Prognoza potrošnje električne energije korišćenjem ARIMA i LSTM modela
Sažetak
Precizno prognoziranje potrošnje električne energije ključno je za pouzdan i efikasan rad savremenih elektroenergetskih sistema. Ova studija prikazuje uporednu analizu dva istaknuta modela za prognozu — Autoregressive Integrated Moving Average (ARIMA) i Long Short-Term Memory (LSTM) — sa ciljem procene njihove efikasnosti u predviđanju potrošnje električne energije. Oba modela su razvijena i optimizovana radi pravičnog poređenja i najboljih mogućih performansi. Procena modela je obuhvatila tačnost predviđanja, računarsku efikasnost i upotrebu memorijskih resursa. Iako je ARIMA pokazala prednosti u brzini predviđanja i jednostavnosti modela, LSTM je dosledno davao preciznije prognoze i bolje prepoznavao složene vremenske obrasce. Rezultati ukazuju na značaj pažljivog izbora modela i strategije podešavanja za konkretne scenarije prognoziranja. Studija ističe LSTM kao pogodniji pristup za primene koje zahtevaju visoku tačnost i prilagodljivost, te pruža osnovu za buduća istraživanja naprednih ili hibridnih metoda.
Reference
N. Ahmad, Y. Ghadi, M. Adnan and M. Ali, "Load Forecasting Techniques for Power System: Research Challenges and Survey," IEEE Access, 2022.
S. Ozturk and F. Ozturk, "Forecasting Energy Consumption of Turkey by Arima Model," Journal of Asian Scientific Research, 2018.
A. Pierre, S. Akim, A. Semenyo and B. Babiga, "Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches," Energies, 2023.
S. Shariff, "Autoregressive Integrated Moving Average and Long Short-Term Memory Network Models for Forecasting Energy Consumptions," European Journal of Electrical Engineering and Computer Science, 2022.
D. Taslim and I. Murwantara, "Comparative analysis of ARIMA and LSTM for predicting fluctuating time series data," Bulletin of Electrical Engineering and Informatics, 2024.
L. Zec, J. Mikulović and M. Žarković, "Application of artificial neural network to power consumption forecasting for the Sarajevo region," Electrical Engineering, 2024.
K. A. Fakhryza, E. N. Budisusila and A. A. Nugroho, "Application of artificial neural network for peak load forecasting in 150 kV Semarang power system," Journal of Electrical Systems and Information Technology, vol. 11, no. 1, 2024.
J. S. Caicedo-Vivas and W. Alfonso-Morales, "Short-Term Load Forecasting Using an LSTM Neural Network for a Grid Operator," Energies, vol. 16, no. 23, 2023.
A. A. d. M. Meneses, "Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction," Energy Reports, vol. 10, 2023.
J. Zheng, X. Chen, K. Yu, L. Gan, Y. Wang and K. Wang, "Short-term Power Load Forecasting of Residential Community Based on GRU Neural Network," in 2018 International Conference on Power System Technology (POWERCON), 2018.
A. Agga, A. Abbou, M. Labbadi and Y. e. Houm, "Short-Term Load Forecasting: Based on Hybrid CNN-LSTM Neural Network," in 2021 6th International Conference on Power and Renewable Energy (ICPRE), 2021.
S. M. Hasanat, K. Ullah, H. Yousaf and K. Munir, "Enhancing Short-Term Load Forecasting with a CNN-GRU Hybrid Model: A Comparative Analysis," IEEE Access, 2024.
G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control. Holden-Day., San Francisco: Holden-Day, 1970.
C. B. Vennerod, A. Kjærran and E. S. Bugge, "Long Short-term Memory RNN," 2021.
S. Raschka and V. Mirjalili, Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, Packt, 2019.
R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, OTexts, 2021.
D. Dickey and W. Fuller, "Distribution of the estimators for autoregressive time series with a unit root," Journal of the American Statistical Association, 1979.
K. Andras, "Understanding Autocorrelation and Partial Autocorrelation Functions (ACF and PACF)," 2024. [Online]. Available: https://medium.com/@kis.andras.nandor/understanding-autocorrelation-and-partial-autocorrelation-functions-acf-and-pacf-2998e7e1bcb5. [Accessed 19 March 2025].
T. O'Malley, E. Bursztein, J. Long, F. Chollet, H. Jin, L. Invernizzi and A. Liu, "Keras Tuner," 2019. [Online]. Available: https://github.com/keras-team/keras-tuner.