135

Y. Zhang, L. Guo, Q. Li, and J. Li, Electricity consumption forecasting method based on MPSO-BP neural network model, Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016), Paris, France: Atlantis Press, 2016.
https://doi.org/10.2991/iceeecs-16.2016.133

Farahat, M., Elgawed, A., Mustafa, H., Ibrahim, A., Short Term Load Forecasting Using BP Neural Network Optimized by Particle Swarm Optimization, (2013) International Review on Modelling and Simulations (IREMOS), 6 (2), pp. 450-454.

Pan Duan, Kaigui Xie, Tingting Guo, and Xiaogang Huang, Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques, Energies, 4, 2011, pp. 173-184.
https://doi.org/10.3390/en4010173

H. H. Monson, Statistical Digital Signal Processing and Modeling, John Wiley & Sons, Inc, 1996.

J.O. Sophocles, Optimum Signal Processing, An Introduction, 2nd edition, 1988, McGraw-Hill International Editions, Electrical Engineering Series

Ministry of Energy and Mineral Resources of Indonesia, Directorate General of Electricity and Energy Utilization, 2020.


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize