KEYWORDS: Buildings, Data modeling, Machine learning, System integration, Neural networks, Performance modeling, Linear regression, Statistical modeling, Integrated modeling, Education and training
As a new and efficient intelligent energy system, integrated energy system has been widely used. As more and more new buildings are incorporated into the system, accurate load forecasting is essential for the planning and operation of integrated energy systems. The historical data of new buildings incorporated into the energy management system is not enough to build accurate prediction models. Transfer learning, as a cross-domain learning method, has been applied in time series prediction. To solve the problem of negative transfer caused by fluctuation and randomness of load data, this paper presents a day-ahead power load forecasting model that combines transfer learning with ensemble learning. Firstly, a multivariate migration method based on data decomposition is proposed, which migrates the data of multiple buildings with high load similarity to enrich the historical data of the tar-get buildings and avoid negative transfer. Secondly, similar day and neural network integrated prediction models are presented to deal with the impact of different date types on prediction accuracy. Finally, the proposed model is validated by simulation experiments. The experimental results show that the proposed method achieves good prediction accuracy.
Internet of Vehicles (IoV) can effectively utilize vehicle dynamic information in the network system, provide vehicles services, and make traffic smoother. For the data sharing problem between different cryptosystems in IoV, an efficient and secure blockchain-based data sharing scheme for the IoV is proposed. Heterogeneous aggregated signcryption technology is used to realize secure communication between certificateless cryptography (CLC) and public key infrastructure (PKI). The outsourced computing operation of smart contracts based on blockchain greatly reduces the computing overhead of roadside units and on-board units, while improving the security of data sharing in the IoV. Furthermore, the analysis results show that the proposed scheme can guarantee the confidentiality and integrity of IoV data sharing and has high computational efficiency.
Smart grid collects data in real-time to accomplish load balancing and power resource allocation. However, the power grid data faces the security risk of being leaked or tampered with when it is transmitted through open channels. Based on blockchain and hybrid encryption, a secure grid data sharing scheme with hidden policy is proposed. The grid data is encrypted using a hybrid encryption technology of symmetric encryption and attribute-based encryption, which ensures the confidentiality and privacy of the grid data. The multi-attribute authorization mechanism is used to prevent collusion attacks, and the policy is hidden to ensure that private information is leaked. Additionally, the verification token is stored on the blockchain to complete message integrity verification. The analysis results show that this scheme has higher computational efficiency and can achieve secure sharing of grid data.
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