Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2020, 3(1); doi: 10.25236/AJCIS.2020.030107.

Integrating Blockchain and Broad Learning for Smart Energy Innovation: Design and Experiment

Author(s)

Yubo Zhai1, 2, *, Xianghan Zheng1, 2, 4 and Songpu Ai3, 4

Corresponding Author:
Yubo Zhai
Affiliation(s)

1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
2. Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou 350116, China
3. Research Institute of Information Technology, Tsinghua University, Beijing 100084, China
4. Blockchain Innovation Lab, MingByte Technology (Qingdao) Co., Ltd., Qingdao 266041, China
*Corresponding author e-mail: 1115373971@qq.com

Abstract

With the rapid development in recent years, the architecture and data sources of energy internet (EI) becomes increasingly complicated. It is a good opportunity and trend to combine energy equipment and information based on blockchain technology. However, high computational complexity inherently due to the blockchain must be addressed to achieve rapidly user electricity prediction. In this paper, an algorithm model of a blockchain technology combined with a broad learning data fusion algorithm is proposed. A novel and detailed combination model is introduced, mainly including CNN and LSTM model. Combination model considers energy data and blockchain data as parameter, and defines selection logic to guarantee user electricity forecast accuracy. Our proposed solution takes the advantage of CNN model in prediction accuracy and computation complexity, and advantage of LSTM model in long term memory. Further experiments illustrate that our solution achieves higher prediction accuracy of 94.1% (without considering the influence of real-time data), with the best result achieved ever. The experimental prediction results provide underlying insights to guide the direction of system resource planning.

Keywords

Blockchain, Broad Learning, CNN+LSTM, Data Fusion, Energy Internet

Cite This Paper

Yubo Zhai, Xianghan Zheng and Songpu Ai. Integrating Blockchain and Broad Learning for Smart Energy Innovation: Design and Experiment. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 1: 59-77. https://doi.org/10.25236/AJCIS.2020.030107.

References

[1] Daneshvar, Mohammadreza, Mahmoud Pesaran, and Behnam Mohammadi-ivatloo. "Transactive energy in future smart homes." The Energy Internet. Woodhead Publishing, 2019. 153-179.
[2] CAI jinqi, et al. Energy transactions based on block chain in energy Internet. Power Construction, 38.9 (2017) 24-31.
[3] Hou, Weigang, Lei Guo, and Zhaolong Ning. "Local Electricity Storage for Blockchain-based Energy Trading in Industrial Internet of Things."IEEE Transactions on Industrial Informatics15.6 (2019): 3610-3619.
[4] Huang, Alex Q., et al. "The future renewable electric energy delivery and management (FREEDM) system: the energy internet." Proceedings of the IEEE 99.1 (2010): 133-148.
[5] RIFKIN J. Third industrial revolution: how lateral power is transforming energy, the economy, and the world [M]. New York: Palgrave Macmillan Trade, 2011: 33-72.
[6] BLOCK C, BOMARIUS F, BRETSCHNEIDER P, et al. Internet of energy-ICT for energy markets of the future [R]. Berlin: BDI, 2010.
[7] ZHANG Jian, et al. “Research on Framework and Typical Application in Energy Internet” [J]. Proceeding of the CSEE, 2016, (15): 4011-4023.
[8] Zhang, Ning, et al. "Blockchain technique in the energy internet: preliminary research framework and typical applications." Proceedings of the CSEE 36.15 (2016): 4011-4022.
[9] S. Ai, C. Rong, and J. Cao, “Utilization of Big Data in Energy Internet Infrastructure,” in Energy Internet - Systems and Applications, 1st ed., Springer, 2020, ch. 9.
[10] Zheng, Zibin, et al. "Blockchain challenges and opportunities: A survey." International Journal of Web and Grid Services 14.4 (2018): 352-375.
[11] Nakamoto S. Bitcoin: A peer-to-peer electronic cash system[R]. Manubot, 2019.
[12] Wood, Gavin. "Ethereum: A secure decentralised generalised transaction ledger." Ethereum project yellow paper 151.2014 (2014): 1-32.
[13] Androulaki, Elli, et al. "Hyperledger fabric: a distributed operating system for permissioned blockchains." Proceedings of the Thirteenth EuroSys Conference. 2018.
[14] Pop, Claudia, et al. "Blockchain based decentralized management of demand response programs in smart energy grids." Sensors 18.1 (2018): 162.
[15] Tapscott, Don, and Alex Tapscott. "How blockchain technology can reinvent the power grid." 2016-05-15). http://fortune.com/2016/05/15/blockchain-reinvents- power-grid (2016).
[16] Nguyen, Clinton. "An indie, off-the-grid, blockchain-traded solar power market comes to brooklyn." 2016-03-18). http://motherboard.vice.com/read/the-plan-to-power-brooklyn-with-a-blockchain-based-microgrid-transactive-solar (2016).
[17] Prisco, Giulio. "An energy blockchain for European prosumers." Bitcoin Mag. https://bitcoinmagazine.Com/articles/an-energy-blockchain-for-european-prosumers - 1462218142. Accessed 3 (2016).
[18] Zeng Ming, Cheng Jun, Wang Yuqing, et al. Research on multi module collaborative autonomous mode of energy internet based on blockchain framework [J]. Proceedings of the CSEE, 2017, 37 (13): 3672-3681 (in Chinese).
[19] Yang Xiaodong, Zhang Youbing, Lu Junjie et al. Automatic demand response of energy storage system in energy local area network based on blockchain technique [J] Proceedings of the CSEE, 2017, 37 (13): 3703-3716 (in Chinese).
[20] Tai Xue, Sun Hongbin, Guo Qinglai. Measures for power trading and congestion management based on blockchain in the energy internet [J]. Power System Technology, 2016, 40 (12): 3630-3638 (in Chinese).
[21] Grover, Aditya, and Jure Leskovec. "node2vec: Scalable feature learning for networks." Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 2016.
[22] Chen, CL Philip, and Zhulin Liu. "Broad learning system: An effective and efficient incremental learning system without the need for deep architecture." IEEE transactions on neural networks and learning systems 29.1 (2017): 10-24.
[23] Yang, Qiang, et al. Transfer learning. Cambridge University Press, 2020.
[24] Mahler, Ronald PS. Statistical multisource-multitarget information fusion. Vol. 685. Norwood, MA: Artech House, 2007.
[25] Chen, CL Philip, Zhulin Liu, and Shuang Feng. "Universal approximation capability of broad learning system and its structural variations." IEEE transactions on neural networks and learning systems 30.4 (2018): 1191-1204.
[26] Wang P, Guo J, Lan Y, et al.  Learning hierarchical representation model for nextbasket recommendation[C]//Proceedings of the 38th International ACM SIGIR conference on Research and Development in Information Retrieval.  ACM, 2015: 403-412.
[27] Möller D P F, Vakilzadian H. Ubiquitous networks: Power line communication and Internet of things in smart home environments [C] //IEEE International Conference on Electro/Information Technology. IEEE, 2014: 596-601.
[28] Liu, Jiaying, et al. "Artificial intelligence in the 21st century." IEEE Access 6 (2018): 34403-34421.
[29] Sun, Yunchuan, et al. "Internet of things and big data analytics for smart and connected communities." IEEE access 4 (2016): 766-773.
[30] Dongsheng, Yang, et al. "Key technologies and application prospects of ubiquitous power Internet of Things." Power generation technology 40.2 (2019): 107-114.
[31] Ming, Yangyang, et al. "Distributed energy sharing in energy internet through distributed averaging." Tsinghua science and Technology 23.3 (2018): 233-242.
[32] Wang, Jiye, et al. "Electricity services based dependability model of power grid communication networking." Tsinghua Science and Technology 19.2 (2014): 121-132.
[33] Xue, Yusheng, and Yening Lai. "Integration of macro energy thinking and big data thinking part one big data and power big data." Automation of Electric Power Systems 40.1 (2016): 1-8.
[34] Armbrust, Michael, et al. "Spark sql: Relational data processing in spark." Proceedings of the 2015 ACM SIGMOD international conference on management of data. 2015.
[35] Flinsbaugh J W, Jones J, Mullendore R N, et al. High performance system providing selective merging of dataframe segments in hardware: U. S. Patent 9,304, 709 [P]. 2016-4-5.
[36] Odersky, Martin, Lex Spoon, and Bill Venners. Programming in scala. Artima Inc, 2008.
[37] Kreps, Jay, Neha Narkhede, and Jun Rao. "Kafka: A distributed messaging system for log processing." Proceedings of the NetDB. Vol. 11. 2011.
[38] Boettiger C. An introduction to Docker for reproducible research [J]. ACM SIGOPS Operating Systems Review, 2015, 49 (1): 71-79.
[39] Hunt, Patrick, et al. "ZooKeeper: Wait-free Coordination for Internet-scale Systems." USENIX annual technical conference. Vol. 8. No. 9. 2010.
[40] Cachin, Christian. "Architecture of the hyperledger blockchain fabric." Workshop on distributed cryptocurrencies and consensus ledgers. Vol. 310. 2016.
[41] Merkel, Dirk. "Docker: lightweight linux containers for consistent development and deployment." Linux journal 2014.239 (2014): 2.
[42] Borthakur, Dhruba. "HDFS architecture guide." Hadoop Apache Project 53.1-13 (2008): 2.
[43] Vaidyanathan S.  3-cells cellular neural network (CNN) attractor and its adaptive biological control [J]. International Journal of PharmTech Research, 2015, 8 (4): 632-640.
[44] Greff K, Srivastava R K, Koutník J, et al.  LSTM: A search space odyssey [J].  IEEE transactions on neural networks and learning systems, 2016, 28 (10): 2222-2232.
[45] Liu, Jinkun. Radial Basis Function (RBF) neural network control for mechanical systems: design, analysis and Matlab simulation. Springer Science & Business Media, 2013.
[46] Ivakhnenko, A. G., and G. A. Ivakhnenko. "The review of problems solvable by algorithms of the group method of data handling (GMDH)." Pattern Recognition and Image Analysis C/C of Raspoznavaniye Obrazov I Analiz Izobrazhenii 5 (1995): 527-535.
[47] Wilamowski, Bogdan M., and Hao Yu. "Improved computation for Levenberg–Marquardt training." IEEE transactions on neural networks 21.6 (2010): 930-937.