Enhancing Electricity Demand Forecasting Accuracy Through Hybrid Models and Deep Learning Techniques: A Systematic Literature Review

Abstract: This reviewed literature on electricity forecasting covers its history, terminology, and techniques. A systematic review of existing studies highlighted key findings and future research opportunities. Conventional statistical techniques and MLA can predict electricity demand over time with various techniques and forecasting windows tailored to data and problem specifics. Most studies focused on STLF, often without testing techniques on MTLF and LTLF. The key findings include: Many studies (26%) used conventional statistical methods like ARIMA, ARIMAX, and SARIMAX for electricity forecasting, often without benchmarking algorithms. Various factors, such as time, weather, electricity price, population, and economy, influence ELF. Weather parameters were the most commonly used predictors, though performance varied across studies. A global increase in electricity demand has driven numerous studies, though less research has been done in low- and middle-income countries. Deep neural networks like LSTM have been underutilised in electricity forecasting. LSTM's ability to store memory and address the vanishing gradient problem makes it promising for future research, particularly in hybrid models combining CNN and LSTM for forecasting peak load demand based on economic and environmental factors.

Computer Science and Machine Learning

1. Aneiros, G., Vilar, J., & Raña, P. (2016). Short-term forecast of daily curves of electricity demand and price. International Journal of Electrical Power & Energy Systems, 80, 96–108. https://doi.org/10.1016/j.ijepes.2016.01.034 [Google Scholar] [Crossref]

2. Ardabili, S., Mosavi, A., & Várkonyi-Kóczy, A. R. (2020). Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research. In Melting Threshold and Thermal Conductivity of CdTe Under Pulsed Laser Irradiation (Vol. 101, pp. 29–42). Springer. https://doi.org/10.1007/978-3-030-36841-8_10 [Google Scholar] [Crossref]

3. Azad, M. K., Uddin, S., & Takruri, M. (2018). Support vector regression based electricity peak load forecasting. 2018 11th International Symposium on Mechatronics and Its Applications (ISMA), 2018-Janua, 1–5. https://doi.org/10.1109/ISMA.2018.8330143 [Google Scholar] [Crossref]

4. Bedi, J., & Toshniwal, D. (2019). Deep learning framework to forecast electricity demand. Applied Energy, 238(October 2018), 1312–1326. https://doi.org/10.1016/j.apenergy.2019.01.113 [Google Scholar] [Crossref]

5. Charytoniuk, W., Chen, M. S., & Van Olinda, P. (1998). Nonparametric regression based short-term load forecasting. IEEE Transactions on Power Systems, 13(3), 725–730. https://doi.org/10.1109/59.708572 [Google Scholar] [Crossref]

6. Dedinec, A., Filiposka, S., Dedinec, A., & Kocarev, L. (2016). Deep belief network based electricity load forecasting: An analysis of Macedonian case. Energy, 115, 1688–1700. https://doi.org/10.1016/j.energy.2016.07.090 [Google Scholar] [Crossref]

7. De Felice, M., Alessandri, A., & Catalano, F. (2015). Seasonal climate forecasts for medium-term electricity demand forecasting. Applied Energy, 137, 435–444. https://doi.org/10.1016/j.apenergy.2014.10.030 [Google Scholar] [Crossref]

8. de Oliveira, E. M., & Cyrino Oliveira, F. L. (2018). Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy, 144, 776–788. https://doi.org/10.1016/j.energy.2017.12.049 [Google Scholar] [Crossref]

9. Dewari, S. S., & Bhandari, V. (2015). Electric load forecasting based on locally weighted support vector regression. International Journal for Scientific Research & Development, 40(4), 2321–0613. [Google Scholar] [Crossref]

10. Divina, F., Gilson, A., Goméz-Vela, F., García Torres, M., & Torres, J. (2018). Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting. Energies, 11(4), 949. https://doi.org/10.3390/en11040949 [Google Scholar] [Crossref]

11. Eeeguide.com. (2014). Forecasting Methodology. http://www.eeeguide.com/forecasting-methodology/ [Google Scholar] [Crossref]

12. Fu, Y., Li, Z., Zhang, H., & Xu, P. (2015). Using Support Vector Machine to Predict Next Day Electricity Load of Public Buildings with Sub-metering Devices. Procedia Engineering, 121, 1016–1022. https://doi.org/10.1016/j.proeng.2015.09.097 [Google Scholar] [Crossref]

13. Ganguly, A., Goswami, K., & Kumar Sil, A. (2020). WANN and ANN based Urban Load Forecasting for Peak Load Management. 2020 IEEE Calcutta Conference (CALCON), 402–406. https://doi.org/10.1109/CALCON49167.2020.9106520 [Google Scholar] [Crossref]

14. Hadjout, D., Torres, J. F., Troncoso, A., Sebaa, A., & Martínez-Álvarez, F. (2021). Electricity consumption forecasting based on ensemble deep learning with application to the algerian market. Energy, 123060. https://doi.org/10.1016/j.energy.2021.123060 [Google Scholar] [Crossref]

15. Haq, M. R., & Ni, Z. (2019). A New Hybrid Model for Short-Term Electricity Load Forecasting. IEEE Access, 7, 125413–125423. https://doi.org/10.1109/ACCESS.2019.2937222 [Google Scholar] [Crossref]

16. Hassan, S., Khosravi, A., Jaafar, J., & Raza, M. Q. (2014). Electricity load and price forecasting with influential factors in a deregulated power industry. 2014 9th International Conference on System of Systems Engineering (SOSE), 79–84. https://doi.org/10.1109/SYSOSE.2014.6892467 [Google Scholar] [Crossref]

17. Jarndal, A., & Hamdan, S. (2017). Forecasting of peak electricity demand using ANNGA and ANN-PSO approaches. 2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), 1–5. https://doi.org/10.1109/ICMSAO.2017.7934842 [Google Scholar] [Crossref]

18. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147(July 2017), 70–90. https://doi.org/10.1016/j.compag.2018.02.016 [Google Scholar] [Crossref]

19. Keele, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. In EBSE Technical Report EBSE-2007-01: Vol. 2.3 (Issue 5). [Google Scholar] [Crossref]

20. Kumi, E. N. (2017). The Electricity Situation in Ghana: Challenges and Opportunities. Center for Global Development, September. www.cgdev.org [Google Scholar] [Crossref]

21. Kuster, C., Rezgui, Y., & Mourshed, M. (2017). Electrical load forecasting models: A critical systematic review. Sustainable Cities and Society, 35, 257–270. https://doi.org/10.1016/j.scs.2017.08.009 [Google Scholar] [Crossref]

22. Liu, Z. (2015). Global Energy Development: The Reality and Challenges. In Global Energy Interconnection (pp. 1–64). Elsevier. https://doi.org/10.1016/B978-0-12-804405-6.00001-4 [Google Scholar] [Crossref]

23. Mengying, H., Jiandong, D., Zequan, H., Peng, W., Shuai, F., Peijia, H., & Chaoyuan, F. (2019). Monthly Electricity Forecast Based on Electricity Consumption Characteristics Analysis and Multiple Effect Factors. 2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP), 1814–1818. https://doi.org/10.1109/APAP47170.2019.9224784 [Google Scholar] [Crossref]

24. Mosavi, A., Salimi, M., Faizollahzadeh Ardabili, S., Rabczuk, T., Shamshirband, S., & Varkonyi-Koczy, A. (2019). State of the Art of Machine Learning Models in Energy Systems, a Systematic Review. Energies, 12(7), 1301. https://doi.org/10.3390/en12071301 [Google Scholar] [Crossref]

25. Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2019a). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 53(4), 3007–3057. https://doi.org/10.1007/s10462-019-09754-z [Google Scholar] [Crossref]

26. Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2019b). Random Forest Based Feature Selection of Macroeconomic Variables for Stock Market Prediction. American Journal of Applied Sciences, 16(7), 200–212. https://doi.org/10.3844/ajassp.2019.200.212 [Google Scholar] [Crossref]

27. Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2021). A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction. Journal of Big Data, 8(1), 17. https://doi.org/10.1186/s40537-020-00400-y [Google Scholar] [Crossref]

28. Pannakkong, W., Sriboonchitta, S., & Huynh, V.-N. (2018). An Ensemble Model of Arima and Ann with Restricted Boltzmann Machine Based on Decomposition of Discrete Wavelet Transform for Time Series Forecasting. Journal of Systems Science and Systems Engineering, 27(5), 690–708. https://doi.org/10.1007/s11518-018-5390-8 [Google Scholar] [Crossref]

29. Pereira, C. M., Almeida, N. N. de, & Velloso, M. L. F. (2015). Fuzzy Modeling to Forecast an Electric Load Time Series. Procedia Computer Science, 55(Itqm), 395–404. https://doi.org/10.1016/j.procs.2015.07.089 [Google Scholar] [Crossref]

30. Rusli, R., Hidayanto, A. N., & Ruldeviyani, Y. (2019). Consumption Prediction on Steam Power Plant Using Data Mining Hybrid Particle Swarm Optimization (PSO) and Auto Regressive Integrated Moving Average (ARIMA). 2019 International Workshop on Big Data and Information Security (IWBIS), 15–20. https://doi.org/10.1109/IWBIS.2019.8935844 [Google Scholar] [Crossref]

31. Ruzic, S., Vuckovic, A., & Nikolic, N. (2003). Weather sensitive method for short term load forecasting in electric power utility of serbia. IEEE Transactions on Power Systems, 18(4), 1581–1586. https://doi.org/10.1109/TPWRS.2003.811172 [Google Scholar] [Crossref]

32. Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers and Operations Research, 119, 104926. https://doi.org/10.1016/j.cor.2020.104926 [Google Scholar] [Crossref]

33. Simeone, O. (2018). A Very Brief Introduction to Machine Learning with Applications to Communication Systems. IEEE Transactions on Cognitive Communications and Networking, 4(4), 648–664. https://doi.org/10.1109/TCCN.2018.2881442 [Google Scholar] [Crossref]

34. Stanisavljevic, D., & Spitzer, M. (2016). A Review of Related Work on Machine Learning in Semiconductor Manufacturing and Assembly Lines. August 2018. [Google Scholar] [Crossref]

35. Yang, A., Li, W., & Yang, X. (2019). Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines. Knowledge-Based Systems, 163, 159–173. https://doi.org/10.1016/j.knosys.2018.08.027 [Google Scholar] [Crossref]

36. Yildiz, B., Bilbao, J. I., & Sproul, A. B. (2017). A review and analysis of regression and machine learning models on commercial building electricity load forecasting. Renewable and Sustainable Energy Reviews, 73(December 2016), 1104–1122. https://doi.org/10.1016/j.rser.2017.02.023 [Google Scholar] [Crossref]

37. Zivanovic, R. (2002). Nonparametric trend model for short term electricity demand forecasting. Fifth International Conference on Power System Management and Control, 2002, 347–352. https://doi.org/10.1049/cp:20020060 [Google Scholar] [Crossref]

• Exploration of Organizational Culture on Employee Well-Being: A Survey-Based Study on Workplace Culture Dimensions
• Peecheck 2.0: Design and Improvement of Rapid and Low-Cost Urine Analysis Device for Rural Health Care
• Energy Mixed More Fuels with Lower Carbon Contain and Renewable Energy Reduce Carbon Dioxide Emissions: A Review
• Sustainable Economic Growth Through Artificial Intelligence -Driven Tax Frameworks Nexus on Enhancing Business Efficiency and Prosperity; An Appraisal
• Federalism and National Integration in Nigeria: A Study of Selected States in South-South, Nigeria