Penerapan Metode Stacking Untuk Meningkatkan Akurasi Hasil Peramalan Konsumsi Listrik
Abstract
Forecasting is one effort that is often carried out by every institution with various business fields. Electricity consumption is a problem that requires a solution in the form of a forecasting model. The electricity consumption data is in the form of time series data which when used for forecasting can be in the form of univariate data or multivariate data. Forecasting using univariate data will be carried out in this study by utilizing the stacking method with the hope of producing a low RMSE value so that the accuracy of the research results becomes better. Besides that, the stacking method will also be applied to three different data intervals to find out the type of interval that is able to provide the smallest error value. Based on the research results, it was found that the application of stacking was able to reduce the RMSE value by 0.7%. In addition, the fact is also generated that the shortest data interval is better able to provide the smallest RMSE with a difference of about 7% with a longer interval.
References
Ahmad, T., & Chen, H. (2020). A review on machine learning forecasting growth trends and their real-time applications in different energy systems. Sustainable Cities and Society, 54, 102010. https://doi.org/10.1016/j.scs.2019.102010
Altinçöp, H., & OKTAY, A. B. (2018). Air Pollution Forecasting with Random Forest Time Series Analysis. International Conference on Artificial Intelligence and Data Processing (IDAP), 8–12. https://doi.org/10.1109/IDAP.2018.8620768
Barić, I., Grbić, R., & Nyarko, E. K. (2019). Short-Term Forecasting of Electricity Consumption Using Artificial Neural Networks - an Overview. 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 1076–1081.
Cecaj, A., Lippi, M., Mamei, M., & Zambonelli, F. (2020). Comparing deep learning and statistical methods in forecasting crowd distribution from aggregated mobile phone data. Applied Sciences (Switzerland), 10(18). https://doi.org/10.3390/APP10186580
Chayama, M., & Hirata, Y. (2016). When univariate model-free time series prediction is better than multivariate. Physics Letters A, 1, 1–7. https://doi.org/10.1016/j.physleta.2016.05.027
Cheung, C. M., Kannan, R., & Prasanna, V. K. (2018). Temporal Ensemble Learning of Univariate Methods for Short Term Load Forecasting. 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).
Chuentawat, R., & Kan-ngan, Y. (2018). The Comparison of PM2 . 5 forecasting methods in the form of multivariate and univariate time series based on Support Vector Machine and Genetic Algorithm. 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 572–575.
Divina, F., Gilson, A., Vela, F. G., Torres, M. G., & Torres, J. F. (2018). Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting. Energies, 11(4), 1–31. https://doi.org/10.3390/en11040949
Doudkin, A., Marushko, Y., Owsinski, J., & Pawlowski, T. (2019). Spacecraft Telemetry Time Series Forecasting with Ensembles of Neural Networks. Proceedings of the 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2019, 2(3), 752–756. https://doi.org/10.1109/IDAACS.2019.8924252
Fleurke, S. (2017). Forecasting Automobile Sales using an Ensemble of Methods. 16, 329–337.
Han, S., Williamson, B. D., & Fong, Y. (2021). Improving random forest predictions in small datasets from two ‑ phase sampling designs. BMC Medical Informatics and Decision Making, 3, 1–9. https://doi.org/10.1186/s12911-021-01688-3
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting : Principles and Practice.
Izonin, I., & Tkachenko, R. (2021). Stacking-based GRNN-SGTM Ensemble Model for Prediction Tasks. 326–330.
Jiang, M., Liu, J., & Zhang, L. (2019). An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms. Physica A, 122272. https://doi.org/10.1016/j.physa.2019.122272
Kapustina, E., Shutov, E., Barskaya, A., & Kalganova, A. (2020). Predicting Electric Energy Consumption for a Jerky Enterprise. 396–406. https://doi.org/10.4236/epe.2020.126024
Kaytez, F., Taplamacioglu, M. C., Cam, E., & Hardalac, F. (2015). Electrical Power and Energy Systems Forecasting electricity consumption : A comparison of regression analysis , neural networks and least squares support vector machines. International Journal of Electrical Power and Energy Systems, 67, 431–438. https://doi.org/10.1016/j.ijepes.2014.12.036
Khwaja, A. S., Anpalagan, A., Naeem, M., & Venkatesh, B. (2020). Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting. Electric Power Systems Research, 179(October 2019), 106080. https://doi.org/10.1016/j.epsr.2019.106080
Kuo, P. (2018). A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting. 1–13. https://doi.org/10.3390/en11010213
Lee, S., KC, B., & Choeh, J. Y. (2020). Comparing performance of ensemble methods in predicting movie box office revenue. Heliyon, 6(6). https://doi.org/10.1016/j.heliyon.2020.e04260
Leite, F., Costa, K., Rodrigues, P. C., & López-gonzales, J. L. (2022). Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector. 1–12.
Liapis, C. M., Karanikola, A., & Kotsiantis, S. (2020). An ensemble forecasting method using univariate time series COVID-19 data. ACM International Conference Proceeding Series, 50–52. https://doi.org/10.1145/3437120.3437273
Luy, M., Ates, V., Barisci, N., Polat, H., & Cam, E. (2018). Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization. Applied Sciences, 8(6). https://doi.org/10.3390/app8060864
Ma, Z., Wang, P., Gao, Z., Wang, R., & Khalighi, K. (2018). Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose. PLOS ONE.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLoS ONE, 13(3), 1–26. https://doi.org/10.1371/journal.pone.0194889
Naim, I., & Mahara, T. (2018). Comparative Analysis of Univariate Forecasting Techniques for Industrial Natural Gas Consumption. May. https://doi.org/10.5815/ijigsp.2018.05.04
Petropoulos, F., & Spiliotis, E. (2021). The Wisdom of the Data : Getting the Most Out of Univariate Time Series Forecasting. Forecasting, 3, 478–497.
Petropoulos, F., Wang, X., & Disney, S. M. (2018). The inventory performance of forecasting methods : Evidence from the M3 competition data. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2018.01.004
Phan, T. (2018). Comparative Study on Univariate Forecasting Methods for Meteorological Time Series. 2380–2384.
Shi, H., Xu, M., & Li, R. (2018). Deep Learning for Household Load Forecasting — A Novel Pooling Deep RNN. IEEE Transactions on Smart Grid, 9(5), 5271–5280. https://doi.org/10.1109/TSG.2017.2686012
Sreehari, K., Adham, M., Cheriya, T. D., & Sheik, R. (2021). A Comparative Study between Univariate and Multivariate Time Series Models for COVID-19 Forecasting. International Conference on Computational Performance Evaluation (ComPE), 485–491.
Syamsiah, N. O. (2020). PERAMALAN HARGA TELUR AYAM RAS DI JAKARTA TIMUR. 5(1).
Varshini, A. G. P., Kumari, A. K., & Varadarajan, V. (2021). Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach. Electronics, 10, 1–21.
Zhai, B., & Chen, J. (2018). Science of the Total Environment Development of a stacked ensemble model for forecasting and analyzing daily average PM 2 . 5 concentrations in Beijing , China. Science of the Total Environment, 635, 644–658. https://doi.org/10.1016/j.scitotenv.2018.04.040
Zheng, J., Xu, C., Zhang, Z., & Li, X. (2017). Electric Load Forecasting in Smart Grids Using Long-Short-Term-Memory based Recurrent Neural Network. 1–6.
Zimmermann, A., & Howlett, R. J. (2021). Human Centred Intelligent Systems.
