Prediction of Electricity Consumption in Residential Area using Random Forest and CNN with Bi-LSTM
Keywords:
Electricity prediction, Gabor filter, hybrid CNN–Bi-LSTM attenuation and Random Forest.Abstract
According to an intelligent power management model, the complex and significant task is electric energy consumption forecasting. The electricity utilization has major impacts on energy management, energy distribution costs and environment. While it comes to power usage prediction, the long-standing model contains inherent restrictions like scalability and accuracy. This paper presents a novel artificial technique to predict the electricity consumption in residential area. The panda’s package selects the input data based on electricity residential dataset. The Gabor filter is used to pre-process the input data to handle the missing data, executing label encoding and removing unnecessary columns. The Greedy stepwise with correlation feature selection to select the relevant features. In residential area, the electricity prediction is performed using a Random Forest (RF) model and Hybrid CNN–Bi-LSTM Attenuation. The Python software implements the simulations results with respect to various measures namely RMSE, MAE, MSE, confusion matrix, ROC, recall, precision and accuracy. Due to the experimental results, the proposed method reveals better results than previous methods in case of electricity prediction.
Downloads
References
Dinh, H.T., Yun, J., Kim, D.M., Lee, K.H. and Kim, D., 2020. A home energy management system with renewable energy and energy storage utilizing main grid and electricity selling. IEEE access, 8, pp.49436-49450.
Yan, H., Chen, Y. and Yang, S.H., 2020. UAV-enabled wireless power transfer with base station charging and UAV power consumption. IEEE Transactions on Vehicular Technology, 69(11), pp.12883-12896.
Ge, X., Xu, F., Wang, Y., Li, H., Wang, F., Hu, J., Li, K., Lu, X. and Chen, B., 2022. Spatio-temporal two-dimensions data-based customer baseline load estimation approach using LASSO regression. IEEE Transactions on Industry Applications, 58(3), pp.3112-3122.
Turner, C.J., Oyekan, J., Stergioulas, L. and Griffin, D., 2020. Utilizing industry 4.0 on the construction site: Challenges and opportunities. IEEE Transactions on Industrial Informatics, 17(2), pp.746-756.
Oshnoei, A., Kheradmandi, M. and Muyeen, S.M., 2020. Robust control scheme for distributed battery energy storage systems in load frequency control. IEEE Transactions on Power Systems, 35(6), pp.4781-4791.
Joseph, A. and Balachandra, P., 2020. Smart grid to energy internet: A systematic review of transitioning electricity systems. IEEE Access, 8, pp.215787-215805.
Alam, M.S., Al-Ismail, F.S., Salem, A. and Abido, M.A., 2020. High-level penetration of renewable energy sources into grid utility: Challenges and solutions. IEEE Access, 8, pp.190277-190299.
Bai, Y., Li, J., He, H., Dos Santos, R.C. and Yang, Q., 2020. Optimal design of a hybrid energy storage system in a plug-in hybrid electric vehicle for battery lifetime improvement. IEEE Access, 8, pp.142148-142158.
Banik, R., Das, P., Ray, S. and Biswas, A., 2021. Prediction of electrical energy consumption based on machine learning technique. Electrical Engineering, 103, pp.909-920.
Olu-Ajayi, R., Alaka, H., Sulaimon, I., Sunmola, F. and Ajayi, S., 2022. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. Journal of Building Engineering, 45, p.103406.
Alden, R.E., Gong, H., Jones, E.S., Ababei, C. and Ionel, D.M., 2021. Artificial intelligence method for the forecast and separation of total and hvac loads with application to energy management of smart and nze homes. IEEE Access, 9, pp.160497-160509.
Baba, A., 2021. Advanced AI-based techniques to predict daily energy consumption: A case study. Expert Systems with Applications, 184, p.115508.
Khan, P.W. and Byun, Y.C., 2020. Genetic algorithm based optimized feature engineering and hybrid machine learning for effective energy consumption prediction. Ieee Access, 8, pp.196274-196286.
Rocha, H.R., Honorato, I.H., Fiorotti, R., Celeste, W.C., Silvestre, L.J. and Silva, J.A., 2021. An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes. Applied Energy, 282, p.116145.
Abera, F.Z. and Khedkar, V., 2020. Machine learning approach electric appliance consumption and peak demand forecasting of residential customers using smart meter data. Wireless Personal Communications, 111, pp.65-82.
Abdulrahman, M.L., Ibrahim, K.M., Gital, A.Y., Zambuk, F.U., Ja’afaru, B., Yakubu, Z.I. and Ibrahim, A., 2021. A Review on Deep Learning with Focus on Deep Recurrent Neural Network for Electricity Forecasting in Residential Building. Procedia Computer Science, 193, pp.141-154.
Flores, V.H. and Rivera, M., 2020. Robust two-step phase estimation using the Simplified Lissajous Ellipse Fitting method with Gabor Filters Bank preprocessing. Optics Communications, 461, p.125286.
Pourhashemi, S.M. and Mashalizadeh, A.M., 2013. A novel feature selection method using CFS with Greedy-Stepwise search algorithm in e-mail spam filtering. Semantic Scholar: Seattle, WA, USA, 15.
Huang, N., Lu, G. and Xu, D., 2016. A permutation importance-based feature selection method for short-term electricity load forecasting using random forest. Energies, 9(10), p.767.
Le, T., Vo, M.T., Vo, B., Hwang, E., Rho, S. and Baik, S.W., 2019. Improving electric energy consumption prediction using CNN and Bi-LSTM. Applied Sciences, 9(20), p.4237.
Li, K., Hu, C., Liu, G. and Xue, W., 2015. Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis. Energy and Buildings, 108, pp.106-113.
Mohsenian-Rad, A.H. and Leon-Garcia, A., 2010. Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE transactions on Smart Grid, 1(2), pp.120-133.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
