Application of Contemporary Artificial Intelligence Algorithms in Real Energy Consumption Estimation in Residences
Öz
The acceleration of industrialisation has resulted in a corresponding increase in the demand for energy supplies, driven by the growing use of new generation electronic equipment in residential settings. The advent of renewable energy, or green energy, has prompted a shift away from traditional methods of energy production, such as the use of natural gas and fossil fuels. However, this transition has given rise to a number of challenges. It is of great importance to monitor data in order to integrate the obtained electricity into the system and to monitor it. The acquisition of energy data on a large scale is made possible by the implementation of dynamic relay communication within micro and macro scale smart grids, which have been specifically designed for this purpose. Deep learning algorithms and machine learning methods are employed for the processing and analysis of data obtained in the context of the Internet of Things (IoT). The implementation of these methods enables smart grids to operate with reduced loss and enhanced efficiency. The estimation of energy consumption at the smallest scale facilitates the implementation of optimised energy management strategies. By ensuring the flow of electricity in the optimal amount (power), the potential for waste can be mitigated. The rapid and sophisticated responses of machine and deep learning algorithms facilitate more structured and sustainable energy management for both users and producers. In this study, four years' worth of electrical energy data from residential sources was analysed using techniques such as Convolutional Neural Network, Long Short-Term Memory, Random Forest and K-Nearest Neighbours Regression. The resulting analyses enabled the estimation of energy consumption. To assess the efficacy of learning algorithms in the study across varying training and test data ratios, the dataset was partitioned using three distinct division methods: hold-out (90% training - 10% testing), hold-out (80% training - 20% testing), and a 67% training - 33% testing split. Additionally, a 10-fold cross-validation approach was employed for further evaluation. Comparative analysis revealed that the LSTM model emerged as the top-performing model, boasting the lowest MSE value of 0.0054 for daily forecasts.
Anahtar Kelimeler
Smart systems, Deep learning, Machine learning
Etik Beyan
This article is derived from the master’s thesis entitled “Comparison of deep learning and machine learning methods for estimating energy consumption in houses” that was completed under the supervision of Prof. Dr. Aysel ERSOY (Master’s Thesis, Istanbul University-Cerrahpaşa, Istanbul, Türkiye, 2020). The data and insights presented herein are based on the findings of the aforementioned thesis, with permission from the author. This work aims to build upon and expand the research conducted in the original thesis, contributing to the ongoing discourse in the field.
Teşekkür
The authors extend their heartfelt gratitude to Erol YAVUZ for his invaluable technical support, which significantly contributed to the efficient and expeditious execution of simultaneous analyses in the software employed during the application of artificial intelligence methods. His expertise and assistance were instrumental in the successful completion of this research endeavor.
Kaynakça
- S. Fathi, R. Srinivasan, A. Fenner, S. Fathi, Machine learning applications in urban building energy performance forecasting: A systematic review, Renewable and Sustainable Energy Reviews. 133 (2020), 110287. doi:10.1016/J.RSER.2020.110287
- S. Yang, M.P. Wan, W. Chen, B.F. Ng, S. Dubey, Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization, Applied Energy. 271 (2020), 115147. doi:10.1016/J.APENERGY.2020.115147
- N.T. Mbungu, R.M. Naidoo, R.C. Bansal, M.W. Siti, D.H. Tungadio, An overview of renewable energy resources and grid integration for commercial building applications, Journal of Energy Storage. 29 (2020), 101385. doi:10.1016/J.EST.2020.101385
- A. Rejeb, K. Rejeb, S. Simske, H. Treiblmaier, S. Zailani, The big picture on the internet of things and the smart city: a review of what we know and what we need to know, Internet of Things (Netherlands). 19 (2022), 100565. doi:10.1016/J.IOT.2022.100565
- A.H. Al-Badi, R. Ahshan, N. Hosseinzadeh, R. Ghorbani, E. Hossain, Survey of smart grid concepts and technological demonstrations worldwide emphasizing on the Oman perspective, Applied System Innovation. 3 (2020) 1–27. doi:10.3390/ASI3010005
- M. Hacıbeyoglu, M. Çelik, Ö. Erdaş Çiçek, Energy Efficiency Estimation in Buildings with K Nearest Neighbor Algorithm, Necmettin Erbakan University Journal of Science and Engineering. 5(2) (2023), 65–74. doi:10.47112/neufmbd.2023.10
- Y. Liu, H. Chen, L. Zhang, X. Wu, X. jia Wang, Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: A case study in China, Journal of Cleaner Production. 272 (2020), 122542. doi:10.1016/J.JCLEPRO.2020.122542
- E. Mocanu, P.H. Nguyen, M. Gibescu, W.L. Kling, Deep learning for estimating building energy consumption, Sustainable Energy, Grids and Networks. 6 (2016), 91–99. doi:10.1016/J.SEGAN.2016.02.005
- T.Y. Kim, S.B. Cho, Predicting residential energy consumption using CNN-LSTM neural networks, Energy. 182 (2019), 72–81. doi:10.1016/J.ENERGY.2019.05.230
- H. Georges and B. Alice. Individual household electric power consumption, UCI Machine Learning Repository. 2012. https://doi.org/10.24432/C58K54