Comparative Analysis of Deep Learning Techniques Based COVID-19 Impact Assessment on Electricity Consumption in Distribution Network
Keywords:
COVID-19, Energy Consumption, Distribution NetworkAbstract
Energy is a fundamental human need for several activities. Energy can be impacted by quite a number of factors ranging from technical to social and environmental. The impact of COVID-19 outbreak on the energy sector is enormous with serious global socioeconomic disruptions affecting all economic sectors, including tourism, industry, higher education, and electricity industry. Based on the unstructured data obtained from Eko Electricity Distribution Company this paper proposes three deep learning (DL) models namely: Long Short-Term Memory (LSTM), Simple Recurrent Neural Network (SimpleRNN), and Gated Recurrent Unit (GRU) were used to analyse the effect of COVID-19 pandemic on energy consumption and predict future energy consumption in various district in Lagos, Nigeria. The models were evaluated using the following performance metrics namely: Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). On the overall, the lowest MAPE, MAE, RMSE, and MSE of 0.120, 71.073, 93.981, and 8832.466 were obtained for LSTM in Orile, SRNN in Ijora, GRU in Ijora, and GRU in Ijora, respectively. Generally, the GRU performed better in predicting the energy consumption in most of the districts of the case study than the LSTM and SimpleRNN. Hence, it can be considered the optimal model for energy consumption prediction in the case study. The importance of having this model is that they can help the government and other stakeholder in economic planning.
References
Abraham, B., & Nair, M. S. (2020). Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier. Biocybernetics and Biomedical Engineering, 40(4), 1436–1445. https://doi.org/10.1016/j.bbe.2020.08.005
Ahn, H. K., & Park, N. (2021). Deep rnn-based photovoltaic power short-term forecast using power iot sensors. Energies, 14(2), 1–17. https://doi.org/10.3390/en14020436
Ahshan, R., Al-Abri, R., Al-Zakwan, H., Ambu-saidi, N., & Hossain, E. (2020). Design and economic analysis of a solar photovoltaic system for a campus sports complex. International Journal of Renewable Energy Research, 10(1), 67–78.
Alavi, A., Sadid, M. S., Ahmed, M., & Abid, F. (2022). Effect analysis of the COVID-19 pandemic on the electricity consumption of Bangladesh. Heliyon, 8(1), e08737. https://doi.org/10.1016/j.heliyon.2022.e08737
Almalaq, A., & Zhang, J. J. (2019). Evolutionary Deep Learning-Based Energy Consumption Prediction for Buildings. IEEE Access, 7(c), 1520–1531. https://doi.org/10.1109/ACCESS.2018.2887023
Amole, A. O., Okelola, M. O., & Amole, G. O. (2020). Reliability Assessment of some Selected 11 kV Feeders within Ibadan Distribution Network. 6(2), 39–46. https://doi.org/10.26555/jiteki.v6i2.18970
Azeem, A., Ismail, I., Jameel, S. M., & Harindran, V. R. (2021). Electrical Load Forecasting Models for Different Generation Modalities : A Review. IEEE Access, PP, 1. https://doi.org/10.1109/ACCESS.2021.3120731
Bahij, M., Labbadi, M., Chatri, C., Cheikh Ahmed, C., & Cherkaoui, M. (2022). Evolutionary Machine Learning-Based Energy Consumption Prediction for the industry. E3S Web of Conferences, 351, 01091. https://doi.org/10.1051/e3sconf/202235101091
De Araujo, J. M. S. (2019). WRF Wind Speed Simulation and SAM Wind Energy Estimation: A Case Study in Dili Timor Leste. IEEE Access, 7, 35382–35393. https://doi.org/10.1109/ACCESS.2019.2904755
Edomah, N., & Ndulue, G. (2020). Energy transition in a lockdown: An analysis of the impact of COVID-19 on changes in electricity demand in Lagos Nigeria. Global Transitions, 2, 127–137. https://doi.org/10.1016/j.glt.2020.07.002
Elsaraiti, M., & Merabet, A. (2021). A comparative analysis of the arima and lstm predictive models and their effectiveness for predicting wind speed. Energies, 14(20). https://doi.org/10.3390/en14206782
Fezzi, C., & Fanghella, V. (2020). Real-Time Estimation of the Short-Run Impact of COVID-19 on Economic Activity Using Electricity Market Data. Environmental and Resource Economics, 76(4), 885–900. https://doi.org/10.1007/s10640-020-00467-4
Ghiani, E., Galici, M., Mureddu, M., & Pilo, F. (2020). Impact on Electricity Consumption and Market Pricing of Energy and Ancillary Services during Pandemic of COVID-19 in Italy.
Haq, I. U., Ullah, A., Khan, S. U., Khan, N., Lee, M. Y., Rho, S., & Baik, S. W. (2021). Sequential learning‐based energy consumption prediction model for residential and commercial sectors. Mathematics, 9(6), 1–17. https://doi.org/10.3390/math9060605
Hernandez-Matamoros, A., Fujita, H., Hayashi, T., & Perez-Meana, H. (2020). Forecasting of COVID19 per regions using ARIMA models and polynomial functions. Applied Soft Computing Journal, 96, 106610. https://doi.org/10.1016/j.asoc.2020.106610
Ibrahim, B., & Rabelo, L. (2021). A deep learning approach for peak load forecasting: A case study on panama. Energies, 14(11). https://doi.org/10.3390/en14113039
Jangali Satish, G., & Nagesha, N. (2015). Energy Consumption Pattern and Environmental Implications in Commercial Sector: a Case Study of an Indian City. International Journal of Environment, Ecology, Family and Urban Studies, 6(1), 39–50.
Ji, Y., Wang, J., Xu, J., & Li, D. (2021). Data-driven online energy scheduling of a microgrid based on deep reinforcement learning. Energies, 14(8). https://doi.org/10.3390/en14082120
Jiang, J., Kong, Q., Plumbley, M. D., Gilbert, N., Hoogendoorn, M., & Roijers, D. M. (2021). Deep Learning-Based Energy Disaggregation and On/Off Detection of Household Appliances. ACM Transactions on Knowledge Discovery from Data, 15(3), 1–21. https://doi.org/10.1145/3441300
Jogunola, O., Adebisi, B., Van Hoang, K., Tsado, Y., Popoola, S. I., Hammoudeh, M., & Nawaz, R. (2022). CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption. Energies, 15(3), 1–16. https://doi.org/10.3390/en15030810
Kalluru, P., Tulimilli, L., Vallabhaneni, S. P., & Adimulam, R. V. P. (2022). Deep Learning Based Approach to Predict Energy Consumption. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4085270
Khan, A. N., Iqbal, N., Rizwan, A., Ahmad, R., & Kim, D. H. (2021). An ensemble energy consumption forecasting model based on spatial-temporal clustering analysis in residential buildings. Energies, 14(11), 1–25. https://doi.org/10.3390/en14113020
Khan, P. W., Kim, Y., Byun, Y. C., & Lee, S. J. (2021). Influencing factors evaluation of machine learning-based energy consumption prediction. Energies, 14(21). https://doi.org/10.3390/en14217167
Khan, S., Aslam, S., Mustafa, I., & Aslam, S. (2021). Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine. Forecasting, 3(3), 460–477. https://doi.org/10.3390/forecast3030028
Khan, Z. A., Ullah, A., Ullah, W., Rho, S., Lee, M., & Baik, S. W. (2020). Electrical energy prediction in residential buildings for short-term horizons using hybrid deep learning strategy. Applied Sciences (Switzerland), 10(23), 1–12. https://doi.org/10.3390/app10238634
Ko, K., Lee, T., & Jeong, S. (2021). A deep learning method for monitoring vehicle energy consumption with gps data. Sustainability (Switzerland), 13(20), 1–15. https://doi.org/10.3390/su132011331
Lee, D., Jeong, J. W., & Choi, G. (2021). Short term prediction of pv power output generation using hierarchical probabilistic model. Energies, 14(10), 1–15. https://doi.org/10.3390/en14102822
Liang, F., Yu, A., Hatcher, W. G., Yu, W., & Lu, C. (2018). Deep Learning-Based Power Usage Forecast Modeling and Evaluation. Procedia Computer Science, 154, 102–108. https://doi.org/10.1016/j.procs.2019.06.016
Mahdavifar, S., & Ghorbani, A. A. (2019). Application of deep learning to cybersecurity: A survey. Neurocomputing, 347, 149–176. https://doi.org/10.1016/j.neucom.2019.02.056
Makinde, K. A., Adewuyi, O. B., Amole, A. O., & Adeaga, O. A. (2021). Design of Grid-connected and Stand-alone Photovoltaic Systems for Residential Energy Usage: A Technical Analysis. Journal of Energy Research and Reviews, 8(1), 34–50. https://doi.org/10.9734/jenrr/2021/v8i130203
Manowska, A. (2020). Using the lstm network to forecast the demand for electricity in poland. Applied Sciences (Switzerland), 10(23), 1–16. https://doi.org/10.3390/app10238455
Masood, Z., Gantassi, R., Ardiansyah, & Choi, Y. (2022). A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting. Energies, 15(7). https://doi.org/10.3390/en15072623
Melanthota, S. K., Gopal, D., Chakrabarti, S., Kashyap, A. A., Radhakrishnan, R., & Mazumder, N. (2022). Deep learning-based image processing in optical microscopy. Biophysical Reviews, 14(2), 463–481. https://doi.org/10.1007/s12551-022-00949-3
Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, 6, 91–99. https://doi.org/10.1016/j.segan.2016.02.005
Mohammad, F., Ahmed, M. A., & Kim, Y. C. (2021). Efficient energy management based on convolutional long short‐term memory network for smart power distribution system. Energies, 14(19). https://doi.org/10.3390/en14196161
Moradzadeh, A., Moayyed, H., Zakeri, S., Mohammadi-Ivatloo, B., & Aguiar, A. P. (2021). Deep learning-assisted short-term load forecasting forsustainable management of energy in microgrid. Inventions, 6(1), 1–11. https://doi.org/10.3390/inventions6010015
Musumeci, G. (2022). Effects of COVID-19 Syndemic on Sport Community. J. Funct. Morphol. Kinesiol. 2022, 7, 19. https://doi.org/10.3390/jfmk7010019
Nour, M., Cömert, Z., & Polat, K. (2020). A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization. Applied Soft Computing Journal, xxxx, 106580. https://doi.org/10.1016/j.asoc.2020.106580
Ojosu, A., & Akolo, A. (2020). COVID-19 AND NIGERIA’s POWER SECTOR Afolabi Ojosu 1 and Alexander Akolo 2. II, 1–21.
Ozdemir, T., Taher, F., Ayinde, B. O., Zurada, J. M., & Ozmen, O. T. (2022). Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction. Applied Sciences (Switzerland), 12(9). https://doi.org/10.3390/app12094463
Pereira, R. M., Bertolini, D., Teixeira, L. O., Silla, C. N., & Costa, Y. M. G. (2020). COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine, 194. https://doi.org/10.1016/j.cmpb.2020.105532
Sehovac, L., Nesen, C., & Grolinger, K. (2019). Forecasting building energy consumption with deep learning: A sequence to sequence approach. Proceedings - 2019 IEEE International Congress on Internet of Things, ICIOT 2019 - Part of the 2019 IEEE World Congress on Services, 108–116. https://doi.org/10.1109/ICIOT.2019.00029
Selim, M., Zhou, R., Feng, W., & Quinsey, P. (2021). Estimating energy forecasting uncertainty for reliable ai autonomous smart grid design. Energies, 14(1), 1–15. https://doi.org/10.3390/en14010247
Shah, A. S., Nasir, H., Fayaz, M., Lajis, A., Ullah, I., & Shah, A. (2020). Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm. IEEE Access, 8, 204744–204762. https://doi.org/10.1109/ACCESS.2020.3037081
Shibly, K. H., Dey, S. K., Islam, M. T. U., & Rahman, M. M. (2020). COVID faster R–CNN: A novel framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray images. Informatics in Medicine Unlocked, 20, 100405. https://doi.org/10.1016/j.imu.2020.100405
Shirzadi, N., Nizami, A., Khazen, M., & Nik-Bakht, M. (2021). Medium-term regional electricity load forecasting through machine learning and deep learning. Designs, 5(2). https://doi.org/10.3390/designs5020027
Slart, R. H. J. A., Williams, M. C., Juarez-Orozco, L. E., Rischpler, C., Dweck, M. R., Glaudemans, A. W. J. M., Gimelli, A., Georgoulias, P., Gheysens, O., Gaemperli, O., Habib, G., Hustinx, R., Cosyns, B., Verberne, H. J., Hyafil, F., Erba, P. A., Lubberink, M., Slomka, P., Išgum, I., … Saraste, A. (2021). Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT. European Journal of Nuclear Medicine and Molecular Imaging, 48(5), 1399–1413. https://doi.org/10.1007/s00259-021-05341-z
Soava, G., Mehedintu, A., Sterpu, M., & Grecu, E. (2021). The impact of the COVID-19 pandemic on electricity consumption and economic growth in romania. Energies, 14(9). https://doi.org/10.3390/en14092394
Ullah, F. U. M., Khan, N., Hussain, T., Lee, M. Y., & Baik, S. W. (2021). Diving deep into short‐term electricity load forecasting: Comparative analysis and a novel framework. Mathematics, 9(6). https://doi.org/10.3390/math9060611
Ungureanu, S., Topa, V., & Cziker, A. C. (2021). Deep learning for short-term load forecasting—industrial consumer case study. Applied Sciences (Switzerland), 11(21). https://doi.org/10.3390/app112110126
Vellingiri, M. T., Mehedi, I. M., & Palaniswamy, T. (2022). A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles. Mathematics, 10(2). https://doi.org/10.3390/math10020260
Wen, L., Zhou, K., Yang, S., & Lu, X. (2019). Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting. Energy, 171, 1053–1065. https://doi.org/10.1016/j.energy.2019.01.075
Wlodarczak, P. (2019). Learning Techniques. Machine Learning and Its Applications, 149–160. https://doi.org/10.1201/9780429448782-9
Zhou, C., Fang, Z., Xu, X., Zhang, X., Ding, Y., Jiang, X., & ji, Y. (2020). Using long short-term memory networks to predict energy consumption of air-conditioning systems. Sustainable Cities and Society, 55(May 2019), 102000. https://doi.org/10.1016/j.scs.2019.102000
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Nigerian Journal of Technological Development
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
In accordance with the Copyright Act of 1976, which became effective January 1, 1978, the following statement signed by each author must accompany the manuscript submitted: "I, the undersigned author, transfer all copyright ownership of the manuscript referenced above to the Nigerian Journal of Technological Development, in the event the work is published. I warrant that the article is original, does not infringe upon any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been published previously. I have reviewed and approve the submitted version of the manuscript and agree to its publication in the Nigerian Journal of Technological Development." A copyright transfer form should be downloaded from the NJTD Website ( http://njtd.com.ng/index.php/njtd). Author(s) will be consulted, whenever possible, regarding republication of material. All authors must have access to the data presented and the authors and sponsor (if applicable) must agree to share original data with the editor if requested.
