Academic Journal of Computing & Information Science, 2020, 3(1); doi: 10.25236/AJCIS.2020.030107.
Yubo Zhai1, 2, *, Xianghan Zheng1, 2, 4 and Songpu Ai3, 4
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: firstname.lastname@example.org
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.
Blockchain, Broad Learning, CNN+LSTM, Data Fusion, Energy Internet
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.
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