Volume 8 • Issue 2 • PP: 10-18 • 2024
Smart Home Energy Management through ARIMA Model Forecasting: Leveraging Weather Data for Improved Efficiency
View PDF Abstract
This study pursues machine learning models for the task of smart homes' energy management with the use of a dataset that combines smart meter readings and weather conditions at the same time. The assessment of the Baseline Qualification and ARIMA models is done using various criteria, such as MSE, RMSE, and others. Most telling, the best performance is shown by ARIMA, which gets the lowest MSE score, 0.0693, in this instance. They show that such a model is optimal in forecasting energy consumption dynamics, and while they could be better, weather information helps improve the accuracy of the forecasts. The conduct helps uncover priceless information, allowing for the development of new smart home operating systems with a prospect of energy efficiency enhancement as well as a sustainable environment.
Keywords
References
[1] Litvinenko, V. S. (2020). Digital economy as a factor in the technological development of the mineral sector. Natural Resources Research, 29(3), 1521–1541. https://doi.org/10.1007/s11053-019-09568-4
[2] Ameen, N., Hosany, S., & Tarhini, A. (2021). Consumer interaction with cutting-edge technologies: Implications for future research. Computers in Human Behavior, 120, 106761. https://doi.org/10.1016/j.chb.2021.106761
[3] 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
[4] 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
[5] 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
[6] 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
[7] 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
[8] 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
[9] 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
[10] 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
[11] 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
[12] 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
[13] 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
[14] 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
[15] 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
[16] Bianchi, V., Bassoli, M., Lombardo, G., Fornacciari, P., & Mordonini, M., et al. (2019). IoT wearable sensor and deep learning: An integrated approach for personalized human activity recognition in a smart home environment. IEEE Internet of Things Journal, 6(5), 8553–8562. https://doi.org/10.1109/JIOT.2019.2920283
[17] Qolomany, B., Al-Fuqaha, A., Gupta, A., Benhaddou, D., & Alwajidi, S., et al. (2019). Leveraging machine learning and big data for smart buildings: A comprehensive survey. IEEE Access, 7, 90316–90356. https://doi.org/10.1109/ACCESS.2019.2926642
[18] Sepasgozar, S., Karimi, R., Farahzadi, L., Moezzi, F., & Shirowzhan, S., et al. (2020). A systematic content review of artificial intelligence and the Internet of Things applications in smart home. Applied Sciences, 10(9), Article 9. https://doi.org/10.3390/app10093074
[19] Kaggle. (2024). Smart home dataset with weather information. Retrieved March 13, 2024, from https://www.kaggle.com/datasets/taranvee/smart-home-dataset-with-weather-information
[20] Paulson, N. H., Kubal, J., Ward, L., Saxena, S., & Lu, W., et al. (2022). Feature engineering for machine learning enabled early prediction of battery lifetime. Journal of Power Sources, 527, 231127. https://doi.org/10.1016/j.jpowsour.2022.231127
[21] 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
Cite This Article
Choose your preferred format