Document Type : Original Article
Department of Computer Engineering, Ayandegan Institute of Higher Education, Tonekabon, Iran.
As the construction sector accounts for the highest energy consumption worldwide, new solutions must be offered in buildings through the adoption of energy-efficient techniques. The main factors involved in energy consumption and residents' behaviors patterns considering environmentally-friendly lifestyle changes must be clearly identified and modeled to provide such solutions. One of the most important topics in smart grids is managing energy consumption in buildings, and one way to optimize energy consumption by analyzing building energy data is to use personalized recommender systems. The Non-Intrusive Load Monitoring (NILM) technique is an important way to cost-effective real-time monitoring the energy consumption and time of use for each appliance. However, the combination of recommender systems and NILM has received less attention. In this paper, a personalized NILM-based recommender system is proposed, which has three main phases: DAE-based NILM, TF-IDF-based text classification, and personalized recommender system. The proposed approach is investigated using the Reference Energy Disaggregation Dataset (REDD). According to the results, the accuracy of the proposed framework is about 60%.
- Cominola, A., Giuliani, M., Piga, D., Castelletti, A., & Rizzoli, A. E. (2017). A hybrid signature-based iterative disaggregation algorithm for non-intrusive load monitoring. Applied energy, 185, 331-344.
- Hart, G. W. (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12), 1870-1891.
- Piccialli, V., & Sudoso, A. M. (2021). Improving non-intrusive load disaggregation through an attention-based deep neural network. Energies, 14(4), 847.
- Yoshimoto, K., Nakano, Y., Amano, Y., & Kermanshahi, B. (2000). Non-intrusive appliances load monitoring system using neural networks. Growth, 2, 2-5.
- Kolter, J. Z. N. A., Batra, S., & Ng, A. (2010). Energy disaggregation via discriminative sparse coding. Advances in neural information processing systems, 23, 1153-1161.
- Yang, H. T., Chang, H. H., & Lin, C. L. (2007, April). Design a neural network for features selection in non-intrusive monitoring of industrial electrical loads. In 2007 11th International Conference on Computer Supported Cooperative Work in Design (pp. 1022-1027). IEEE.
- Lin, Y. H., & Tsai, M. S. (2010, May). A novel feature extraction method for the development of nonintrusive load monitoring system based on BP-ANN. In 2010 International Symposium on Computer, Communication, Control and Automation (3CA) (Vol. 2, pp. 215-218). IEEE.
- Kelly, J., & Knottenbelt, W. (2015, November). Neural nilm: Deep neural networks applied to energy disaggregation. In Proceedings of the 2nd ACM international conference on embedded systems for energy-efficient built environments (pp. 55-64).
- 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.
- Mocanu, D. C., Mocanu, E., Nguyen, P. H., Gibescu, M., & Liotta, A. (2016, October). Big IoT data mining for real-time energy disaggregation in buildings. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 003765-003769). IEEE.
- Zhang, C., Zhong, M., Wang, Z., Goddard, N., & Sutton, C. (2018, April). Sequence-to-point learning with neural networks for non-intrusive load monitoring. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
- Deshpande, R., Hire, S., & Mohammed, Z. A. (2022). Smart Energy Management System Using Non-intrusive Load Monitoring. SN Computer Science, 3(2), 1-11.
- Kim, H., Marwah, M., Arlitt, M., Lyon, G., & Han, J. (2011, April). Unsupervised disaggregation of low frequency power measurements. In Proceedings of the 2011 SIAM international conference on data mining (pp. 747-758). Society for Industrial and Applied Mathematics.
- Egarter, D., & Elmenreich, W. (2013, July). Evonilm: Evolutionary appliance detection for miscellaneous household appliances. In Proceedings of the 15th annual conference companion on Genetic and evolutionary computation (pp. 1537-1544).
- Trung, K. N., Dekneuvel, E., Nicolle, B., Zammit, O., Van, C. N., & Jacquemod, G. (2014, June). Event detection and disaggregation algorithms for nialm system. In Proceedings of 2nd International Non-Intrusive Load Monitoring (NILM) Workshop.
- Hassan, T. (2012). Bi-level characterization of manual setup residential non-intrusive demand disaggregation using enhanced differential evolution. In 1st Int. Workshop Non-Intrusive Load Monitoring.