Elektrikli Ev Aletlerinde Müdahalesiz Yük İzleme, Sınıflandırma ve Kontrolünün Gerçekleştirilmesi

Yıl 2023, Cilt: 11 Sayı: 4, 1209 - 1222, 28.12.2023

Fethi Batıncan Gürbüz , Ramazan Bayındır , Seyfettin Vadi

https://doi.org/10.29109/gujsc.1360516

Öz

Son yıllarda giderek büyüyen enerji ihtiyacı, araştırmacıları enerji yönetimi ve akıllı şebekeler gibi alanlara yöneltmiştir. Özellikle, şebekeye bağlı yüklerin analizleri ve izlenmesi gün geçtikçe daha da önem kazanmıştır. Bu ihtiyaç, müdahalesiz yük izleme (MYİ) yönteminin ortaya çıkmasına yol açmıştır. MYİ, elektrikli cihazların şebeke üzerinden izlenmesini ve tespit edilmesini, özelliklerine göre kategorize edilmesini amaçlamaktadır. Bu sistemler, şebekeye bağlı cihazların tek bir noktadan izlenerek güç kullanımının takip edilebilmesine katkı sağlamaktadır. Bu çalışmada, deneysel ortamda toplanan verilerle müdahalesiz yük izleme yöntemine uygun yazılım ve donanımlar oluşturulmaktadır. Ayrıca, toplanan veri setleri üzerinde yapılan çalışmalarla bir hibrit algoritma önerilmektedir. Böylece, elde edilen verilerin doğruluğu ve algoritmanın etkinliği daha iyi anlaşılmaktadır. Çalışma kapsamında geliştirilen cihaz kontrol üniteleri, yüklerin belirli senaryolarda otomatik olarak etkinleştirilmesi veya devre dışı bırakılmasını sağlayarak, yeni bir perspektif sunmaktadır. Bu sayede, enerji yönetimine daha esnek ve etkili bir yaklaşım sunulmaktadır. Çalışma, enerji yönetim sistemleri ve akıllı şebekelerin geliştirilmesine katkıda bulunmayı amaçlamaktadır. Enerji ihtiyacının artmasıyla ortaya çıkan zorluklara çözümler sunarak enerji verimliliğini artırmayı ve elektrik kayıplarını azaltmayı hedeflemektedir. Müdahalesiz yük izleme yöntemiyle elde edilen bulgular, enerji sektöründeki uygulamalara yönelik yeni çözümler sunmak için önemli bir adım olacaktır.

Anahtar Kelimeler

Müdahalesiz Yük İzleme, KNN, Akıllı Şebekelerin Olay Tespiti, SVM

Proje Numarası

07/2020-15

Kaynakça

• [1] Shi, K., Chen, Y., Yu, B., Xu, T., Yang, C., Li, L., Huang, C., Chen, Z., Liu, R., and Wu, J. (2016). Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data. Applied Energy, 184, 450–463.
• [2] Liu, H., Wu, H., and Yu, C. (2019). A hybrid model for appliance classification based on time series features. Energy and Buildings, 196, 112-123.
• [3] Hart, G. W. (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12), 1870–1891.
• [4] Gillis, J. M., Alshareef, S. M., & Morsi, W. G. (2016). Nonintrusive load monitoring using wavelet design and machine learning. IEEE Transactions on Smart Grid, 7(1), 320–328.
• [5] J. Wang, C. Pang, X. Zeng and Y. Chen, "Non-Intrusive Load Monitoring Based on Residual U-Net and Conditional Generation Adversarial Networks," in IEEE Access, vol. 11, pp. 77441-77451, 2023.
• [6] Du, Y., Du, L., Lu, B., Harley, R., and Habetler, T. (2010). A review of identification and monitoring methods for electric loads in commercial and residential buildings. IEEE Energy Conversion Congress and Exposition, 4527-4533, Atalanta, USA
• [7] A. F. M. Jaramillo et al., "Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques," in IEEE Access, vol. 11, pp. 19469-19486, 2023
• [8] M. Ghaffar, S. R. Sheikh, N. Naseer, S. A. Usama, B. Salah and S. A. K. Alkhatib, "Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering," in IEEE Access, vol. 11, pp. 53165-53175, 2023
• [9] Paradiso, F., Paganelli, F., Luchetta, A., Giuli, D., and Castrogiovanni, P. (2013). ANN-based appliance recognition from low-frequency energy monitoring data. IEEE 14th International Symposium on" A World of Wireless, Mobile and Multimedia Networks", 1-6, Madrid, Spain.
• [10] Wichakool, W., Avestruz, A. T., Cox, R. W., and Leeb, S. B. (2009). Modeling and estimating current harmonics of variable electronic loads. IEEE Transactions on power electronics, 24(12), 2803-2811.
• [11] Dong, M., Meira, P. C., Xu, W., and Freitas, W. (2012). An event window-based load monitoring technique for smart meters. IEEE transactions on smart grid, 3(2), 787-796.
• [12] Roos, J. G., Lane, I. E., Botha, E. C., and Hancke, G. P. (1994). Using neural networks for non-intrusive monitoring of industrial electrical loads. IEEE Instrumentation and Measurement Technology Conference, 1115-1118, Hamamatsu, Japan.
• [13] Srinivasan, D., Ng, W. S., and Liew, A. C. (2006). Neural-network-based signature recognition for harmonic source identification. IEEE Transactions on Power Delivery, 21(1), 398–405.
• [14] Yang, H. T., Chang, H. H., and Lin, C. L. (2007). Design a neural network for features selection in non-intrusive monitoring of industrial electrical loads. 11th International Conference on Computer Supported Cooperative Work in Design, 1022-1027, Melbourne, Australia.
• [15] Makonin, S. W. (2014). Real-time embedded low frequency load disaggregation, (Doctoral Dissertation, Simon Fraser University, 2014), Dissertation Abstracts International, 40-55.
• [16] He, D. (2016). An advanced non-instrusive load monitoring technique and its application in smart grid building energy management systems, (Doctoral Dissertation, Georgia Institute of Technology University, 2016), Dissertation Abstracts International, 32-47.
• [17] Basu, K., Hably, A., Debusschere, V., Bacha, S., Driven, G. J., and Ovalle, A. (2016). A comparative study of low sampling non intrusive load disaggregation. IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, 5137-5142, Florence, Italy.
• [18] Larcher, D., and Tarascon, J. M. (2015). Towards greener and more sustainable batteries for electrical energy storage. Nature Chemistry, 7(1), 19–29.
• [19] Kelly, J., and Knottenbelt, W. (2015). The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Scientific Data, 2(1), 1-14.
• [20] Yang, C. C., Soh, C. S., and Yap, V. V. (2014). Comparative study of event detection methods for non-intrusive appliance load monitoring. Energy Procedia, 61, 1840-1843.
• [21] Leeb, S. B., Shaw, S. R., and Kirtley, J. L. (1995). Transient event detection in spectral envelope estimates for nonintrusive load monitoring. IEEE Transactions on Power Delivery, 10(3), 1200-1210.
• [22] Afzalan, M., Jazizadeh, F., and Wang, J. (2019). Self-configuring event detection in electricity monitoring for human-building interaction. Energy and Buildings, 187, 95-109.
• [23] Zoha, A., Gluhak, A., Imran, M. A., and Rajasegarar, S. (2012). Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors, 12(12), 16838-16866.
• [24] Norford, L. K., & Leeb, S. B. (1996). Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy and Buildings, 24(1), 51-64.
• [25] Tabatabaei, S. M., Dick, S., and Xu, W. (2016). Toward non-intrusive load monitoring via multi-label classification. IEEE Transactions on Smart Grid, 8(1), 26-40.
• [26] Gray, M., and Morsi, W. G. (2015). Application of wavelet-based classification in non-intrusive load monitoring. IEEE 28th Canadian conference on electrical and computer engineering (CCECE), 41-45, Halifax, Canada.
• [27] He, K., Stankovic, L., Liao, J., and Stankovic, V. (2018). Non-intrusive load disaggregation using graph signal processing. IEEE Transactions on Smart Grid, 9(3), 1739–1747.
• [28] De Baets, L., Ruyssinck, J., Develder, C., Dhaene, T., and Deschrijver, D. (2018). Appliance classification using VI trajectories and convolutional neural networks. Energy and Buildings, 158, 32-36.
• [29] Zheng, Z., Chen, H., and Luo, X. (2018). A supervised event-based non-intrusive load monitoring for non-linear appliances. Sustainability, 10(4), 1001.
• [30] Mathis, M., Rumsch, A., Kistler, R., Andrushevich, A., and Klapproth, A. (2014). Improving the recognition performance of NIALM algorithms through technical labeling. 12th IEEE international conference on embedded and ubiquitous computing, 227-233, Milan, Italy.
• [31] Shen, D., and Ip, H. H. (1999). Discriminative wavelet shape descriptors for recognition of 2-D patterns. Pattern Recognition, 32(2), 151-165.
• [32] Luo, S., Hu, Q., He, X., Li, J., Jin, J. S., and Park, M. (2009). Automatic liver parenchyma segmentation from abdominal CT images using support vector machines. ICME International Conference on Complex Medical Engineering, 1-5, Tempe, USA.
• [33] Debnath, R., and Takahashi, H. (2004). Kernel selection for the support vector machine. IEICE Transactions on Information and Systems, 87(12), 2903-2904.
• [34] S. Naderian, "A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks," arXiv preprint arXiv:2104.07809, 2021.