Konutların Günlük Elektrik Güç Tüketimi Tahmini İçin Uygun Model Seçimi

Yıl 2018, Cilt: 30 Sayı: 3, 15 - 21, 20.09.2018

Aykut Çayır İşıl Yenidoğan Hasan Dağ

Öz

Zamana bağlı değişim gösteren olayların modellenmesi zorlu
bir veri analizi problemidir. Bu olaylardan biri olan elektrik güç tüketiminde
ise veriden mevsimsel etki ve tatil günleri gibi örüntülerin öğrenilerek bir
tüketim tahmin modelinin geliştirilebilmesi için klasik makine öğrenmesi ve
derin öğrenme yöntemlerinden yararlanılmaktadır. Bu çalışmada, İngiltere’nin
Londra şehrindeki belirli bir bölgede 30 farklı eve ait yaklaşık 3 yıllık elektrik
güç tüketimi veri kümesi kullanılarak uygun bir kısa vadeli tüketim tahmin
modelinin makine öğrenmesi algoritmaları ile bulunması amaçlanmıştır.

Anahtar Kelimeler

Zaman serisi, makine öğrenmesi, derin öğrenme, konut bazlı elektrik enerjisi tüketim tahmini

Kaynakça

• 1. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning", Nature 521.7553 (2015): 436-444. 2. Suykens, Johan AK, and Joos Vandewalle. "Least squares support vector machine classifiers", Neural processing letters, 9.3 (1999): 293-300. 3. Dietterich, Thomas G. "Ensemble learning", The handbook of brain theory and neural networks, 2 (2002): 110-125. 4. Cortes, Corinna, and Vladimir Vapnik. "Support-vector networks", Machine Learning, 20.3 (1995): 273-297. 5.CACI ACORN Group (2010) [Online]. Available: https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households 6. Greff, Klaus, et al. "LSTM: A search space odyssey", IEEE transactions on neural networks and learning systems, (2017). 7. Yu, Wei, et al. "Towards statistical modelling and machine learning based energy usage forecasting in smart grid", ACM SIGAPP Applied Computing Review, 15.1 (2015): 6-16. 8. Kumar, Manish, and M. Thenmozhi. "Forecasting stock index movement: A comparison of support vector machines and random forest", (2006). 9. Cheng, Ying-Ying, Patrick PK Chan, and Zhi-Wei Qiu. "Random forest based ensemble system for short term load forecasting", Machine Learning and Cybernetics (ICMLC), 2012 International Conference on. Vol. 1. IEEE, 2012. 10. Fugon, Lionel, Jérémie Juban, and Georges Kariniotakis. "Data mining for wind power forecasting", European Wind Energy Conference & Exhibition EWEC 2008, EWEC, 2008. 11. Gers, Felix A., Douglas Eck, and Jürgen Schmidhuber. "Applying LSTM to time series predictable through time-window approaches", Neural Nets WIRN Vietri-01, Springer London, 2002. 193-200. 12. Malhotra, Pankaj, et al. "Long short term memory networks for anomaly detection in time series", Proceedings, Presses universitaires de Louvain, 2015. 13.Filonov, Pavel, Andrey Lavrentyev, and Artem Vorontsov. "Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model", arXiv preprint arXiv:1612.06676, (2016). 14. Gamboa, John Cristian Borges. "Deep Learning for Time-Series Analysis.", arXiv preprint arXiv:1701.01887, (2017). 15. Zhang, Shengdong, et al. "Deep Symbolic Representation Learning for Heterogeneous Time-series Classification", arXiv preprint arXiv:1612.01254, (2016). 16.Oğcu, Gamze, Omer F. Demirel, and Selim Zaim. "Forecasting electricity consumption with neural networks and support vector regression." Procedia-Social and Behavioral Sciences 58 (2012): 1576-1585. 17. Kavaklioglu, Kadir. "Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression." Applied Energy 88.1 (2011): 368-375. 18.Tso, Geoffrey KF, and Kelvin KW Yau. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks." Energy 32.9 (2007): 1761-1768. 19. Beccali, M., et al. "Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area." Renewable and Sustainable Energy Reviews 12.8 (2008): 2040-2065. 20. Suganthi, L., and Anand A. Samuel. "Energy models for demand forecasting—A review." Renewable and sustainable energy reviews 16.2 (2012): 1223-1240. 21. Swan, Lukas G., and V. Ismet Ugursal. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques." Renewable and sustainable energy reviews 13.8 (2009): 1819-1835. 22.Breiman, Leo. "Random forests", Machine Learning”, 45.1 (2001): 5-32. 23.Loh, Wei‐Yin. "Classification and regression trees", Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1.1 (2011): 14-23. 24. Gers, Felix A., Jürgen Schmidhuber, and Fred Cummins. "Learning to forget: continual prediction with LSTM", (1999): 850-855. 25. Graves, Alex, and Jürgen Schmidhuber. "Framewise phoneme classification with bidirectional LSTM and other neural network architectures", Neural Networks, 18.5 (2005): 602-610. 26.Hochreiter, Sepp, et al. "Gradient flow in recurrent nets: the difficulty of learning long-term dependencies." (2001). 27. Domingos, Pedro. "A few useful things to know about machine learning." Communications of the ACM 55.10 (2012): 78-87.