This paper proposes an intelligent Deep Learning (DL) based approach for Data-Driven Security-Constrained Unit Commitment (DD-SCUC) decision-making. The proposed approach includes data pre-processing and a two-stage decision-making process. Firstly, historical data is accumulated and pre-processed. Then, the DD-SCUC model is created based on the Gated Recurrent Unit-Neural Network (GRU-NN). The mapping model between system daily load and decision results is created by training the DL model with historical data and then is utilized to make SCUC decisions. The two-stage decisionmaking process outputs the decision results based on various applications and scenarios. This approach has self-learning capabilities because the accumulation of historical data sets can revise the mapping model and therefore improve its accuracy. Simulation results from the IEEE 118-bus test system and a real power system from China showed that compared with deterministic Physical-Model-Driven (PMD)-SCUC methods, the approach has higher accuracy, better efficiency in the practical use case, and better adaptability to different types of SCUC problems.

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In the field of information security, privacy protection based on machine learning is currently a hot topic. Combining differential privacy protection with AdaBoost, a machine learning ensemble classification algorithm, this paper proposes a scheme under differential privacy named CART-DPsAdaBoost (CART-Differential privacy structure of AdaBoost). In the process of boosting, the algorithm combines the idea of bagging, and uses a classification and regression tree (CART) stump as the base learner for ensemble learning. Applying feature perturbation, based on a random subspace algorithm, the exponential mechanism is used to select the splitting point for continuous attributes. We use the Gini index to find the optimal binary partitioning point for discrete attributes and add noise according to the Laplace mechanism. Throughout the process, a privacy budget is allocated in order to meet the appropriate differential privacy protection needs for the current application. Unlike similar algorithms, this method does not require discretization during preprocessing of the data. Experimental results with the Census Income, Digit Recognizer, and Adult Data Set show that while protecting private information, the scheme has little impact on classification accuracy and can effectively address large-scale and high-dimensional data classification problems.

There is a problem that the amount of users’ preference data on the mobile is small, and users are unwilling to disclose the preference data for the recommendation system about mobile users, so the server can’t centrally train a large amount of users’ preference data for a personalized recommendation. This paper proposes a personalized federated matrix factorization algorithm by introducing a federated matrix factorization model. The algorithm introduces users’ and items’ biases to modify the predictive rating model on each mobile; At the same time, conformity is introduced to give different weights to the preference data. In the case that the preference data does not leave the mobile, but the user preference data of the multiparty mobiles is shared, the multi-party mobiles and the server jointly train personalized matrix factorization model. The experimental results show that the algorithm in this paper still has high recommendation accuracy under the premise of correct updating in the federated matrix factorization model that uses bias and conformity.