A precise short-term load-forecasting model is vital for energy companies to create accurate supply plans to reduce carbon dioxide production, causing our lives to be more environmentally friendly. A variety of high-voltage-level load-forecasting approaches, such as linear regression (LR), autoregressive integrated moving average (ARIMA), and artificial neural network (ANN) models, have been proposed in recent decades. However, unlike load forecasting in high-voltage transmission systems, load forecasting at the distribution network level is more challenging since distribution networks are more variable and nonstationary. Moreover, existing load-forecasting models only consider the features of the time domain, while the demand load is highly correlated to the frequency-domain information. This paper introduces a robust wavelet transform neural network load-forecasting model. The proposed model utilizes both time- and frequency-domain information to improve the model's prediction accuracy. Firstly, three wavelet transform methods, variational mode decomposition (VMD), empirical mode decomposition (EMD), and empirical wavelet transformation (EWT), were introduced to transform the time-domain demand load data into frequency-domain data. Then, neural network models were trained to predict all components simultaneously. Finally, all the predicted data were aggregated to form the predicted demand load. Three cases were simulated in the case study stage to evaluate the prediction accuracy under different layer numbers, weather information, and neural network types. The simulation results showed that the proposed robust time–frequency load-forecasting model performed better than the traditional time-domain forecasting models based on the comparison of the performance metrics, including the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE).

Underground cable is one of the main components of urban power supply systems. However, it is often damaged by large machinery in the process of urban road construction. To protect the underground cable, this paper proposes a novel design of a smart vibration device based on a complex embedded system, including the STM32 control chip, vibration sensors, NB-IoT communication module, and charging module. Through vibration signal detection of the excavator and other large-scale mechanical behavior, the vibration device can indicate the direction where the damage is occurring, and send out a warning message to the operation center. Finally, some experiments for vibration monitoring devices are conducted to verify their effectiveness and future application potential prospects.

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