Household Power Consumption Forecasting using IoT Smart Home Data

Fitri Indra Indikawati, Guntur Maulana Zamroni

Abstract


The use of the forecasting system is becoming more prominent in recent years. One of the implementations of the forecasting system is to predict electricity consumption demand. In this paper, we have developed a forecasting system for household electricity consumption using a well-known Extreme Gradient Boosting algorithm. We utilized time-series data from a smart meter dataset to make a predictive model. First, we evaluated the importance of time-series feature from the dataset and resampled the original dataset. Then, we used the resampled data to train the model and calculated training loss function. Our experimental studies with real IoT Smart Home data demonstrate that our forecasting system works well with small dataset using one-hour downsampling on the dataset.

Keywords


time-series forecasting; power consumption; smart meter; IoT

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References


S. Sorrell, “Reducing energy demand: A review of issues, challenges and approaches,” Renewable and Sustainable Energy Reviews, vol. 47, pp. 74–82, 2015, doi: 10.1016/j.rser.2015.03.002.

F. Karanfil and Y. Li, “Electricity consumption and economic growth: exploring panel-specific differences,” Energy Policy, vol. 82, pp. 264–277, 2015, doi: 10.1016/j.enpol.2014.12.001.

S. Sarwar, W. Chen, and R. Waheed, “Electricity consumption, oil price and economic growth: Global perspective,” Renewable and Sustainable Energy Reviews, vol. 76, pp. 9–18, 2017, doi: 10.1016/j.rser.2017.03.063.

Z. Mohamed and P. Bodger, “Forecasting Electricity Consumption A comparison of models for New Zealand,” in Electricity Engineers’ Association of New Zealand Annual Conference, 2004 doi: 10.1016/j.rser.2017.03.063.

F. Kaytez, M. C. Taplamacioglu, E. Cam, and F. Hardalac, “Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines,” International Journal of Electrical Power & Energy Systems, vol. 67, pp. 431–438, 2015, doi: 10.1016/j.ijepes.2014.12.036.

A. Veit, C. Goebel, R. Tidke, C. Doblander, and H.-A. Jacobsen, “Household Electricity Demand Forecasting--Benchmarking State-of-the-Art Methods,” in Proceedings of the 5th International Conference on Future Energy Systems, 2014, available at: https://arxiv.org/abs/1404.0200.

H. Guo, Q. Chen, Q. Xia, C. Kang, and X. Zhang, “A monthly electricity consumption forecasting method based on vector error correction model and self-adaptive screening method,” International Journal of Electrical Power and Energy Systems, pp. 427–439, 2018, doi: 10.1016/j.ijepes.2017.09.011.

A. Hussain, M. Rahman, and J. A. Memon, “Forecasting electricity consumption in Pakistan: The way forward,” Energy Policy, no. 90, pp. 73–80, 2016, doi: 10.1016/j.enpol.2015.11.028.

F. L. Quilumba, W.-J. Lee, H. Huang, D. Y. Wang, and R. L. Szabados, “Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities,” IEEE Transactions on Smart Grid, vol. 6, no. 2, pp. 911–918, 2015, doi: 10.1109/TSG.2014.2364233.

J. Kwac, J. Flora, and R. Rajagopal, “Household energy consumption segmentation using hourly data,” IEEE Transactions on Smart Grid, vol. 5, no. 1, pp. 420–430, 2014, doi: 10.1109/TSG.2013.2278477.

S. Haben, C. Singleton, and P. Grindrod, “Analysis and clustering of residential customers energy behavioral demand using smart meter data,” IEEE transactions on smart grid, vol. 7, no. 1, pp. 136–144, 2016, doi: 10.1109/TSG.2015.2409786.

T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794, 2016, available at: https://arxiv.org/abs/1603.02754.

Kaggle, “Smart Home Dataset with Weather Information.”, available at: Google.

Python, “Python Panda Dataframe.”, available at: Google.

J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, pp. 1189–1232, 2001, available at : Google Scholar.




DOI: http://dx.doi.org/10.26555/jiteki.v5i1.13184

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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika
ISSN 2338-3070 (print) | 2338-3062 (online)
Organized by Electrical Engineering Department - Universitas Ahmad Dahlan
Published by Universitas Ahmad Dahlan
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