Creating Data Collection Device For Non-Intrusive Device Load Monitoring Method And Demand Side Management In Smart Grids

Yıl 2021, Cilt: 13 Sayı: 3, 215 - 229, 31.12.2021

Ramazan Güngüneş , Ertuğrul Çam , Volkan Ateş

https://doi.org/10.29137/umagd.1035908

Öz

In research on smart grids, it is seen that device load monitoring has a great effect on energy efficiency. The continuous increase in the demand for electrical energy, the fact that the use of electrical energy differs from household to household and the type of device used has increased the necessity of monitoring the amount of consumption.
It is an important issue to make arrangements that will provide financial benefits in consumption without changing the usage habits of the end user and to offer alternative solutions that will lead the consumer to conscious use. Among these alternative solutions, there may be suggestions such as detailed reporting of consumption amounts and directing them to different tariff transitions.
With the help of a data collection device with an optical port and a device connected to the existing electronic electricity meter, the instantaneous electricity consumption data of residential and industrial consumers are collected on a device called a central communication device. The collected data is stored both on a memory card on the device and on a remote server by being associated with the web server. In this way, a data set device was created for artificial intelligence-based decomposition methods to be performed with the non-invasive device load monitoring technique. At the same time, with an HMI screen developed in the study, it is ensured that the end consumer can monitor the amount of energy consumption instantly, and in this way, the ground has been prepared for a structure that will form the basis for consumption control and demand-side management systems with consumer participation.

Anahtar Kelimeler

Non-İntrusive Load Monitoring, Demand Side Management, Smart Meter, Smart Grid, IOT Devices

Akıllı Şebekelerde Müdahaleci Olmayan Cihaz Yükü İzleme Yöntemi Ve Talep Tarafı Yönetimi İçin Veri Toplama Cihazı Oluşturma

Öz

Akıllı şebekeler üzerine yapılan araştırmalarda cihaz yükü izlemenin enerji verimliliği üzerinde büyük ölçüde etkisinin olduğu görülmektedir. Elektrik enerjisine olan talebin sürekli artması, elektrik enerjisi kullanımının haneden haneye ve kullanılan cihaz türüne göre farklılık göstermesi tüketim miktarının izlenmesi gerekliliğini daha da arttırmıştır.
Son kullanıcının kullanım alışkanlıklarının değiştirilmeden, tüketim konusunda maddi faydalar sağlayacak düzenlemelerin yapılması ve tüketiciyi bilinçli kullanıma sevk edecek alternatif çözümler sunulması önemli bir konudur. Bu alternatif çözümler içerisinde tüketim miktarlarının ayrıntılı olarak raporlanması ve farklı tarife geçişlerine yönlendirmesi gibi önermeler bulunabilir.
Yapılan çalışmada üretilen optik portlu bir veri toplama cihazı ile mevcut elektronik elektrik sayacı üzerine bağlanan bir cihaz yardımı ile konut ve endüstri tüketicilerinin anlık elektrik tüketim verileri merkezi haberleşme cihazı adı verilen bir cihaz üzerinde toplanmaktadır. Toplanan veriler hem cihaz üzerinde bulunan bir hafıza kartında hem de web server ile ilişkilendirilerek bir uzak sunucuda depolanmaktadır. Bu sayede müdahalesiz cihaz yükü izleme tekniği ile gerçekleştirilecek yapay zeka temelli ayrıştırma yöntemleri için bir veri seti cihazı oluşturulmuştur. Aynı zamanda yapılan çalışmada geliştirilen bir HMI ekran ile son tüketicinin de enerji tüketim miktarını anlık olarak takip etmesi sağlanmış bu şekilde tüketim kontrolü ve tüketici katılımlı talep tarafı yönetimli sistemlere temel oluşturacak bir yapıya da zemin hazırlanmıştır.

Anahtar Kelimeler

Müdahalesiz Cihaz Yükü İzleme, Talep Tarafı Yönetimi, Akıllı Sayaçlar, Akıllı Şebeke, IOT Cihazlar

Kaynakça

• Aiad, M., & Lee, P. H. (2016). Unsupervised approach for load disaggregation with devices interactions. Energy and Buildings, 116, 96–103.
• Baloğlu, U. B. (2017). Akıllı Şebekelerde Hesapsal Yöntem Uygulamaları. 131.
• Bartlett, A. A. (1986). Sustained availability: A management program for nonrenewable resources. American Journal of Physics, 54(5), 398–402.
• Batra, N., Kelly, J., Parson, O., Dutta, H., Knottenbelt, W., Rogers, A., Singh, A., & Srivastava, M. (2014). NILMTK: an open source toolkit for non-intrusive load monitoring. Proceedings of the 5th International Conference on Future Energy Systems, 265–276.
• Beckel, C., Kleiminger, W., Cicchetti, R., Staake, T., & Santini, S. (2014). The ECO data set and the performance of non-intrusive load monitoring algorithms. Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, 80–89.
• Bhotto, M. Z. A., Makonin, S., & Bajić, I. V. (2016). Load disaggregation based on aided linear integer programming. IEEE Transactions on Circuits and Systems II: Express Briefs, 64(7), 792–796.
• Biansoongnern, S., & Plungklang, B. (2016). Non-Intrusive Appliances Load Monitoring (NILM) for Energy Conservation in Household with Low Sampling Rate. Procedia Computer Science, 86(March), 172–175. https://doi.org/10.1016/j.procs.2016.05.049
• Butner, R. S., Reid, D. J., Hoffman, M. G., Sullivan, G., & Blanchard, J. (2013). Non-intrusive load monitoring assessment: literature review and laboratory protocol. Pacific Northwest National Lab.(PNNL), Richland, WA (United States).
• Chang, H. H., Lin, L. S., Chen, N., & Lee, W. J. (2013). Particle-swarm-optimization-based nonintrusive demand monitoring and load identification in smart meters. IEEE Transactions on Industry Applications. https://doi.org/10.1109/TIA.2013.2258875
• 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.
• De Lello, G. C., Caldeira, J. F., Aredes, M., Franca, F. M. G., & Lima, P. M. V. (2020). Weightless neural networks applied to nonintrusive load monitoring. Proceedings - 2020 IEEE 34th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020. https://doi.org/10.1109/IPDPSW50202.2020.00143
• Djordjevic, S., & Simic, M. (2018). Nonintrusive identification of residential appliances using harmonic analysis. Turkish Journal of Electrical Engineering and Computer Sciences, 26(2), 780–791. https://doi.org/10.3906/elk-1705-262
• Edebal, E. Y. H., Enstit, F. B., Dal, A., Esener, I., Tezi, L., Dan, T., & Tez, M. K. (2012). Akilli si̇stemler kullanilarak güç si̇stemleri̇nde yük tahmi̇ni̇ anali̇zi̇ ve uygulamasi.
• Esa, N. F., Abdullah, M. P., & Hassan, M. Y. (2016). A review disaggregation method in Non-intrusive Appliance Load Monitoring. Renewable and Sustainable Energy Reviews, 66, 163–173. https://doi.org/10.1016/j.rser.2016.07.009
• Harell, A., Makonin, S., & Bajic, I. V. (2019). Wavenilm: A Causal Neural Network for Power Disaggregation from the Complex Power Signal. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2019-May, 8335–8339. https://doi.org/10.1109/ICASSP.2019.8682543
• Hart, G. W. (1992). Nonintrusive Appliance Load Monitoring. Proceedings of the IEEE, 80(12), 1870–1891. https://doi.org/10.1109/5.192069
• Johnson, M. J., & Willsky, A. S. (2013). Bayesian nonparametric hidden semi-Markov models. Journal of Machine Learning Research, 14(Feb), 673–701.
• Kamat, P. V. (2007). Meeting the clean energy demand: Nanostructure architectures for solar energy conversion. Journal of Physical Chemistry C, 111(7), 2834–2860. https://doi.org/10.1021/jp066952u
• Kelly, J., & Knottenbelt, W. (2015). Neural nilm: Deep neural networks applied to energy disaggregation. Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, 55–64.
• Kim, H., Marwah, M., Arlitt, M., Lyon, G., & Han, J. (2011). Unsupervised disaggregation of low frequency power measurements. Proceedings of the 2011 SIAM International Conference on Data Mining, 747–758.
• Kim, J., Le, T.-T.-H., & Kim, H. (2017). Nonintrusive load monitoring based on advanced deep learning and novel signature. Computational Intelligence and Neuroscience, 2017.
• Kolter, J. Z., & Jaakkola, T. (2012). Approximate inference in additive factorial hmms with application to energy disaggregation. Artificial Intelligence and Statistics, 1472–1482.
• Kolter, J. Z., & Johnson, M. J. (2011). REDD: A public data set for energy disaggregation research. Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA, 25(Citeseer), 59–62.
• Kothari, D. P., & Nagrath, I. J. (2003). Modern power system analysis Tata McGraw. Hill Publishing Company Limited.
• Li, Y., Peng, Z., Huang, J., Zhang, Z., & Son, J. H. (2014). Energy disaggregation via hierarchical factorial hmm. Proceedings of the 2nd International Workshop on Non-Intrusive Load Monitoring, Austin, TX, USA, 3.
• Machlev, R., Levron, Y., & Beck, Y. (2018). Modified cross-entropy method for classification of events in NILM systems. IEEE Transactions on Smart Grid, 10(5), 4962–4973.
• Maier, M., Bremer, M., & Schramm, S. (2020). Load Profile Modeling Using High-Frequency Appliance Measurements for Nonintrusive Load Monitoring. 2020 8th International Conference on Smart Energy Grid Engineering, SEGE 2020. https://doi.org/10.1109/SEGE49949.2020.9181962
• Makonin, S., Popowich, F., Bajić, I. V, Gill, B., & Bartram, L. (2015). Exploiting HMM sparsity to perform online real-time nonintrusive load monitoring. IEEE Transactions on Smart Grid, 7(6), 2575–2585.
• Makonin, S., Popowich, F., Bartram, L., Gill, B., & Bajić, I. V. (2013). AMPds: A public dataset for load disaggregation and eco-feedback research. 2013 IEEE Electrical Power & Energy Conference, 1–6.
• Mueller, J. A., Sankara, A., Kimball, J. W., & McMillin, B. (2014). Hidden Markov models for nonintrusive appliance load monitoring. 2014 North American Power Symposium, NAPS 2014. https://doi.org/10.1109/NAPS.2014.6965464
• Nalmpantis, C., & Vrakas, D. (2019). Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation. Artificial Intelligence Review, 52(1), 217–243. https://doi.org/10.1007/s10462-018-9613-7
• Nefzi, E., Houidi, S., & Attia Sethom, H. Ben. (2020). A Novel and Scalable Home Appliances Electrical Signature Database for Smart Home Energy Management. 2020 15th International Conference on Ecological Vehicles and Renewable Energies, EVER 2020. https://doi.org/10.1109/EVER48776.2020.9242957
• Paradiso, F., Paganelli, F., Giuli, D., & Capobianco, S. (2016). Context-based energy disaggregation in smart homes. Future Internet, 8(1), 4.
• Parson, O., Ghosh, S., Weal, M., & Rogers, A. (2014). An unsupervised training method for non-intrusive appliance load monitoring. Artificial Intelligence, 217, 1–19.
• Rafiq, H., Zhang, H., Li, H., & Ochani, M. K. (2018). Regularized LSTM Based Deep Learning Model: First Step towards Real-Time Non-Intrusive Load Monitoring. 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE), 234–239.
• Sultanem, F. (1991). Using appliance signatures for monitoring residential loads at meter panel level. IEEE Transactions on Power Delivery, 6(4), 1380–1385.
• Tabatabaei, S. M., Dick, S., & Xu, W. (2016). Toward non-intrusive load monitoring via multi-label classification. IEEE Transactions on Smart Grid, 8(1), 26–40.
• Valenti, M., Bonfigli, R., Principi, E., & Squartini, S. (2018). Exploiting the Reactive Power in Deep Neural Models for Non-Intrusive Load Monitoring. 2018 International Joint Conference on Neural Networks (IJCNN), 1–8.
• Valera, I., Ruiz, F. J. R., & Perez-Cruz, F. (2015). Infinite factorial unbounded-state hidden markov model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(9), 1816–1828.
• Weisz, P. B. (2004). Basic choices and constraints on long-term energy supplies. Physics Today, 57(7), 47–52. https://doi.org/10.1063/1.1784302
• Welikala, S., Dinesh, C., Ekanayake, M. P. B., Godaliyadda, R. I., & Ekanayake, J. (2019). Incorporating Appliance Usage Patterns for Non-Intrusive Load Monitoring and Load Forecasting. IEEE Transactions on Smart Grid. https://doi.org/10.1109/TSG.2017.2743760
• Wittmann, F. M., López, J. C., & Rider, M. J. (2018). Nonintrusive load monitoring algorithm using mixed-integer linear programming. IEEE Transactions on Consumer Electronics, 64(2), 180–187.
• Wong, Y. F., Şekercioğlu, Y. A., Drummond, T., & Wong, V. S. (2013). Recent approaches to non-intrusive load monitoring techniques in residential settings. 2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG), 73–79.
• Yenilmez, M. (2016). AKILLI ŞEBEKELRDE ( Smart Grid ) DAĞITIM S İSTEM O TOMASYONDAKİ G ELİŞMELER LİSANS TEZİ MEKATRONİK MÜHENDİSLİĞİ.
• Zeifman, M., Member, S., & Roth, K. (2011). Nonintrusive Appliance Load Monitoring : Review and Outlook. 57(1), 76–84.
• Zhong, M., Goddard, N., & Sutton, C. (2014a). Interleaved factorial non-homogeneous hidden Markov models for energy disaggregation. ArXiv Preprint ArXiv:1406.7665.
• Zhong, M., Goddard, N., & Sutton, C. (2014b). Signal aggregate constraints in additive factorial HMMs, with application to energy disaggregation. Advances in Neural Information Processing Systems, 3590–3598.
• Zoha, A., Gluhak, A., Imran, M. A., & Rajasegarar, S. (2012). Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors, 12(12), 16838–16866.
• Zoha, A., Gluhak, A., Nati, M., & Imran, M. A. (2013). Low-power appliance monitoring using factorial hidden markov models. 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 527–532.