Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 8 , Issue 2 , PP: 01-09, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Nima Khodadadi 1 * , Benyamin Abdollahzadeh 2
Doi: https://doi.org/10.54216/JAIM.080201
This paper proposes to evaluate how different machine learning techniques can be used to predict daytime power generation based on the "Daily Power Generation Data" data set. As a result of six models, which contain Random Forest Regressor, Decision Tree Regressor, Nearest Neighbors, Linear Regression, MLP Regressor, and SVR, a clear understanding has been accomplished by assessing the performance using multiple metrics. First, the Random Forest Regressor turned out to be the best in terms of the Mean Squared Error (MSE) of 3.57E-06, which was the lowest among the three ML models. The introduction of the paper highlights the role of precise planning of the power market and the consecutive sections describing the topic mathematically. The table below, with a total list of performance issues, explains why the Random Forest Regressor is the superior full-proof model using the lowest MSE, highest explained variance, and great resistance to outlying samples. The paper thus gave various useful approval criteria that we can largely choose the best model out of them because the Random Forest Regressor was able to get the highest performance metrics.
Power Generation , Daily Power Generation , Machine Learning , Random Forest Regressor
[1] Almetwally, E. M., & Meraou, M. A. (2022). Application of environmental data with new extension of Nadarajah-Haghighi distribution. Computational Journal of Mathematical and Statistical Sciences, 1(1), 26–41. https://doi.org/10.21608/cjmss.2022.271186
[2] Muhammed, H. Z., & Almetwally, E. M. (2024). Bayesian and non-Bayesian estimation for the shape parameters of new versions of bivariate inverse Weibull distribution based on progressive type II censoring. Computational Journal of Mathematical and Statistical Sciences, 3(1), 85–111. https://doi.org/10.21608/cjmss.2023.250678.1028
[3] Towfek, S. K. (2023). Navigating the storm: Cutting-edge risk mitigation and analysis for volatile markets. Journal of Artificial Intelligence and Metaheuristics, 4(2), 36–44. https://doi.org/10.54216/JAIM.040204
[4] Towfek, S. K. (2023). A semantic approach for extracting the medical association rules. Journal of Artificial Intelligence and Metaheuristics, 5(1), 46–52. https://doi.org/10.54216/JAIM.050105
[5] Alotaibi, R., AL-Dayian, G. R., Almetwally, E. M., & Rezk, H. (2024). Bayesian and non-Bayesian two-sample prediction for the Fréchet distribution under progressive type II censoring. AIP Advances, 14(1), 015137. https://doi.org/10.1063/5.0174390
[6] El-Kenawy, E.-S. M., Khodadadi, N., Mirjalili, S., Abdelhamid, A. A., & Eid, M. M. et al. (2024). Greylag goose optimization: Nature-inspired optimization algorithm. Expert Systems with Applications, 238, 122147. https://doi.org/10.1016/j.eswa.2023.122147
[7] El-Kenawy, E.-S., Ibrahim, A., Mirjalili, S., Zhang, Y.-D., & Elnazer, S. et al. (2022). Optimized ensemble algorithm for predicting metamaterial antenna parameters. Computers, Materials & Continua, 71(3), 4989–5003. https://doi.org/10.32604/cmc.2022.023884
[8] Khafaga, D. S., Ibrahim, A., El-Kenawy, E.-S. M., Abdelhamid, A. A., & Karim, F. K. et al. (2022). An Al-Biruni earth radius optimization-based deep convolutional neural network for classifying monkeypox disease. Diagnostics, 12(11), Article 11. https://doi.org/10.3390/diagnostics12112892
[9] Djaafari, A., Ibrahim, A., Bailek, N., Bouchouicha, K., & Hassan, M. A. et al. (2022). Hourly predictions of direct normal irradiation using an innovative hybrid LSTM model for concentrating solar power projects in hyper-arid regions. Energy Reports, 8, 15548–15562. https://doi.org/10.1016/j.egyr.2022.10.402
[10] AlEisa, H., El-Kenawy, E.-S., Alhussan, A., Saber, M., & Abdelhamid, A. et al. (2022). Transfer learning for chest X-rays diagnosis using dipper throated algorithm. Computers, Materials & Continua, 73(2), 2371–2387. https://doi.org/10.32604/cmc.2022.030447
[11] El-Kenawy, E.-S. M., Khodadadi, N., Mirjalili, S., Makarovskikh, T., & Abotaleb, M. et al. (2022). Metaheuristic optimization for improving weed detection in wheat images captured by drones. Mathematics, 10(23), Article 23. https://doi.org/10.3390/math10234421
[12] Abdelhamid, A. A., Towfek, S. K., Khodadadi, N., Alhussan, A. A., & Khafaga, D. S. et al. (2023). Waterwheel plant algorithm: A novel metaheuristic optimization method. Processes, 11(5), Article 5. https://doi.org/10.3390/pr11051502
[13] Khafaga, D., El-Kenawy, E.-S., Karim, F., Alshetewi, S., & Ibrahim, A. et al. (2022). Optimized weighted ensemble using dipper throated optimization algorithm in metamaterial antenna. Computers, Materials & Continua, 73(3), 5771–5788. https://doi.org/10.32604/cmc.2022.032229
[14] Bhavsar, S., Pitchumani, R., & Ortega-Vazquez, M. A. (2021). Machine learning enabled reduced-order scenario generation for stochastic analysis of solar power forecasts. Applied Energy, 293, 116964. https://doi.org/10.1016/j.apenergy.2021.116964
[15] Ding, S., Zhang, H., Tao, Z., & Li, R. (2022). Integrating data decomposition and machine learning methods: An empirical proposition and analysis for renewable energy generation forecasting. Expert Systems with Applications, 204, 117635. https://doi.org/10.1016/j.eswa.2022.117635
[16] Mukherjee, D., Chakraborty, S., & Ghosh, S. (2022). Power system state forecasting using machine learning techniques. Electrical Engineering, 104(1), 283–305. https://doi.org/10.1007/s00202-021-01328-z
[17] Daily power generation data. (2024). Kaggle. Retrieved March 13, 2024, from https://www.kaggle.com/datasets/arvindnagaonkar/power-generation-data
[18] Alam, S., & Yao, N. (2019). The impact of preprocessing steps on the accuracy of machine learning algorithms in sentiment analysis. Computational and Mathematical Organization Theory, 25(3), 319–335. https://doi.org/10.1007/s10588-018-9266-8
[19] Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695. https://doi.org/10.1007/s12525-021-00475-2
[20] Xue, L., Liu, Y., Xiong, Y., Liu, Y., & Cui, X. et al. (2021). A data-driven shale gas production forecasting method based on the multi-objective random forest regression. Journal of Petroleum Science and Engineering, 196, 107801. https://doi.org/10.1016/j.petrol.2020.107801
