Long-term forecasting for growth of electricity load based on customer sectors
The availability of electrical energy is an important issue. Along with the growth of the human population, electrical energy also increases. This study addresses problems in the operation of the electric power system. One of the problems that occur is the power imbalance due to scale growth between demand and generation. Alternative countermeasures that can be done are to prepare for the possibility that will occur in the future or what we are familiar with forecasting. Forecasting using the multiple linear regression method with this research variable assumes the household sector, business, industry, and public sectors, and is considered by the influence of population, gross regional domestic product, and District Minimum Wage. In forecasting, it is necessary to evaluate the accuracy using mean absolute percentage error (MAPE). MAPE evaluation results show a value of 0.142 % in the household sector, 0.085 % in the business sector, 1.983 % in the industrial sector, and 0.131 % in the total customer sector.
M. Subekti, I. A. Rahardjo, and D. Rosyanti, “Forecasting electrical energy demand of PT. PLN (Persero) UP3 Sukabumi using analytical, econometrics, and trends methods,” IOP Conference Series: Materials Science and Engineering, vol. 1098, no. 4, p. 042032, 2021.
K. B. Lindberg, P. Seljom, H. Madsen, D. Fischer, and M. Korpås, “Long-term electricity load forecasting: Current and future trends,” Utilities Policy, vol. 58, pp. 102–119, 2019.
A. Setiawan, “Losses sharing regulation as a solution for spike losses at PLN’s Distribution system caused by energy over production from independent power producers,” in 2020 International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP), Bandung, Indonesia, Sep., pp. 219–224, 2020.
G. Zhang and J. Guo, “A novel method for hourly electricity demand forecasting,” IEEE Transactions on Power Systems, vol. 35, no. 2, pp. 1351–1363, 2019.
S. Aslam, H. Herodotou, S. M. Mohsin, N. Javaid, N. Ashraf, and S. Aslam, “A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids,” Renewable and Sustainable Energy Reviews, vol. 144, p. 110992, 2021.
Z. A. Khan et al., “Efficient short-term electricity load forecasting for effective energy management,” Sustainable Energy Technologies and Assessments, vol. 53, p. 102337,, 2022.
L. Peng, L. Wang, D. Xia, and Q. Gao, “Effective energy consumption forecasting using empirical wavelet transform and long short-term memory,” energy, vol. 238, p. 121756,, 2022.
H. Zhou, Y. Zhang, L. Yang, Q. Liu, K. Yan, and Y. Du, “Short-term photovoltaic power forecasting based on long short term memory neural network and attention mechanism,” IEEE Access, vol. 7, pp. 78063–78074, 2019.
J. Fahmi, J. Windarta, and A. Y. Wardaya, “Studi awal penerapan distributed generation untuk optimalisasi PLTS atap on grid pada pelanggan PLN sistem jawa bali untuk memenuhi target EBT nasional,” Jurnal Energi Baru dan Terbarukan, vol. 2, no. 1, pp. 1–13, 2021.
L. Wen, K. Zhou, and S. Yang, “Load demand forecasting of residential buildings using a deep learning model,” Electric Power Systems Research, vol. 179, p. 106073, 2020.
Y. Yu, J. Cao, and J. Zhu, “An LSTM short-term solar irradiance forecasting under complicated weather conditions,” IEEE Access, vol. 7, pp. 145651–145666, 2019.
M. Pavlicko, M. Vojteková, and O. Blažeková, “Forecasting of electrical energy consumption in Slovakia,” Mathematics, vol. 10, no. 4, p. 577, 2022.
B. Nepal, M. Yamaha, A. Yokoe, and T. Yamaji, “Electricity load forecasting using clustering and ARIMA model for energy management in buildings,” Japan Architectural Review, vol. 3, no. 1, pp. 62–76, 2020.
N. J. Johannesen, M. Kolhe, and M. Goodwin, “Relative evaluation of regression tools for urban area electrical energy demand forecasting,” Journal of cleaner production, vol. 218, pp. 555–564, 2019.
H. Riahi-Madvar, M. Dehghani, R. Memarzadeh, and B. Gharabaghi, “Short to long-term forecasting of river flows by heuristic optimization algorithms hybridized with ANFIS,” Water Resources Management, vol. 35, no. 4, pp. 1149–1166, 2021.
A. Sherstinsky, “Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network,” Physica D: Nonlinear Phenomena, vol. 404, p. 132306, 2020.
P. Malhan and M. Mittal, “A novel ensemble model for long-term forecasting of wind and hydro power generation,” Energy Conversion and Management, vol. 251, p. 114983, 2022.
N. Talkhi, N. A. Fatemi, Z. Ataei, and M. J. Nooghabi, “Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods,” Biomedical Signal Processing and Control, vol. 66, p. 102494, 2021.
R. Sihite, “Analisis pengaruh pendapatan perkapita, jumlah konsumsi dan pertumbuhan ekonomi di Kabupaten/Kota Provinsi Kalimantan Tengah,” JEPP: Jurnal Ekonomi Pembangunan Dan Pariwisata, vol. 2, no. 1, pp. 46–57, 2022
P. Mangera, “Perkiraan kebutuhan energi listrik jangka panjang pada PT. PLN (Persero) wilayah Papua dan Papua Barat area Merauke dengan menggunakan metode regresi linier,” Mustek Anim Ha, vol. 7, no. 3, 2018.
J. Deng, Y. Deng, and K. H. Cheong, “Combining conflicting evidence based on Pearson correlation coefficient and weighted graph,” International Journal of Intelligent Systems, vol. 36, no. 12, pp. 7443–7460, 2021.
H. Zhu, X. You, and S. Liu, “Multiple ant colony optimization based on pearson correlation coefficient,” IEEE Access, vol. 7, pp. 61628–61638, 2019.
P. Schober, C. Boer, and L. A. Schwarte, “Correlation coefficients: appropriate use and interpretation,” Anesthesia & Analgesia, vol. 126, no. 5, pp. 1763–1768, 2018.
F. Zinzendoff Okwonu, B. Laro Asaju, and F. Irimisose Arunaye, “Breakdown analysis of Pearson correlation coefficient and robust correlation methods,” IOP Conference Series: Materials Science and Engineering, vol. 917, no. 1, 2020.
A. Ali et al., “A k-Nearest neighbours based ensemble via optimal model selection for regression,” IEEE Access, vol. 8, pp. 132095–132105, 2020.
M. Ausloos, A. Eskandary, P. Kaur, and G. Dhesi, “Evidence for Gross Domestic Product growth time delay dependence over Foreign Direct Investment. A time-lag dependent correlation study,” Physica A: Statistical Mechanics and Its Applications, vol. 527, p. 121181, 2019.
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Computer Science, vol. 7, pp. 1–24, 2021.
S. Nakagawa, P. C. D. Johnson, and H. Schielzeth, “The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded,” Journal of the Royal Society Interface, vol. 14, no. 134, 2017.
A. M. da Silva Filho, G. F. Zebende, A. P. N. de Castro, and E. F. Guedes, “Statistical test for multiple detrended cross-correlation coefficient,” Physica A: Statistical Mechanics and its Applications, vol. 562, p. 125285, 2021.
T. M. Hope, “Linear regression,” in Machine Learning, Elsevier, pp. 67–81, 2020.
M. Sharifzadeh, A. Sikinioti-Lock, and N. Shah, “Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression,” Renewable and Sustainable Energy Reviews, vol. 108, pp. 513–538, 2019.
H.-Y. Kim, “Statistical notes for clinical researchers: simple linear regression 3 – residual analysis,” Restorative Dentistry & Endodontics, vol. 44, no. 1, pp. 1–8, 2019.
T. A. Trunfio, A. Scala, A. D. Vecchia, A. Marra, and A. Borrelli, “Multiple regression model to predict length of hospital stay for patients undergoing femur fracture surgery at ‘San Giovanni Di Dio e Ruggi d’Aragona’ University Hospital,” in European Medical and Biological Engineering Conference, pp. 840–847, 2020.
M. Korkmaz, “A study over the general formula of regression sum of squares in multiple linear regression,” Numerical Methods for Partial Differential Equations, vol. 37, no. 1, pp. 406–421, 2021.
N. Kusuma, M. Roestam, and L. Pasca, “The analysis of forecasting demand method of linear exponential smoothing,” International Journal of Educational Administration, Management, and Leadership, pp. 7–18, 2020.
S. Prayudani, A. Hizriadi, Y. Y. Lase, and Y. Fatmi, “Analysis accuracy of forecasting measurement technique on random K-nearest neighbor (RKNN) using MAPE and MSE,” in Journal of Physics: Conference Series, vol. 1361, no. 1, p. 012089, 2019.
E. Vivas, H. Allende-Cid, and R. Salas, “A systematic review of statistical and machine learning methods for electrical power forecasting with reported mape score,” Entropy, vol. 22, no. 12, p. 1412, 2020.
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