Smart electrical grids based on cloud, IoT, and big data technologies: state of the art

The smart electrical grid (SEG), that utilizes information for creating a widely distributed automated energy delivery network, is considered as an advanced digital 2-way power flow power system. Under different uncertainties, SEG is capable of self-healing, adaptive, resilient, and sustainable with foresight for prediction. Hence, SEG is considered as the next generation power grid. In this paper, a comprehensive survey on SEG as a new technology and operating models which will affect performance of distribution networks in the future are explored in detail. Most of the basic concepts affect such new technology like (Internet of Things (IoT), fog, cloud computing, and big data analysis) are discussed. A brief overview of IoT technologies is provided. It will explore the architectural structure of a typical IoT, cloud computing system, and different levels of the system. Furthermore, many classification methods and then electrical load forecasting (ELF) strategy that includes the preprocessing phase and the prediction phase have been discussed. Additionally, the different techniques used to manage big data generated by sensors and meters for application processing are explored. Feature selection and outlier rejection are discussed as a preprocessing process to filter the data, and then the load prediction process is explained. Finally, this paper covers the analysis of the load prediction phase in ELF strategy in which the prediction techniques will be reviewed.

[1]  Ahmed I. Saleh,et al.  A new distributed feature selection technique for classifying gene expression data , 2019, International Journal of Biomathematics.

[2]  Guanglu Sun,et al.  Feature selection for IoT based on maximal information coefficient , 2018, Future Gener. Comput. Syst..

[3]  K. Vijayakumar,et al.  An ACO–ANN based feature selection algorithm for big data , 2019, Cluster Computing.

[4]  Kae Sato,et al.  A Microfluidic Cell Stretch Device to Investigate the Effects of Stretching Stress on Artery Smooth Muscle Cell Proliferation in Pulmonary Arterial Hypertension , 2018, Inventions.

[5]  K. Arunesh,et al.  Map Reduce for big data processing based on traffic aware partition and aggregation , 2018, Cluster Computing.

[6]  Robert John Walters,et al.  Fog Computing and the Internet of Things: A Review , 2018, Big Data Cogn. Comput..

[7]  Ravil Bikmetov,et al.  Infrastructure and applications of Internet of Things in smart grids: A survey , 2017, 2017 North American Power Symposium (NAPS).

[8]  Kim-Kwang Raymond Choo,et al.  Fog data analytics: A taxonomy and process model , 2019, J. Netw. Comput. Appl..

[9]  Simin Li,et al.  Large dataset summarization with automatic parameter optimization and parallel processing for local outlier detection , 2018, Concurr. Comput. Pract. Exp..

[10]  Danladi Ali,et al.  Application of fuzzy – Neuro to model weather parameter variability impacts on electrical load based on long-term forecasting , 2017 .

[11]  Agnieszka Wosiak,et al.  Integrating Correlation-Based Feature Selection and Clustering for Improved Cardiovascular Disease Diagnosis , 2018, Complex..

[12]  Khaled M. Abo-Al-Ez,et al.  A New Strategy of Load Forecasting Technique for Smart Grids. , 2015 .

[13]  Sonja Filiposka,et al.  Feature Ranking Based on Information Gain for Large Classification Problems with MapReduce , 2015, TrustCom 2015.

[14]  Xiaofeng Guo,et al.  Modeling and forecasting building energy consumption: A review of data-driven techniques , 2019, Sustainable Cities and Society.

[15]  Matteo Muratori,et al.  Big Data issues and opportunities for electric utilities , 2015 .

[16]  Haifei Liu,et al.  Credit Risk Contagion in an Evolving Network Model Integrating Spillover Effects and Behavioral Interventions , 2018, Complex..

[17]  George Atia,et al.  Randomized Robust Subspace Recovery and Outlier Detection for High Dimensional Data Matrices , 2015, IEEE Transactions on Signal Processing.

[18]  Rafael Vasconcelos,et al.  Smartphone-based outlier detection: a complex event processing approach for driving behavior detection , 2017, Journal of Internet Services and Applications.

[19]  Ivo D. Dinov Probabilistic Learning: Classification Using Naive Bayes , 2018 .

[20]  Xiaofeng Zhu,et al.  Efficient kNN Classification With Different Numbers of Nearest Neighbors , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[21]  Arunesh Kumar Singh,et al.  Load forecasting techniques and methodologies: A review , 2012, 2012 2nd International Conference on Power, Control and Embedded Systems.

[22]  S. Surender Reddy,et al.  Short term electrical load forecasting using back propagation neural networks , 2014, 2014 North American Power Symposium (NAPS).

[23]  Abdullah Alqahtani,et al.  Investigating the effect of correlation based feature selection on breast cancer diagnosis using artificial neural network and support vector machines , 2017, 2017 International Conference on Informatics, Health & Technology (ICIHT).

[24]  Fei Jiang,et al.  Big data issues in smart grid – A review , 2017 .

[25]  Han Rui,et al.  An approach to Smart Grid metrics , 2011, 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies.

[26]  GuoQiang An Effective Algorithm for Improving the Performance of Naive Bayes for Text Classification , 2010 .

[27]  Sanjeevikumar Padmanaban,et al.  Internet of Things Applications as Energy Internet in Smart Grids and Smart Environments , 2019, Electronics.

[28]  Sajjad Hussain Shah,et al.  A survey: Internet of Things (IOT) technologies, applications and challenges , 2016, 2016 IEEE Smart Energy Grid Engineering (SEGE).

[29]  Divya Jennifer D'Souza,et al.  Detecting Anomalies in Data Stream Using Efficient Techniques: A Review , 2018, 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT).

[30]  Yan Zhen,et al.  Application of Internet of Things in Smart Grid Power Transmission , 2012, 2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing.

[31]  Nuno Cruz,et al.  LoBEMS—IoT for Building and Energy Management Systems , 2019, Electronics.

[32]  Xinyu Yang,et al.  A Survey on the Edge Computing for the Internet of Things , 2018, IEEE Access.

[33]  Xin Liu,et al.  Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases , 2018, Complex..

[34]  Tao Huang,et al.  Big data analytics in smart grids: a review , 2018, Energy Informatics.

[35]  Mostafa A. Elhosseini,et al.  Enhancing smart grid transient performance using storage device-based MPC controller , 2017 .

[36]  Carlos Denner dos Santos Changes in free and open source software licenses: managerial interventions and variations on project attractiveness , 2017, Journal of Internet Services and Applications.

[37]  Xu Peng,et al.  Short-Term City Electric Load Forecasting with Considering Temperature Effects: An Improved ARIMAX Model , 2015 .

[38]  Dirk Schaefer,et al.  Fog Computing as a Complementary Approach to Cloud Computing , 2019, 2019 International Conference on Computer and Information Sciences (ICCIS).

[39]  M. Mohanapriya,et al.  Map-Reduce framework based cluster architecture for academic student’s performance prediction using cumulative dragonfly based neural network , 2018, Cluster Computing.

[40]  Özgür B. Akan,et al.  Energy Harvesting Cognitive Radio Networking for IoT-enabled Smart Grid , 2018, Mob. Networks Appl..

[41]  M. M. Sufyan Beg,et al.  Fog Computing for Internet of Things (IoT)-Aided Smart Grid Architectures , 2019, Big Data Cogn. Comput..

[42]  Sudip Misra,et al.  Cloud Computing Applications for Smart Grid: A Survey , 2015, IEEE Transactions on Parallel and Distributed Systems.

[43]  Yasser Abdel-Rady I. Mohamed,et al.  Data Lake Lambda Architecture for Smart Grids Big Data Analytics , 2018, IEEE Access.

[44]  Song Guo,et al.  Robust Big Data Analytics for Electricity Price Forecasting in the Smart Grid , 2019, IEEE Transactions on Big Data.

[45]  Milos Manic,et al.  Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions , 2019 .

[46]  Hesham A. Ali,et al.  A fog based load forecasting strategy based on multi-ensemble classification for smart grids , 2020, J. Ambient Intell. Humaniz. Comput..

[47]  Mona Nasr,et al.  Detection outliers on internet of things using big data technology , 2019 .

[48]  Alireza Ghasempour,et al.  Internet of Things in Smart Grid: Architecture, Applications, Services, Key Technologies, and Challenges , 2019, Inventions.

[49]  Lei Cao,et al.  Distributed Top-N local outlier detection in big data , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[50]  Nadeem Javaid,et al.  Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids , 2019, Electronics.

[51]  Athanasios V. Vasilakos,et al.  Energy Big Data Analytics and Security: Challenges and Opportunities , 2016, IEEE Transactions on Smart Grid.

[52]  Radha Senthilkumar,et al.  An online approach for feature selection for classification in big data , 2017 .

[53]  P. Sumathi,et al.  An hybrid metaheuristic approach for efficient feature selection , 2018, Cluster Computing.

[54]  Yong Wang,et al.  A Feature Selection Method for Large-Scale Network Traffic Classification Based on Spark , 2016, Inf..

[55]  Houda Daki,et al.  Big Data management in smart grid: concepts, requirements and implementation , 2017, Journal of Big Data.

[56]  Cheong Hee Park,et al.  Outlier and anomaly pattern detection on data streams , 2018, The Journal of Supercomputing.

[57]  Nathan Marz,et al.  Big Data: Principles and best practices of scalable realtime data systems , 2015 .

[58]  Arwa Alrawais,et al.  An Attribute-Based Encryption Scheme to Secure Fog Communications , 2017, IEEE Access.

[59]  Yuan Tian,et al.  Chi-square Statistics Feature Selection Based on Term Frequency and Distribution for Text Categorization , 2015 .

[60]  Ivo D. Dinov,et al.  Data Science and Predictive Analytics , 2018, Springer International Publishing.

[61]  Chao-Tung Yang,et al.  On construction of an energy monitoring service using big data technology for the smart campus , 2019, Cluster Computing.

[62]  Hamid Sharif,et al.  A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges , 2013, IEEE Communications Surveys & Tutorials.

[63]  Tao Wang,et al.  Feature Grouping-Based Outlier Detection Upon Streaming Trajectories , 2017, IEEE Transactions on Knowledge and Data Engineering.

[64]  Wanneng Shu,et al.  Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System , 2019, IEEE Access.

[65]  Abdelkader Bousselham,et al.  The Internet of Energy: Smart Sensor Networks and Big Data Management for Smart Grid , 2015, FNC/MobiSPC.

[66]  T Vijayakumar,et al.  REVIEW ON IOT BASED SMART GRID ARCHITECTURE IMPLEMENTATIONS , 2019, Journal of Electrical Engineering and Automation.

[67]  Ahmed I. Saleh,et al.  Gene expression cancer classification using modified K-Nearest Neighbors technique , 2019, Biosyst..

[68]  Mehdi Hosseinzadeh,et al.  Resource allocation mechanisms and approaches on the Internet of Things , 2019, Cluster Computing.

[69]  Kuang-Ching Wang,et al.  Review of Internet of Things (IoT) in Electric Power and Energy Systems , 2018, IEEE Internet of Things Journal.

[70]  Danladi Ali,et al.  Long-term load forecast modelling using a fuzzy logic approach , 2016 .

[71]  Mahak Motwani,et al.  A Novel Semi Supervised Algorithm for Text Classification Using BPNN by Active Search , 2014 .

[72]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[73]  Gehao Sheng,et al.  A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model , 2018, IEEE Transactions on Smart Grid.

[74]  Lukasz Chomatek,et al.  Efficient Genetic Algorithm for Breast Cancer Diagnosis , 2018, ITIB.

[75]  Jianhui Wang,et al.  A Hierarchical Framework for Smart Grid Anomaly Detection Using Large-Scale Smart Meter Data , 2018, IEEE Transactions on Smart Grid.

[76]  Rinkle Rani,et al.  A scalable correlation‐based approach for outlier detection in wireless body sensor networks , 2019, Int. J. Commun. Syst..

[77]  Hesham A. Ali,et al.  A fog based load forecasting strategy for smart grids using big electrical data , 2018, Cluster Computing.

[78]  N. Radhika,et al.  A big data framework for intrusion detection in smart grids using apache spark , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[79]  Khaled M. Abo-Al-Ez,et al.  A data mining based load forecasting strategy for smart electrical grids , 2016, Adv. Eng. Informatics.

[80]  Hesham A. Ali,et al.  A new outlier rejection methodology for supporting load forecasting in smart grids based on big data , 2019, Cluster Computing.

[81]  D. S. Chauhan,et al.  Short-Term Load Forecasting by using Ann, Fuzzy Logic and Fuzzy Neural Network , 2017 .

[82]  Aoife Foley,et al.  Ensemble Methods of Classification for Power Systems Security Assessment , 2016, Applied Computing and Informatics.

[83]  S.Jagan natha,et al.  Performance Evaluation by Throughput Analysis in Private Cloud , 2017 .

[84]  Hamed Mohsenian-Rad,et al.  Power systems big data analytics: An assessment of paradigm shift barriers and prospects , 2018, Energy Reports.

[85]  Behnam Bahrak,et al.  Evaluation of big data frameworks for analysis of smart grids , 2019, J. Big Data.

[86]  Shanlin Yang,et al.  Big data driven smart energy management: From big data to big insights , 2016 .

[87]  Ousama Ben-Salha,et al.  Short-term electric load forecasting in Tunisia using artificial neural networks , 2020, Energy Systems.

[88]  Lei Guo,et al.  Temporal, Functional and Spatial Big Data Computing Framework for Large-Scale Smart Grid , 2019, IEEE Transactions on Emerging Topics in Computing.

[89]  Dunwei Gong,et al.  Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends , 2019, IEEE Access.

[90]  Huaxiang Cai,et al.  Iterative Learning Control with Extended State Observer for Telescope System , 2015 .

[91]  Nasser Yousefi,et al.  A New Prediction Model Based on Cascade NN for Wind Power Prediction , 2019 .