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This paper takes a parallel learning approach for robust and transparent AI. A deep neural network is trained in parallel on multiple tasks, where each task is trained only on a subset of the network resources. Each subset consists of network segments, that can be combined and shared across specific tasks. Tasks can share resources with other tasks, while having independent task-related network resources. Therefore, the trained network can share similar representations across various tasks, while also enabling independent task-related representations. The above allows for some crucial outcomes. (1) The parallel nature of our approach negates the issue of catastrophic forgetting. (2) The sharing of segments uses network resources more efficiently. (3) We show that the network does indeed use learned knowledge from some tasks in other tasks, through shared representations. (4) Through examination of individual task-related and shared representations, the model offers transparency in the network and in the relationships across tasks in a multi-task setting. Evaluation of the proposed approach against complex competing approaches such as Continual Learning, Neural Architecture Search, and Multi-task learning shows that it is capable of learning robust representations. This is the first effort to train a DL model on multiple tasks in parallel. Our code is available at https://github.com/MahsaPaknezhad/PaRT

Most of the factors influencing multivariate regression prediction models are macroeconomic indicators, and indicators tend to have strong correlations. The regression prediction model has strong multicollinearity, which results in distortion or inaccurate estimation of model estimates. To solve this problem, this paper first analyzes the principal components of the indicators, extracts the two principal components, eliminates the multicollinearity between the indicators, and considers the policy background of the “Electrification of Xinjiang”, and finally establishes a mid-long term load forecasting model based on principal component regression. The principal component regression prediction model has a better prediction effect. After inspection, the prediction accuracy of this model is ideal.