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Open Access

Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble

Information Systems Department, Faculty of Computing and Information Technology (FCIT), King Abdulaziz University (KAU), Jeddah 21589, Kingdom of Saudi Arabia
Information Systems Department, FCIT, KAU, Jeddah 21589, Kingdom of Saudi Arabia, and also with School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah 22246, Kingdom of Saudi Arabia
Information Technology Department, FCIT, KAU, Jeddah 21589, Kingdom of Saudi Arabia, and also with Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, Egypt
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Abstract

The ability to make accurate energy predictions while considering all related energy factors allows production plants, regulatory bodies, and governments to meet energy demand and assess the effects of energy-saving initiatives. When energy consumption falls within normal parameters, it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy consumption. The objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily interpretable. The proposed model is an implementation of the stacked Long Short-Term Memory (LSTM) snapshot ensemble combined with the Fast Fourier Transform (FFT) and meta-learner. Hebrail and Berard’s Individual Household Electric-Power Consumption (IHEPC) dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy consumption. The model is trained, and the results measured using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination ( R2) metrics are 0.020, 0.013, 0.017, and 0.999, respectively. The stacked LSTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized errors. The results of this study show that prediction accuracy is high, and the model’s stability is high as well. The model shows that high levels of accuracy prove accurate predictive ability, and together with high levels of stability, the model has good interpretability, which is not typically accounted for in models. However, this study shows that it can be inferred.

Keywords

Artificial Intelligence (AI)Deep Learning (DL)energy consumptionsnapshot ensembleprediction

References

[1]

M. Cellura, F. Guarino, S. Longo, and G. Tumminia, Climate change and the building sector: Modelling and energy implications to an office building in southern Europe, Energy Sustain. Dev., vol. 45, pp. 46–65, 2018.
Crossref Google Scholar
[2]

T. Zhang, D. Wang, H. Liu, Y. Liu, and H. Wu, Numerical investigation on building envelope optimization for low-energy buildings in low latitudes of China, Build. Simul., vol. 13, no. 2, pp. 257–269, 2020.
Crossref Google Scholar
[3]

A. D. Pham, N. T. Ngo, T. T. Ha Truong, N. T. Huynh, and N. S. Truong, Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability, J. Cleaner Prod., vol. 260, p. 121082, 2020.
Crossref Google Scholar
[4]

A. Raza, T. N. Malik, M. F. N. Khan, and S. Ali, Energy management in residential buildings using energy hub approach, Build. Simul., vol. 13, no. 2, pp. 363–386, 2020.
Crossref Google Scholar
[5]

Z. Wang and R. S. Srinivasan, A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models, Renew. Sustain. Energy Rev., vol. 75, pp. 796–808, 2017.
Crossref Google Scholar
[6]

H. Song, A. K. Qin, and F. D. Salim, Evolutionary model construction for electricity consumption prediction, Neural Comput. Appl., vol. 32, no. 16, pp. 12155–12172, 2020.
Crossref Google Scholar
[7]

E. Jahani, K. Cetin, and I. H. Cho, City-scale single family residential building energy consumption prediction using genetic algorithm-based numerical moment matching technique, Build. Environ., vol. 172, p. 106667, 2020.
Crossref Google Scholar
[8]
J. Xu, W. Gao, and X. Huo, Analysis on energy consumption of rural building based on survey in northern China, Energy Sustain. Dev., vol. 47, pp. 34–38, 2018.
Crossref
[9]

A. Zeng, H. Ho, and Y. Yu, Prediction of building electricity usage using Gaussian Process Regression, J. Build. Eng., vol. 28, p. 101054, 2020.
Crossref Google Scholar
[10]

C. Dai, H. Zhang, E. Arens, and Z. Lian, Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions, Build. Environ., vol. 114, pp. 1–10, 2017.
Crossref Google Scholar
[11]

C. Xu, H. Chen, J. Wang, Y. Guo, and Y. Yuan, Improving prediction performance for indoor temperature in public buildings based on a novel deep learning method, Build. Environ., vol. 148, pp. 128–135, 2019.
Crossref Google Scholar
[12]

D. K. Bui, T. N. Nguyen, T. D. Ngo, and H. Nguyen-Xuan, An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings, Energy, vol. 190, p. 116370, 2020.
Crossref Google Scholar
[13]

M. A. Jallal, A. González-Vidal, A. F. Skarmeta, S. Chabaa, and A. Zeroual, A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction, Appl. Energy, vol. 268, p. 114977, 2020.
Crossref Google Scholar
[14]

S. Ganguly, A. Ahmed, and F. Wang, Optimised building energy and indoor microclimatic predictions using knowledge-based system identification in a historical art gallery, Neural Comput. Appl., vol. 32, no. 8, pp. 3349–3366, 2020.
Crossref Google Scholar
[15]
J. S. Chou, N. T. Ngo, W. K. Chong, and G. E. Jr Gibson, Big data analytics and cloud computing for sustainable building energy efficiency, in Start-Up Creation : The Smart Eco-Efficient Built Environment, F. Pacheco-Torgal, E. Rasmussen, C. G. Granqvist, V. Ivanov, A. Kaklauskas, and S. Makonin, eds. Cambridge, UK: Woodhead Publishing, 2016, pp. 397–412.
Crossref
[16]

S. Seyedzadeh, F. Pour Rahimian, P. Rastogi, and I. Glesk, Tuning machine learning models for prediction of building energy loads, Sustain. Cities Soc., vol. 47, p. 101484, 2019.
Crossref Google Scholar
[17]

S. Badillo, B. Banfai, F. Birzele, I. I. Davydov, L. Hutchinson, T. Kam-Thong, J. Siebourg-Polster, B. Steiert, and J. D. Zhang, An introduction to machine learning, Clin. Pharmacol. Ther., vol. 107, no. 4, pp. 871–885, 2020.
Crossref Google Scholar
[18]

J. Runge and R. Zmeureanu, Forecasting energy use in buildings using artificial neural networks: A review, Energies, vol. 12, no. 17, p. 3254, 2019.
Crossref Google Scholar
[19]
J. Moon, S. Park, S. Rho, and E. Hwang, A comparative analysis of artificial neural network architectures for building energy consumption forecasting, Int. J. Distrib. Sens. Netw., vol. 15, no. 9, p. 155014771987761, 2019.
Crossref
[20]

M. F. Alsharekh, S. Habib, D. A. Dewi, W. Albattah, M. Islam, and S. Albahli, Improving the efficiency of multistep short-term electricity load forecasting via R-CNN with ML-LSTM, Sensors, vol. 22, no. 18, p. 6913, 2022.
Crossref Google Scholar
[21]

A. Moradzadeh, S. Zakeri, M. Shoaran, B. Mohammadi-Ivatloo, and F. Mohammadi, Short-term load forecasting of microgrid via hybrid support vector regression and long short-term memory algorithms, Sustainability, vol. 12, no. 17, p. 7076, 2020.
Crossref Google Scholar
[22]
R. K. Agrawal, F. Muchahary, and M. M. Tripathi, Long term load forecasting with hourly predictions based on long-short-term-memory networks, in Proc. 2018 IEEE Texas Power and Energy Conf. (TPEC ), Station, TX, USA, 2018, pp. 1–6.
Crossref
[23]
J. Q. Wang, Y. Du, and J. Wang, LSTM based long-term energy consumption prediction with periodicity, Energy, vol. 197, p. 117197, 2020.
Crossref
[24]

A. Galicia, R. Talavera-Llames, A. Troncoso, I. Koprinska, and F. Martínez-Álvarez, Multi-step forecasting for big data time series based on ensemble learning, Knowl.-Based Syst., vol. 163, pp. 830–841, 2019.
Crossref Google Scholar
[25]

Y. Yang, W. Hong, and S. Li, Deep ensemble learning based probabilistic load forecasting in smart grids, Energy, vol. 189, p. 116324, 2019.
Crossref Google Scholar
[26]

M. Azarafza, M. Azarafza, H. Akgün, P. M. Atkinson, and R. Derakhshani, Deep learning-based landslide susceptibility mapping, Sci. Rep., vol. 11, no. 1, p. 24112, 2021.
Crossref Google Scholar
[27]

P. T. T. Ngo, M. Panahi, K. Khosravi, O. Ghorbanzadeh, N. Kariminejad, A. Cerda, and S. Lee, Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran, Geosci. Front., vol. 12, no. 2, pp. 505–519, 2021.
Crossref Google Scholar
[28]

W. Xie, X. Li, W. Jian, Y. Yang, H. Liu, L. F. Robledo, and W. Nie, A novel hybrid method for landslide susceptibility mapping-based GeoDetector and machine learning cluster: A case of Xiaojin County, China, ISPRS Int. J. Geo-Inf., vol. 10, no. 2, p. 93, 2021.
Crossref Google Scholar
[29]
A. L. Achu, G. Gopinath, and U. Surendran, Landslide susceptibility modelling using deep-learning and machine-learning methods—A study from southern Western Ghats, India, in Proc. 2021 IEEE Int. India Geoscience and Remote Sensing Symp. (InGARSS ), Ahmedabad, India, 2021, pp. 360–364.
Crossref
[30]
G. Hebrail and A. Berard, Individual household electric power consumption, https://archive.ics.uci.edu/dataset/235/individual+household+electric+power+consumption, 2012.
[31]

J. F. Torres, A. Galicia, A. Troncoso, and F. Martínez-Álvarez, A scalable approach based on deep learning for big data time series forecasting, Integr. Comput.-Aided Eng., vol. 25, no. 4, pp. 335–348, 2018.
Crossref Google Scholar
[32]
J. F. Torres, D. Gutiérrez-Avilés, A. Troncoso, and F. Martínez-Álvarez, Random hyper-parameter search-based deep neural network for power consumption forecasting, in Proc. 15 th Int. Work-Conf. Artificial Neural Networks, Gran Canaria, Spain, 2019, pp. 259–269.
Crossref
[33]

W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang, Short-term residential load forecasting based on LSTM recurrent neural network, IEEE Trans. Smart Grid, vol. 10, no. 1, pp. 841–851, 2019.
Crossref Google Scholar
[34]
N. M. Ibrahim, A. I. Megahed, and N. H. Abbasy, Short-term individual household load forecasting framework using LSTM deep learning approach, in Proc. 5 th Int. Symp. Multidisciplinary Studies and Innovative Technologies (ISMSIT ), Ankara, Turkey, 2021, pp. 257–262.
Crossref
[35]
C. Xu, W. Li, M. Yu, J. Xu, J. Liu, Y. Wang, and L. Zhu, A correlation sorting-LSTM method for high accuracy short-term load forecasting based on smart meter data, in Proc. 7 th Int. Conf. Information, Cybernetics, and Computational Social Systems (ICCSS ), Guangzhou, China, 2020, pp. 755–760.
Crossref
[36]

M. Ma, C. Liu, R. Wei, B. Liang, and J. Dai, Predicting machine’s performance record using the stacked long short-term memory (LSTM) neural networks, J. Appl. Clin. Med. Phys., vol. 23, no. 3, p. E13558, 2022.
Crossref Google Scholar
[37]
G. Huang, Y. Li, G. Pleiss, Z. Liu, J. E. Hopcroft, and K. Q. Weinberger, Snapshot ensembles: Train 1, get M for free, arXiv preprint arXiv:1704.00109, 2017.
[38]

T. Y. Kim and S. B. Cho, Predicting residential energy consumption using CNN-LSTM neural networks, Energy, vol. 182, pp. 72–81, 2019.
Crossref Google Scholar
[39]
R. Rajabi and A. Estebsari, Deep learning based forecasting of individual residential loads using recurrence plots, in Proc. 2019 IEEE Milan PowerTech, Milan, Italy, 2019, pp. 1–5.
Crossref
[40]

Z. A. Khan, T. Hussain, A. Ullah, S. Rho, M. Lee, and S. W. Baik, Towards efficient electricity forecasting in residential and commercial buildings: A novel hybrid CNN with an LSTM-AE based framework, Sensors, vol. 20, no. 5, p. 1399, 2020.
Crossref Google Scholar
[41]

F. U. M. Ullah, A. Ullah, I. U. Haq, S. Rho, and S. W. Baik, Short-term prediction of residential power energy consumption via CNN and multi-layer bi-directional LSTM networks, IEEE Access, vol. 8, pp. 123369–123380, 2020.
Crossref Google Scholar
[42]

M. Sajjad, Z. A. Khan, A. Ullah, T. Hussain, W. Ullah, M. Y. Lee, and S. W. Baik, A novel CNN-GRU-based hybrid approach for short-term residential load forecasting, IEEE Access, vol. 8, pp. 143759–143768, 2020.
Crossref Google Scholar
[43]

Z. A. Khan, T. Hussain, and S. W. Baik, Boosting energy harvesting via deep learning-based renewable power generation prediction, J. King Saud Univ.—Sci., vol. 34, no. 3, p. 101815, 2022.
Crossref Google Scholar
[44]
M. Abdel-Basset, H. Hawash, K. Sallam, S. S. Askar, and M. Abouhawwash, STLF-Net: Two-stream deep network for short-term load forecasting in residential buildings, J. King Saud Univ.—Comput. Inf. Sci., vol. 34, no. 7, pp. 4296–4311, 2022.
Crossref
[45]

S. U. Khan, I. U. Haq, Z. A. Khan, N. Khan, M. Y. Lee, and S. W. Baik, Atrous convolutions and residual GRU based architecture for matching power demand with supply, Sensors, vol. 21, no. 21, p. 7191, 2021.
Crossref Google Scholar
[46]

Z. A. Khan, T. Hussain, I. U. Haq, F. U. M. Ullah, and S. W. Baik, Towards efficient and effective renewable energy prediction via deep learning, Energy Rep., vol. 8, pp. 10230–10243, 2022.
Crossref Google Scholar
[47]
D. L. Marino, K. Amarasinghe, and M. Manic, Building energy load forecasting using deep neural networks, in Proc. 42 nd Annu. Conf. IEEE Industrial Electronics Society, Florence, Italy, 2016, pp. 7046–7051.
Crossref
[48]

F. U. M. Ullah, N. Khan, T. Hussain, M. Y. Lee, and S. W. Baik, Diving deep into short-term electricity load forecasting: Comparative analysis and a novel framework, Mathematics, vol. 9, no. 6, p. 611, 2021.
Crossref Google Scholar
[49]
N. Khan, I. U. Haq, S. U. Khan, S. Rho, M. Y. Lee, and S. W. Baik, DB-Net: A novel dilated CNN based multi-step forecasting model for power consumption in integrated local energy systems, Int. J. Electr. Power Energy Syst., vol. 133, p. 107023, 2021.
Crossref
[50]

J. Y. Kim and S. B. Cho, Electric energy consumption prediction by deep learning with state explainable autoencoder, Energies, vol. 12, no. 4, p. 739, 2019.
Crossref Google Scholar
[51]

I. U. Haq, A. Ullah, S. U. Khan, N. Khan, M. Y. Lee, S. Rho, and S. W. Baik, Sequential learning-based energy consumption prediction model for residential and commercial sectors, Mathematics, vol. 9, no. 6, p. 605, 2021.
Crossref Google Scholar
[52]

S. J. Bu and S. B. Cho, Time series forecasting with multi-headed attention-based deep learning for residential energy consumption, Energies, vol. 13, no. 18, p. 4722, 2020.
Crossref Google Scholar
[53]
N. Khan, I. U. Haq, F. U. M. Ullah, S. U. Khan, and M. Y. Lee, CL-Net: ConvLSTM-based hybrid architecture for batteries’ state of health and power consumption forecasting, Mathematics, vol. 9, no. 24, p. 3326, 2021.
Crossref
[54]

U. Ugurlu, I. Oksuz, and O. Tas, Electricity price forecasting using recurrent neural networks, Energies, vol. 11, no. 5, p. 1255, 2018.
Crossref Google Scholar
Big Data Mining and Analytics
Volume 7 Issue 2,
June 2024
Pages 247-270
DOI: 10.26599/BDMA.2023.9020030
Cite this article:
Ahamd Alghamdi M, AL-Malaise AL-Ghamdi AS, Ragab M. Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble. Big Data Mining and Analytics, 2024, 7(2): 247-270. https://doi.org/10.26599/BDMA.2023.9020030

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Received: 19 January 2023
Revised: 04 June 2023
Accepted: 16 October 2023
Published: 22 April 2024
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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