Forecasting Analysis of Electricity Consumption in East Kolaka and Konawe Districts Using Prophet Method
DOI:
https://doi.org/10.20956/j.v21i3.43563Keywords:
Forecasting, Prophet, Electricity, consumption, MAPE, MSEAbstract
This study aims to determine electricity consumption forecasting in East Kolaka Regency and Konawe Regency using the Prophet method. The data used in this study are secondary data obtained from PT PLN ULP Unaaha which consists of data on the amount of monthly electricity consumption from January 2019 to November 2023. The results showed that the Prophet method obtained an error calculation value using MAPE of 1.09%. So that the Prophet method can be used to forecast electricity consumption in East Kolaka Regency and Konawe Regency.
References
[1] Armi, A. E., Kridalaksana, A. H., & Arifin, Z., 2019. Peramalan Angka Inflasi Kota Samarinda Menggunakan Metode Double Exponential Smoothing (Studi Kasus : Badan Pusat Statistik Kota Samarinda). Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, Vol. 14(1), No. 21.
[2] Fauziah, Ningsih, Y. I., & Setiarini, E., 2019. Analisis Peramalan (Forecasting) Penjualan Jasa Pada Warnet Bulian City di Muara Bulian. Jurnal Ilmiah Ekonomi Dan Bisnis, Vol. 10(1), No. 61–67.
[3] Hamirsa, M. H., & Rumita, R., 2022. Usulan Perencanaan Peramalan (Forecating) dan Safety Stock Persediaan Spare Part Busi Champion Type RA7YC-2 (EV-01/EW-01/2) Menggunakan metode Time Series Pada PT Triangle Motorindo Semarang. Industrial Engineering Online Journal, Vol. 11(1), No. 1–10.
[4] Li, Y., 2024. Energy consumption forecasting with deep learning. Journal of Physics: Conference Series, 2711(1).
[5] Maleki, N., Lundström, O., Musaddiq, A., Olsson, T., Ahlgren, F., & Jeansson, J., 2024. Future energy insights: Time-series and deep learning models for city load forecasting. Applied Energy, 374(June), 124067.
[6] Menculini, L., Marini, A., Proietti, M., Garinei, A., Bozza, A., Moretti, C., & Marconi, M. (2021). Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices. Forecasting, Vol. 3(3), No. 644–662.
[7] Muzakki, M. A., Sabila, M. A., Sundari, S., & Wisnuadhi, B., 2021. Analisis Algoritma Prophet untuk Memprediksi Harga Pangan di Kota Bandung. Prosiding Industrial Research Workshop and National Seminar, Vol. 12, No. 659–664.
[8] Nabillah, I. & Ranggadara, I., 2020. Mean Absolute Percentage Error untuk Evaluasi Hasil Prediksi Komoditas Laut. JOINS (Journal of Information System), Vol. 5(2), No. 250–255.
[9] Peteleaza, D., Matei, A., Sorostinean, R., Gellert, A., Fiore, U., Zamfirescu, B. C., & Palmieri, F., 2024. Electricity consumption forecasting for sustainable smart cities using machine learning methods. Internet of Things (Netherlands), Vol. 27(June), 101322.
[10] Pontoh, R. S., Zahroh, S., Nurahman, H. R., Aprillion, R. I., Ramdani, A., & Akmal, D. I., 2021. Applied of feed-forward neural network and facebook Prophet model for train passengers forecasting. Journal of Physics: Conference Series, Vol. 1776(1).
[11] Prakoso, F. B., Darmawan, G., & Bachrudin, A., 2023. Penerapan Metode Facebook Prophet Untuk Meramalkan Jumlah Penumpang Trans Metro Bandung Koridor 1. ARMADA : Jurnal Penelitian Multidisiplin, Vol. 1(3), No. 133–147.
[12] Rahmawati, T., Sudarmana, L., & Priyanto, A., 2020. Penerapan Metode Arima Box-Jenkins Untuk Peramalan Konsumsi Listrik. Politeknosains, Vol. XIX(1), No. 6–11.
[13] Rezki, S. F., 2024. Metode Fuzzy Time Series Markov Chain dan Fuzzy Time Series Chen untuk Peramalan Konsumsi Listrik Studi Kasus : ULP Unaaha. Skripsi, Universitas Halu Oleo.
[14] Shen, J., Valagolam, D., & McCalla, S., 2020. Prophet forecasting model: A machine learning approach to predict the concentration of air pollutants (PM2.5, PM10, O3, NO2, SO2, CO) in Seoul, South Korea. PeerJ, Vol. 8(2).
[15] Taylor, S. J., & Letham, B., 2017. Business Time Series Forecasting at Scale. PeerJ Preprints, Vol. 35(8), No. 48–90.
[16] Toharudin, T., Pontoh, R. S., Caraka, R. E., Zahroh, S., Lee, Y., & Chen, R. C. (2023). Employing long short-term memory and Facebook prophet model in air temperature forecasting. Communications in Statistics: Simulation and Computation, Vol. 52(2), No. 279–290.
[17] Zamanzadeh Darban, Z., Webb, G. I., Pan, S., Aggarwal, C., & Salehi, M., 2024. Deep Learning for Time Series Anomaly Detection: A Survey. ACM Computing Surveys, 57(1).
[18] Zhao, N., Liu, Y., Vanos, J. K., & Cao, G., 2018. Day-of-week and seasonal patterns of PM2.5 concentrations over the United States: Time-series analyses using the Prophet procedure. Atmospheric Environment, Vol. 192(April), No. 116–127.
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