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Akıllı Enerji Sistemlerinde Büyük Veri: Eleştirel Bir İnceleme

Yıl 2020, Cilt: 11 Sayı: 41, 11 - 26, 05.08.2020

Keziban Seçkin Codal İzzet Arı H. Kemal İlter

https://doi.org/10.5824/ajite.2020.02.001.x

Öz

İklim değişikliği yadsınamaz bir gerçektir. Seragazı emisyonlarının üçte ikisinin enerji sektöründen kaynaklandığı düşünüldüğünde, dünya enerji sisteminin yenilenebilir enerji kaynaklarıyla dönüştürülmesi ve enerji verimliliğinin sürekli artırılması beklenmektedir. Enerjiye bağlı karbondioksit emisyonlarının azaltılması, enerjide dönüşümün gereğidir. Enerji sistemlerindeki büyük veriler, hem uyarlanabilir kapasitenin değerlendirilmesinde hem de enerji talebini ve arzını yönetmek için daha akıllıca yatırım yapılmasında çok önemli bir rol oynamaktadır. Gerçekten de, akıllı enerji şebekesinin ve sayaçların akıllı enerji sistemleri üzerindeki etkisi, karar vericilere enerji üretimi, tüketimi ve topluluklarını dönüştürme konusunda yardımcı olmaktadır.
Bu çalışma, büyük veri ve akıllı enerji sistemlerini değerlendirmek için literatürü gözden geçirmekte ve bölgesel perspektife, döneme, disiplinlere, büyük veri özelliklerine ve kullanılan veri analizlerine göre eleştirilmektedir. Eleştirel inceleme mevcut temalara ayrılmıştır. Bulgular, akıllı enerji yaklaşımlarının geleceği dikkate alınarak veri analitiği kullanılarak akıllı enerji literatürü ve bilimsel çalışma seçeneklerine dayanan büyük veri özellikleri dahil olmak üzere konuları ele almaktadır. Akıllı enerji sistemlerindeki büyük verilere ilişkin yazılar umut verici bir konudur, ancak disiplinler arası kapsamlı çalışmalar yoluyla konuyu genişletmek zorunludur.

Anahtar Kelimeler

akıllı enerji akıllı enerji sistemleri büyük enerji verileri veri analizi iklim değişikliği

Kaynakça

  • Aman, S., Simmhan, Y., & Prasanna, V. K. (2015). Holistic measures for evaluating prediction models in smart grids. IEEE Transactions on Knowledge and Data Engineering, 27(2), 475–486. https://doi.org/10.1109/TKDE.2014.2327022
  • Anderson, B., Lin, S., Newing, A., Bahaj, A. B., & James, P. (2017). Electricity consumption and household characteristics: Implications for census-taking in a smart metered future. Computers, Environment and Urban Systems. https://doi.org/10.1016/j.compenvurbsys.2016.06.003
  • Annual Energy Outlook 2019. (2019). https://doi.org/DOE/EIA-0383(2012) U.S.
  • Bedingfield, S., Alahakoon, D., Genegedera, H., & Chilamkurti, N. (2018). Multi-granular electricity consumer load profiling for smart homes using a scalable big data algorithm. Sustainable Cities and Society, 40, 611–624. https://doi.org/10.1016/j.scs.2018.04.006
  • Chou, J. S., & Ngo, N. T. (2016). Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns. Applied Energy, 177, 751–770. https://doi.org/10.1016/j.apenergy.2016.05.074
  • Chui, K. T., Lytras, M. D., & Visvizi, A. (2018). Energy sustainability in smart cities: Artificial intelligence, smart monitoring, and optimization of energy consumption. Energies, 11(11), 2869. https://doi.org/10.3390/en11112869
  • Clifton, A. (2013). Using Machine Learning to Create Turbine Performance Models.
  • Climate Transparency. (2018). Brown to Green: the G20 Transition to A Low-Carbon Economy.
  • Ebinger, J., & Vergara, W. (2011). Climate Impacts on Energy Systems. The World Bank. https://doi.org/10.1596/978-0-8213-8697
  • EEA. (2019). Energy and climate change. Retrieved January 3, 2020, from https://www.eea.europa.eu/signals/signals-2017/articles/energy-and-climate-change
  • GCF. (2019). Green Climate Fund. Retrieved January 3, 2020, from https://www.greenclimate.fund/mwg-internal/de5fs23hu73ds/progress? id=qt1rnb1dSS9YVz1pGtR0TDXCLexQeB4NKzFJqBzpUKo,&dl
  • Huang, X., Hu, T., Ye, C., Xu, G., Wang, X., & Chen, L. (2019). Electric load data compression and classification based on deep stacked auto-encoders. Energies, 12(4), 653. https://doi.org/10.3390/en12040653
  • Joseph, S., & Erakkath Abdu, J. (2018). Real-time retail price determination in smart grid from real-time load profiles. International Transactions on Electrical Energy Systems, 28(3), e2509. https://doi.org/10.1002/etep.2509
  • Jucikas, T. (2017). Artificial Intelligence and the future of energy. Retrieved October 24, 2019, from https://medium.com/wepower/artificial-intelligence-and-the-future-of-energy-105ac6053de4
  • Khan, G. M., Ali, J., & Mahmud, S. A. (2014). Wind power forecasting - An Application of Machine Learning in Renewable Energy. In 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 1130–1137). IEEE. https://doi.org/10.1109/IJCNN.2014.6889771
  • Koponen, P., Saco, L. D., Orchard, N., Vorisek, T., Rochas, C., Morch, A. Z., … Togeby, M. (2008). Definition of Smart Metering and Applications and Identification of Benefits. Intelligent Energy.
  • Kwac, J., & Rajagopal, R. (2016). Data-driven targeting of customers for demand response. IEEE Transactions on Smart Grid, 7(5), 2199–2207. https://doi.org/10.1109/TSG.2015.2480841
  • Lammers, I., & Hoppe, T. (2019). Watt rules? Assessing decision-making practices on smart energy systems in Dutch city districts. Energy Research and Social Science, 47(January 2018), 233–246. https://doi.org/10.1016/j.erss.2018.10.003
  • Li, C., Liu, C., Deng, K., Yu, X., & Huang, T. (2018). Data-Driven Charging Strategy of PEVs under Transformer Aging Risk. IEEE Transactions on Control Systems Technology, 26(4), 1386–1399. https://doi.org/10.1109/TCST.2017.2713321
  • Li, K., Cursio, J. D., & Sun, Y. (2018). Principal component analysis of price fluctuation in the smart grid electricity market. Sustainability (Switzerland), 10(11), 4019. https://doi.org/10.3390/su10114019
  • Li, R., Li, F., & Smith, N. D. (2016). Multi-Resolution Load Profile Clustering for Smart Metering Data. IEEE Transactions on Power Systems, 31(6), 4473–4482. https://doi.org/10.1109/TPWRS.2016.2536781
  • Lund, H., Østergaard, P. A., Connolly, D., & Mathiesen, B. V. (2017). Smart energy and smart energy systems. Energy. https://doi.org/10.1016/j.energy.2017.05.123
  • Maaß, H., Cakmak, H. K., Bach, F., Mikut, R., Harrabi, A., Süß, W., … Hagenmeyer, V. (2015). Data processing of high-rate low-voltage distribution grid recordings for smart grid monitoring and analysis. EURASIP Journal on Advances in Signal Processing, 2015(1), 14. https://doi.org/10.1186/s13634-015-0203-4
  • Marinakis, V., Doukas, H., Tsapelas, J., Mouzakitis, S., Sicilia, Á., Madrazo, L., & Sgouridis, S. (2018). From big data to smart energy services: An application for intelligent energy management. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2018.04.062
  • Mohamed, M. F., El-Gayyar, M., Shabayek, A. E. R., & Nassar, H. (2018). Data reduction in a cloud-based AMI framework with service-replication. Computers and Electrical Engineering, 69, 212–223. https://doi.org/10.1016/j.compeleceng.2018.02.042
  • Mohammad, R. (2018). AMI Smart Meter Big Data Analytics for Time Series of Electricity Consumption. In 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 1771–1776). IEEE. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00267
  • Munshi, A. A., & Mohamed, Y. A. R. I. (2017). Big data framework for analytics in smart grids. Electric Power Systems Research, 151, 369–380. https://doi.org/10.1016/j.epsr.2017.06.006 Pei, X., Zheng, D., She, S., Ma, J., Guo, C., Mo, X., … Wang, Y. (2017). The PSMP-CCR2 interactions trigger monocyte/macrophage-dependent colitis /631/154/51/1568 /631/250/98 /13/21 /13/1 /13/31 /38/77 /64/60 /82/51 /13 article. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-05255-7
  • Peppanen, J., Reno, M. J., Thakkar, M., Grijalva, S., & Harley, R. G. (2015). Leveraging AMI Data for Distribution System Model Calibration and Situational Awareness. IEEE Transactions on Smart Grid, 6(4), 2050–2059. https://doi.org/10.1109/TSG.2014.2385636
  • Perera, K. S., Aung, Z., & Woon, W. L. (2014). Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey (pp. 81–96). https://doi.org/10.1007/978-3-319-13290-7_7
  • Rodríguez Fernández, M., González Alonso, I., & Zalama Casanova, E. (2016). Online identification of appliances from power consumption data collected by smart meters. Pattern Analysis and Applications, 19(2), 463–473. https://doi.org/10.1007/s10044-015-0487-x
  • Rusitschka, S., & Curry, E. (2016). Big Data in the Energy and Transport Sectors. In New Horizons for a Data-Driven Economy (pp. 225–244). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-21569-3_13
  • Salami, M., Movahedi Sobhani, F., & Ghazizadeh, M. (2018). Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market. Data, 3(4), 43. https://doi.org/10.3390/data3040043
  • Shi, H., Xu, M., & Li, R. (2018). Deep Learning for Household Load Forecasting-A Novel Pooling Deep RNN. IEEE Transactions on Smart Grid, 9(5), 5271–5280. https://doi.org/10.1109/TSG.2017.2686012
  • Singh, S., & Yassine, A. (2018). Big data mining of energy time series for behavioral analytics and energy consumption forecasting. Energies, 11(2), 452. https://doi.org/10.3390/en11020452
  • SRU. (2011). Pathways Towards a 100% Renewable Electricity System.
  • The Conversation. (2018). Winds of change: Britain now generates twice as much electricity from wind as coal. Retrieved March 2, 2020, from https://theconversation.com/winds-of-change-britain-now-generates-twice-as-much-electricity-from-wind-as-coal-89598
  • Tong, X., Kang, C., & Xia, Q. (2016). Smart Metering Load Data Compression Based on Load Feature Identification. IEEE Transactions on Smart Grid, 7(5), 2414–2422. https://doi.org/10.1109/TSG.2016.2544883
  • Treiber, N. A., Heinermann, J., & Kramer, O. (2016). Wind Power Prediction with Machine Learning (pp. 13–29). https://doi.org/10.1007/978-3-319-31858-5_2
  • Wang, Y., Chen, Q., Gan, D., Yang, J., Kirschen, D. S., & Kang, C. (2019). Deep learning-based socio-demographic information identification from smart meter data. IEEE Transactions on Smart Grid, 10(3), 2593–2602. https://doi.org/10.1109/TSG.2018.2805723
  • Wang, Z., Liu, M., & Guo, H. (2016). A strategic path for the goal of clean and low-carbon energy in China. Natural Gas Industry B, 3(4), 305–311. https://doi.org/10.1016/j.ngib.2016.12.006
  • Wen, L., Zhou, K., Yang, S., & Li, L. (2018). Compression of smart meter big data: A survey. Renewable and Sustainable Energy Reviews, 91, 59–69. https://doi.org/10.1016/j.rser.2018.03.088
  • Zahid, M., Ahmed, F., Javaid, N., Abbasi, R. A., Kazmi, H. S. Z., Javaid, A., … Ilahi, M. (2019). Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics (Switzerland), 8(2), 122. https://doi.org/10.3390/electronics8020122
  • Zhang, P., Wu, X., Wang, X., & Bi, S. (2015). Short-term load forecasting based on big data technologies. CSEE Journal of Power and Energy Systems, 1(3), 59–67. https://doi.org/10.17775/CSEEJPES.2015.00036
  • Zhang, Q., Wan, S., Wang, B., Gao, D. W., & Ma, H. (2019). Anomaly detection based on random matrix theory for industrial power systems. Journal of Systems Architecture, 95, 67–74. https://doi.org/10.1016/j.sysarc.2019.01.008
  • Zhang, Y., Huang, T., & Bompard, E. F. (2018). Big data analytics in smart grids: a review. Energy Informatics, 1(1), 8. https://doi.org/10.1186/s42162-018-0007-5
  • Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56(2016), 215–225. https://doi.org/10.1016/j.rser.2015.11.050
  • Zhou, K., Yang, C., & Shen, J. (2017). Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China. Utilities Policy, 44, 73–84. https://doi.org/10.1016/j.jup.2017.01.004
  • Zhou, K., & Yang, S. (2018). Smart Energy Management. Comprehensive Energy Systems (Vol. 5–5). https://doi.org/10.1016/B978-0-12-809597-3.00525-3
  • Zhou, X., Li, K., Ma, Y., & Gao, Z. (2018). Research Review on Big Data of the Smart Grid. In 2018 IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 2030–2035). IEEE. https://doi.org/10.1109/ICMA.2018.8484631
  • Zhou, Y., Hao, F., Meng, W., & Fu, J. (2014). Scenario analysis of energy-based low-carbon development in China. Journal of Environmental Sciences (China), 26(8), 1631–1640. https://doi.org/10.1016/j.jes.2014.06.003

Big Data in Smart Energy Systems: A Critical Review

Yıl 2020, Cilt: 11 Sayı: 41, 11 - 26, 05.08.2020

Keziban Seçkin Codal İzzet Arı H. Kemal İlter

https://doi.org/10.5824/ajite.2020.02.001.x

Öz

Climate change is an undeniable fact. Considering that two-thirds of greenhouse gas emissions originate from the energy sector, it is expected that the world's energy system will be transformed with renewable energy sources. Energy efficiency will be continuously increased. Reducing energy-related carbon dioxide emissions is the heart of the energy transition. Big data in energy systems play a crucial role in evaluating the adaptive capacity and investing more smartly to manage energy demand and supply. Indeed, the impact of the smart energy grid and meters on smart energy systems provide and assist decision-makers in transforming energy production, consumption, and communities. This study reviews the literature for aligning big data and smart energy systems and criticized according to regional perspective, period, disciplines, big data characteristics, and used data analytics. The critical review has been categorized into present themes. The results address issues, including scientific studies using data analysis techniques that take into account the characteristics of big data in the smart energy literature and the future of smart energy approaches. The manuscripts on big data in smart energy systems are a promising issue, albeit it is essential to expand subjects through comprehensive interdisciplinary studies

Anahtar Kelimeler

Smart Energy Smart Energy Systems Energy Big Data Data Analytics Climate Change

Kaynakça

  • Aman, S., Simmhan, Y., & Prasanna, V. K. (2015). Holistic measures for evaluating prediction models in smart grids. IEEE Transactions on Knowledge and Data Engineering, 27(2), 475–486. https://doi.org/10.1109/TKDE.2014.2327022
  • Anderson, B., Lin, S., Newing, A., Bahaj, A. B., & James, P. (2017). Electricity consumption and household characteristics: Implications for census-taking in a smart metered future. Computers, Environment and Urban Systems. https://doi.org/10.1016/j.compenvurbsys.2016.06.003
  • Annual Energy Outlook 2019. (2019). https://doi.org/DOE/EIA-0383(2012) U.S.
  • Bedingfield, S., Alahakoon, D., Genegedera, H., & Chilamkurti, N. (2018). Multi-granular electricity consumer load profiling for smart homes using a scalable big data algorithm. Sustainable Cities and Society, 40, 611–624. https://doi.org/10.1016/j.scs.2018.04.006
  • Chou, J. S., & Ngo, N. T. (2016). Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns. Applied Energy, 177, 751–770. https://doi.org/10.1016/j.apenergy.2016.05.074
  • Chui, K. T., Lytras, M. D., & Visvizi, A. (2018). Energy sustainability in smart cities: Artificial intelligence, smart monitoring, and optimization of energy consumption. Energies, 11(11), 2869. https://doi.org/10.3390/en11112869
  • Clifton, A. (2013). Using Machine Learning to Create Turbine Performance Models.
  • Climate Transparency. (2018). Brown to Green: the G20 Transition to A Low-Carbon Economy.
  • Ebinger, J., & Vergara, W. (2011). Climate Impacts on Energy Systems. The World Bank. https://doi.org/10.1596/978-0-8213-8697
  • EEA. (2019). Energy and climate change. Retrieved January 3, 2020, from https://www.eea.europa.eu/signals/signals-2017/articles/energy-and-climate-change
  • GCF. (2019). Green Climate Fund. Retrieved January 3, 2020, from https://www.greenclimate.fund/mwg-internal/de5fs23hu73ds/progress? id=qt1rnb1dSS9YVz1pGtR0TDXCLexQeB4NKzFJqBzpUKo,&dl
  • Huang, X., Hu, T., Ye, C., Xu, G., Wang, X., & Chen, L. (2019). Electric load data compression and classification based on deep stacked auto-encoders. Energies, 12(4), 653. https://doi.org/10.3390/en12040653
  • Joseph, S., & Erakkath Abdu, J. (2018). Real-time retail price determination in smart grid from real-time load profiles. International Transactions on Electrical Energy Systems, 28(3), e2509. https://doi.org/10.1002/etep.2509
  • Jucikas, T. (2017). Artificial Intelligence and the future of energy. Retrieved October 24, 2019, from https://medium.com/wepower/artificial-intelligence-and-the-future-of-energy-105ac6053de4
  • Khan, G. M., Ali, J., & Mahmud, S. A. (2014). Wind power forecasting - An Application of Machine Learning in Renewable Energy. In 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 1130–1137). IEEE. https://doi.org/10.1109/IJCNN.2014.6889771
  • Koponen, P., Saco, L. D., Orchard, N., Vorisek, T., Rochas, C., Morch, A. Z., … Togeby, M. (2008). Definition of Smart Metering and Applications and Identification of Benefits. Intelligent Energy.
  • Kwac, J., & Rajagopal, R. (2016). Data-driven targeting of customers for demand response. IEEE Transactions on Smart Grid, 7(5), 2199–2207. https://doi.org/10.1109/TSG.2015.2480841
  • Lammers, I., & Hoppe, T. (2019). Watt rules? Assessing decision-making practices on smart energy systems in Dutch city districts. Energy Research and Social Science, 47(January 2018), 233–246. https://doi.org/10.1016/j.erss.2018.10.003
  • Li, C., Liu, C., Deng, K., Yu, X., & Huang, T. (2018). Data-Driven Charging Strategy of PEVs under Transformer Aging Risk. IEEE Transactions on Control Systems Technology, 26(4), 1386–1399. https://doi.org/10.1109/TCST.2017.2713321
  • Li, K., Cursio, J. D., & Sun, Y. (2018). Principal component analysis of price fluctuation in the smart grid electricity market. Sustainability (Switzerland), 10(11), 4019. https://doi.org/10.3390/su10114019
  • Li, R., Li, F., & Smith, N. D. (2016). Multi-Resolution Load Profile Clustering for Smart Metering Data. IEEE Transactions on Power Systems, 31(6), 4473–4482. https://doi.org/10.1109/TPWRS.2016.2536781
  • Lund, H., Østergaard, P. A., Connolly, D., & Mathiesen, B. V. (2017). Smart energy and smart energy systems. Energy. https://doi.org/10.1016/j.energy.2017.05.123
  • Maaß, H., Cakmak, H. K., Bach, F., Mikut, R., Harrabi, A., Süß, W., … Hagenmeyer, V. (2015). Data processing of high-rate low-voltage distribution grid recordings for smart grid monitoring and analysis. EURASIP Journal on Advances in Signal Processing, 2015(1), 14. https://doi.org/10.1186/s13634-015-0203-4
  • Marinakis, V., Doukas, H., Tsapelas, J., Mouzakitis, S., Sicilia, Á., Madrazo, L., & Sgouridis, S. (2018). From big data to smart energy services: An application for intelligent energy management. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2018.04.062
  • Mohamed, M. F., El-Gayyar, M., Shabayek, A. E. R., & Nassar, H. (2018). Data reduction in a cloud-based AMI framework with service-replication. Computers and Electrical Engineering, 69, 212–223. https://doi.org/10.1016/j.compeleceng.2018.02.042
  • Mohammad, R. (2018). AMI Smart Meter Big Data Analytics for Time Series of Electricity Consumption. In 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 1771–1776). IEEE. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00267
  • Munshi, A. A., & Mohamed, Y. A. R. I. (2017). Big data framework for analytics in smart grids. Electric Power Systems Research, 151, 369–380. https://doi.org/10.1016/j.epsr.2017.06.006 Pei, X., Zheng, D., She, S., Ma, J., Guo, C., Mo, X., … Wang, Y. (2017). The PSMP-CCR2 interactions trigger monocyte/macrophage-dependent colitis /631/154/51/1568 /631/250/98 /13/21 /13/1 /13/31 /38/77 /64/60 /82/51 /13 article. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-05255-7
  • Peppanen, J., Reno, M. J., Thakkar, M., Grijalva, S., & Harley, R. G. (2015). Leveraging AMI Data for Distribution System Model Calibration and Situational Awareness. IEEE Transactions on Smart Grid, 6(4), 2050–2059. https://doi.org/10.1109/TSG.2014.2385636
  • Perera, K. S., Aung, Z., & Woon, W. L. (2014). Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey (pp. 81–96). https://doi.org/10.1007/978-3-319-13290-7_7
  • Rodríguez Fernández, M., González Alonso, I., & Zalama Casanova, E. (2016). Online identification of appliances from power consumption data collected by smart meters. Pattern Analysis and Applications, 19(2), 463–473. https://doi.org/10.1007/s10044-015-0487-x
  • Rusitschka, S., & Curry, E. (2016). Big Data in the Energy and Transport Sectors. In New Horizons for a Data-Driven Economy (pp. 225–244). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-21569-3_13
  • Salami, M., Movahedi Sobhani, F., & Ghazizadeh, M. (2018). Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market. Data, 3(4), 43. https://doi.org/10.3390/data3040043
  • Shi, H., Xu, M., & Li, R. (2018). Deep Learning for Household Load Forecasting-A Novel Pooling Deep RNN. IEEE Transactions on Smart Grid, 9(5), 5271–5280. https://doi.org/10.1109/TSG.2017.2686012
  • Singh, S., & Yassine, A. (2018). Big data mining of energy time series for behavioral analytics and energy consumption forecasting. Energies, 11(2), 452. https://doi.org/10.3390/en11020452
  • SRU. (2011). Pathways Towards a 100% Renewable Electricity System.
  • The Conversation. (2018). Winds of change: Britain now generates twice as much electricity from wind as coal. Retrieved March 2, 2020, from https://theconversation.com/winds-of-change-britain-now-generates-twice-as-much-electricity-from-wind-as-coal-89598
  • Tong, X., Kang, C., & Xia, Q. (2016). Smart Metering Load Data Compression Based on Load Feature Identification. IEEE Transactions on Smart Grid, 7(5), 2414–2422. https://doi.org/10.1109/TSG.2016.2544883
  • Treiber, N. A., Heinermann, J., & Kramer, O. (2016). Wind Power Prediction with Machine Learning (pp. 13–29). https://doi.org/10.1007/978-3-319-31858-5_2
  • Wang, Y., Chen, Q., Gan, D., Yang, J., Kirschen, D. S., & Kang, C. (2019). Deep learning-based socio-demographic information identification from smart meter data. IEEE Transactions on Smart Grid, 10(3), 2593–2602. https://doi.org/10.1109/TSG.2018.2805723
  • Wang, Z., Liu, M., & Guo, H. (2016). A strategic path for the goal of clean and low-carbon energy in China. Natural Gas Industry B, 3(4), 305–311. https://doi.org/10.1016/j.ngib.2016.12.006
  • Wen, L., Zhou, K., Yang, S., & Li, L. (2018). Compression of smart meter big data: A survey. Renewable and Sustainable Energy Reviews, 91, 59–69. https://doi.org/10.1016/j.rser.2018.03.088
  • Zahid, M., Ahmed, F., Javaid, N., Abbasi, R. A., Kazmi, H. S. Z., Javaid, A., … Ilahi, M. (2019). Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics (Switzerland), 8(2), 122. https://doi.org/10.3390/electronics8020122
  • Zhang, P., Wu, X., Wang, X., & Bi, S. (2015). Short-term load forecasting based on big data technologies. CSEE Journal of Power and Energy Systems, 1(3), 59–67. https://doi.org/10.17775/CSEEJPES.2015.00036
  • Zhang, Q., Wan, S., Wang, B., Gao, D. W., & Ma, H. (2019). Anomaly detection based on random matrix theory for industrial power systems. Journal of Systems Architecture, 95, 67–74. https://doi.org/10.1016/j.sysarc.2019.01.008
  • Zhang, Y., Huang, T., & Bompard, E. F. (2018). Big data analytics in smart grids: a review. Energy Informatics, 1(1), 8. https://doi.org/10.1186/s42162-018-0007-5
  • Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56(2016), 215–225. https://doi.org/10.1016/j.rser.2015.11.050
  • Zhou, K., Yang, C., & Shen, J. (2017). Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China. Utilities Policy, 44, 73–84. https://doi.org/10.1016/j.jup.2017.01.004
  • Zhou, K., & Yang, S. (2018). Smart Energy Management. Comprehensive Energy Systems (Vol. 5–5). https://doi.org/10.1016/B978-0-12-809597-3.00525-3
  • Zhou, X., Li, K., Ma, Y., & Gao, Z. (2018). Research Review on Big Data of the Smart Grid. In 2018 IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 2030–2035). IEEE. https://doi.org/10.1109/ICMA.2018.8484631
  • Zhou, Y., Hao, F., Meng, W., & Fu, J. (2014). Scenario analysis of energy-based low-carbon development in China. Journal of Environmental Sciences (China), 26(8), 1631–1640. https://doi.org/10.1016/j.jes.2014.06.003
Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makaleleri
Yazarlar

Keziban Seçkin Codal 0000-0003-1967-7751

İzzet Arı 0000-0002-6117-3605

H. Kemal İlter

Yayımlanma Tarihi 5 Ağustos 2020
Gönderilme Tarihi 10 Nisan 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 11 Sayı: 41

Kaynak Göster

APA Seçkin Codal, K., Arı, İ., & İlter, H. K. (2020). Big Data in Smart Energy Systems: A Critical Review. AJIT-E: Academic Journal of Information Technology, 11(41), 11-26. https://doi.org/10.5824/ajite.2020.02.001.x
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