Prediction of Electric Energy Consumption using Recurrent Neural Networks
AUTHORS
Md. Abdur Rahman,Professor, Department of Mathematics, Jahangirnagar University, Dhaka, Bangladesh
Tarafder Md. Mehedi Al Masud,Assistant Professor, Southeast Business School, Southeast University, Dhaka, Bangladesh
Purba Biswas,Student, Department of Mathematics, Jahangirnagar University, Dhaka, Bangladesh
ABSTRACT
The prediction of power consumption is a complex and important task for a smart home, a city, and a country. However, deep learning plays a substantial role in predicting electric energy consumption more efficiently. Recurrent Neural Network (RNN) of deep learning is well capable of handling time-series datasets and predicting the electricity consumption better than the machine learning approaches such as ARMA, SVR. In this work, we use a large electricity consumption dataset of Dominion Virginia Power (DOM) using the proposed RNN approach to identify the hidden patterns of the dataset as well as predict electric power consumption. The results from the proposed approach are compared with the above-mentioned approaches to validate the performance to unveil the hidden patterns and predict the consumption behaviors. The accuracy is varied from 94.02% to 96.86% based on the number of epochs. Also, the error matrices like MSE, RMSE, MAE, and MAPE are demonstrated to validate its robustness for the prediction of electricity consumption. understanding the demand for electric power.
KEYWORDS
Deep learning, Electric power consumption prediction, Electric power consumption prediction model, Machine learning, Recurrent neural networks
REFERENCES
[1] G. M. Uebner, I. Hamilton, Z. Chalabi, D. Shipworth, and T. Oreszczyn, “Explaining domestic energy consumption – The comparative contribution of building factors, socio-demographics, behaviors and attitudes,” Applied energy, vol.159, pp.589-600, (2015)
[2] L. Pérez-Lombard, J. Ortiz, C. and Pout, “A review on buildings energy consumption information. Energy and buildings,” vol.40, no.3, pp.394-398, (2008)
[3] E. Cuce, D. Harjunowibowo, and P. M. Cuce, “Renewable and sustainable energy-saving strategies for greenhouse systems: A comprehensive review,” Renewable and Sustainable Energy Reviews, vol.64, pp.34-59, (2016)
[4] H. R. Khosravani, M. D. M. Castilla, M. Berenguel, A. E. Ruano, and P. M. Ferreira, “A comparison of energy consumption prediction models based on neural networks of a bioclimatic building,” Energies, vol.9, no.1, pp.57, (2016)
[5] H. Li and G. Henkelman, “Dehydrogenation selectivity of ethanol on close-packed transition metal surfaces: A computational study of monometallic Pd/Au, and Rh/Au catalysts,” The Journal of Physical Chemistry C, vol.121, no.49, pp.27504-27510, (2017)
[6] H. Li and Z. Liu, “Performance prediction and optimization of solar water heater via a knowledge-based machine learning method,” In Handbook of Research on Power and Energy System Optimization, pp. 55-74, IGI Global, (2018)
[7] Y. Liu, H. Li, W. Cen, J. Li, Z. Wang, and G. Henkelman, “A computational study of supported Cu-based bimetallic nanoclusters for CO oxidation,” Physical Chemistry Chemical Physics, vol.20, no.11, pp.7508-7513, (2018)
[8] H. Liu, S. Yue, Y. Wang, and J. Zhang, “Unsteady study on the effects of matching characteristic of tandem cascade on the performance and flow at a large angle of attack,” Journal of Thermal Science, vol.27, no.6, pp.505-515, (2018)
[9] Z. Liu, D. Wu, Y. Liu, Z. Han, L. Lun, J. Gao, G. Jin, and G. Cao, “Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction. Energy Exploration and Exploitation,” vol.37, no.4, pp.1426-1451, (2019)
[10] M. Bauer and J. L. Scartezzini, ‘A simplified correlation method accounting for heating and cooling loads in energy-efficient buildings,” Energy and Buildings, vol.27, no.2, pp.147-154, (1998)
[11] A. Dhar, T. A. Reddy, and D. E. Claridge, “A Fourier series model to predict hourly heating and cooling energy use in commercial buildings with outdoor temperature as the only weather variable, (1999)
[12] M. Aydinalp, V. I. Ugursal, and A. S. Fung, “Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks,” Applied Energy, vol.71, no.2, pp.87-110, (2002)
[13] A. Lomet, F. Suard, and D. Chèze, “Statistical modeling for real domestic hot water consumption forecasting,” Energy Procedia, vol.70, pp.379-387, (2015)
[14] X. Lü, T. Lu, C. J. Kibert, and M. Viljanen, “Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach,” Applied Energy, vol.144, pp.261-275, (2015)
[15] A. L. Samuel, “Some studies in machine learning using the game of checkers,” II—Recent progress, IBM Journal of research and development, vol.11, no.6, pp.601-617, (1967)
[16] W. Chen, H. R. Pourghasemi, A. Kornejady, and N. Zhang, “Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques,” Geoderma, vol.305, pp.314-327, (2017)
[17] U. Ugurlu, I. Oksuz, and O. Tas, “Electricity price forecasting using recurrent neural networks,” Energies, vol.11, no.5, pp.1255, (2018)
[18] S. Mishra and P. Palanisamy, “Multi-time-horizon solar forecasting using recurrent neural network,” In 2018 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 18-24, IEEE, September, (2018)
[19] A. Rahman, V. Srikumar, and A. D. Smith, “Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks,” Applied Energy, vol.212, pp.372-385, (2018)
[20] M. B. Zia, Z. J. Juan, and N. Xiao, “Detection and classification of a lung nodule in diagnostic CT: A TsDN method based on improved 3D-faster R-CNN and multi-scale multi-crop convolutional neural network,” International Journal of Hybrid Information Technology, vol.13, no.2, pp.45-56, (2020)
[21] M. A. Rahman, “Deep learning approaches for tomato plant disease detection,” International Journal of Hybrid Information Technology, Global Vision Press, vol.13, no.2, pp.71-78, Australia, (2020)
[22] M. A. Rahman, “Detection of distributed denial of service attacks based on machine learning algorithms,” International Journal of Smart Home, Global Vision Press, vol.14, no.2, pp.15-24, Australia, (2020)
[23] T. Y. Kim and S. B. Cho, “Predicting residential energy consumption using CNN-LSTM neural networks,” Energy, vol.182, pp.72-81, (2019)
[24] T. Y. Kim and S. B. Cho, “Electric energy consumption prediction by deep learning with state explainable autoencoder,” Energies, vol.12, no.4, pp.739, (2019)
[25] T. Lee, M. T. Vo, B. Vo, E. Hwang, S. Rho, and S. W. Baik, ‘Improving electric energy consumption prediction using CNN and Bi-LSTM,” Applied Sciences, vol.9, no.20, pp.4237, (2019)
[26] P. Chujai, N. Kerdprasop, and K. Kerdprasop, “Time series analysis of household electricity consumption with ARIMA and ARMA models,” In Proceedings of the International MultiConference of Engineers and Computer Scientists, vol.1, pp.295-300, March, (2013)
[27] R. Rajabi and A. Estebsari, “Deep learning-based forecasting of individual residential loads using recurrence plots,” In 2019 IEEE Milan PowerTech. pp.1-5, June, (2019)