Accurate and effective power system load forecasting is an important prerequisite for the safe and stable operation of the power grid and the normal production and operation of society. In recent years, convolutional neural networks (CNNs) have been widely used in time series prediction due to their parallel computing and other characteristics, but it is difficult for CNNs to capture the relationship of sequence context and meanwhile, it easily leads to information leakage. To avoid the drawbacks of CNNs, we adopt a temporal convolutional network (TCN), specially designed for time series. TCN combines causal convolution, dilated convolution, and residual connection, and fully considers the causal correlation between historical data and future data. Considering that the power load data has strong periodicity and is greatly influenced by seasons and holidays, we adopt the Prophet model to decompose the load data and fit the trend component, season component, and holiday component. We use TCN and Prophet to forecast the power load data respectively, and then use the least square method to fuse the two models, and make use of their respective advantages to improve the forecasting accuracy. Experiments show that the proposed TCN-Prophet model has a higher prediction accuracy than the classic ARIMA, RNN, LSTM, GRU, and some ensemble models, and can provide more effective decision-making references for power grid departments.

The lithium-ion battery plays an increasingly im-portant role in energy storage and electric vehicles with the development of renewable energy. To ensure the safe operation of the lithium-ion battery system in application, the battery management system (BMS) is often equipped to monitor the battery cells in real-time. However, due to the inaccuracy and high cost, the traditional BMS is difficult to be adapted to the large-scale lithium-ion battery system, which will be increasingly common in the future. To overcome these two shortcomings, a cloud-end collaboration BMS framework is proposed in this study, which takes advantage of cloud computing and machine learning to monitor the numerous battery cells. In addition, based on this cloud-end collaboration BMS framework, the gated recurrent unit neural network and transfer learning algorithm are used to estimate the state-of-charge of different batteries. And the accuracy and flexibility of the method have been validated through experiments.