Comparison of Machine Learning Methods for Predicting Electrical Energy Consumption

Abstract

This research investigates how to accurately predict electrical energy consumption to address growing global energy demands. The study employs three Machine Learning (ML) models: k-Nearest Neighbors (KNN), Random Forest (RF), and CatBoost. To enhance prediction accuracy, the researchers included a data pre-processing step using min-max normalization. The analysis utilized a dataset containing 52,416 records of power consumption from Tetouan City. The dataset was divided into training and testing sets using different ratios (90:10, 80:20, 50:50) to evaluate model performance. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to assess prediction accuracy. Min-max normalization significantly improved KNN's performance (reduced RMSE and MAPE). RF achieved similar accuracy with and without normalization. CatBoost also demonstrated stable performance regardless of normalization. Data pre-processing, specifically min-max normalization, is crucial for improving the accuracy of distance-based algorithms like KNN. Decision tree-based algorithms like RF and CatBoost are less sensitive to data normalization. These findings emphasize the importance of selecting appropriate pre-processing techniques to optimize energy consumption prediction models, which can contribute to better energy management strategies.

Keywords

CatBoost; energy consumption; machine learning; data normalization; Random Forest.

Full Text:

PDF

References

H. Hasanah and N. Nurmalitasari, “Aplikasi Sistem Cerdas Untuk Prediksi Energi Listrik Pemakaian Sendiri Di PT. Indonesia Power Sub Unit PLTA Kabupaten Wonogiri,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 8, no. 2, p. 489, 2017. https://doi.org/10.24176/simet.v8i2.1324

D. Puspita, “Energi Bersih Dan Terjangkau Dalam Mewujudkan Tujuan Pembangunan Berkelanjutan (SDGs),” J. Sos. dan sains, vol. 4, no. 3, pp. 271–280, 2024, doi: https://doi.org/10.59188/jurnalsosains.v4i3.1245.

T. Hong, P. Pinson, Y. Wang, R. Weron, D. Yang, and H. Zareipour, “Energy Forecasting: A Review and Outlook,” IEEE Open Access J. Power Energy, vol. 7, pp. 376–388, 2020. https://doi.org/10.1109/OAJPE.2020.3029979

A. Ibrahim, M. M. Muhammed, S. O. Sowole, R. Raheem, and O. Rabiat, “Performance of CatBoost classifier and other machine learning methods,” Data Sci., pp. 1–14, 2020, [Online]. Available: https://www.datasciencehub.net/system/files/ds-paper-644.pdf

V. Kumar, N. Kedam, K. V. Sharma, D. J. Mehta, and T. Caloiero, “Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models,” Water (Switzerland), vol. 15, no. 14, 2023. https://doi.org/10.3390/w15142572.

R. A. Asmara, Arief Prasetyo, Siska Stevani, and R. I. Hapsari, “Prediksi Banjir Lahar Dingin pada Lereng Merapi menggunakan Data Curah Hujan dari Satelit,” J. Inform. Polinema, vol. 7, no. 2, pp. 35–42, 2021. https://doi.org/10.33795/jip.v7i2.494

S. Widaningsih, “Penerapan Data Mining untuk Memprediksi Siswa Berprestasi dengan Menggunakan Algoritma K Nearest Neighbor,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 3, pp. 2598–2611, 2022. https://doi.org/10.35957/jatisi.v9i3.859

V. Ariyani, P. Putri, A. B. Prasetijo, and D. Eridani, “Perbandingan Kinerja Algoritme Naïve Bayes Dan K-Nearest Neighbor (Knn) Untuk Prediksi Harga Rumah,” J. Ilm. Tek. Elektro, vol. 24, no. 2, pp. 162–171, 2022. https://doi.org/10.14710/transmisi.24.4.162-171

P. Syahputra, “Prediksi Lama Rawat Pasien Covid-19 Berbasis Machine Learning,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 4, pp. 3374–3382, 2022. https://doi.org/10.35957/jatisi.v9i4.2883

M. Yunus and N. K. A. Pratiwi, “Prediksi Status Gizi Balita Dengan Algoritma K-Nearest Neighbor (KNN) di Puskemas Cakranegara,” JTIM J. Teknol. Inf. dan Multimed., vol. 4, no. 4, pp. 221–231, 2023. https://doi.org/10.35746/jtim.v4i4.328

F. Li and G. Jin, “Research on power energy load forecasting method based on KNN,” Int. J. Ambient Energy, vol. 43, no. 1, pp. 946–951, 2022. https://doi.org/10.1080/01430750.2019.1682041

D. Eko Waluyo et al., “Implementasi Algoritma Regresi pada Machine Learning untuk Prediksi Indeks Harga Saham Gabungan,” J. Inform. J. Pengemb. IT, vol. 9, no. 1, pp. 12–17, 2024.

O. H. Kombo, S. Kumaran, Y. H. Sheikh, A. Bovim, and K. Jayavel, “Long-term groundwater level prediction model based on hybrid KNN-RF technique,” Hydrology, vol. 7, no. 3, pp. 1–24, 2020. https://doi.org/10.3390/HYDROLOGY7030059

S. Huang, M. Huang, and Y. Lyu, “An Improved KNN‐Based Slope Stability Prediction Model,” Adv. Civ. Eng., vol. 2020, no. 1, p. 16, 2020. https://doi.org/10.1155/2020/8894109

V. K. Gupta, A. Gupta, D. Kumar, and A. Sardana, “Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model,” Big Data Min. Anal., vol. 4, no. 2, pp. 116–123, 2021. https://doi.org/10.26599/BDMA.2020.9020016

N. R. Prasad, N. R. Patel, and A. Danodia, “Crop yield prediction in cotton for regional level using random forest approach,” Spat. Inf. Res., vol. 29, no. 2, pp. 195–206, 2021. https://doi.org/10.1007/s41324-020-00346-6.

E. P. Febtiawan, L. A. Syamsul, I. Akbar, and A. S. Rachman, “Forecasting Produksi Energi Photovoltaic Menggunakan Algoritma Random Forest Classification,” J. Inf. Syst. Res., vol. 5, no. 4, pp. 1053–1062, 2024. https://doi.org/10.47065/josh.v5i4.5514

L. Breiman, “Random Forests,” Mach. Learn., vol. 12343 LNCS, pp. 503–515, 2001, doi: https://doi.org/10.1023/A:1010933404324

M. Gholizadeh, M. Jamei, I. Ahmadianfar, and R. Pourrajab, “Prediction of nanofluids viscosity using random forest (RF) approach,” Chemom. Intell. Lab. Syst., vol. 201, no. March, p. 104010, 2020. https://doi.org/10.1016/j.chemolab.2020.104010

A. V. Dorogush, V. Ershov, and A. Gulin, “CatBoost: gradient boosting with categorical features support,” arXiv, pp. 1–7, 2018. https://doi.org/10.48550/arXiv.1810.11363

F. Ahmed, M. Saleem, Z. Rajpoot, and A. Noor, “Intelligent Heart Disease Prediction Using CatBoost Empowered with XAI,” Int. J. Comput. Innov. Sci., vol. 4, no. December, pp. 8–13, 2023.

A. A. Ibrahim, R. L. Ridwan, M. M. Muhammed, R. O. Abdulaziz, and G. A. Saheed, “Comparison of the CatBoost Classifier with other Machine Learning Methods,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 11, pp. 738–748, 2020. https://doi.org/10.14569/IJACSA.2020.0111190.

D. T. Tran, J. Kanniainen, M. Gabbouj, and A. Iosifidis, “Bilinear Input Normalization for Neural Networks in Financial Forecasting,” 2021. https://doi.org/10.48550/arXiv.2109.00983

H. Gadde, “AI-Assisted Decision-Making in Database Normalization and Optimization Hemanth Gadde,” Int. J. Mach. Learn. Res. Cybersecurity Artif. Intell., vol. 11, no. 01, pp. 230–259, 2020.

A. Pranolo, F. Usha, and A. Khansa, “Enhanced Multivariate Time Series Analysis Using LSTM: A Comparative Study of Min-Max and Z-Score Normalization Techniques,” vol. 16, no. 2, pp. 210–220, 2024.

K. Karthick, R. Dharmaprakash, and S. Sathya, “Predictive Modeling of Energy Consumption in the Steel Industry Using CatBoost Regression: A Data-Driven Approach for Sustainable Energy Management,” Int. J. Robot. Control Syst., vol. 4, no. 1, pp. 33–49, 2024. https://doi.org/10.31763/ijrcs.v4i1.1234

D. U. Ozsahin, M. Taiwo Mustapha, A. S. Mubarak, Z. Said Ameen, and B. Uzun, “Impact of feature scaling on machine learning models for the diagnosis of diabetes,” in 2022 International Conference on Artificial Intelligence in Everything (AIE), pp. 87–94, 2022. https://doi.org/10.1109/AIE57029.2022.00024

http://dx.doi.org/10.28989/avitec.v7i1.2722

Article Metrics

Abstract view: 61 times
Download     : 24   times

This work is licensed under a Creative Commons Attribution 4.0 International License.

Refbacks

• There are currently no refbacks.