No abstract

Influenced by climate change and urbanization, urban flood frequently occurs and represents a serious challenge for many cities. Therefore, it is necessary to generate refined predictions of urban floods, such as the prediction of water accumulation processes at water accumulation points, which is of great significance for supporting water-related managers to reduce flood losses. In this study, 16 combination schemes of rainfall sensitivity indicators were used to determine the optimal scheme for predicting the depth of accumulated water, and the gradient boosting decision tree (GBDT) algorithm in deep learning was used to build a prediction model of the accumulation process of urban stormy accumulation points. Among the 16 schemes, the relative error of scheme 1 is 15.39%, and the qualified rate is 92.86%. This scheme exhibits the highest accuracy for the prediction results of water accumulation depth. Given this finding, the GBDT algorithm was used to construct a regression prediction model of the water accumulation process based on the collected historical rainfall water accumulation data of 50 water accumulation points. The results demonstrated that the GBDT regression prediction model has a mean relative error of 19.77%, a qualified rate of 82.00%, and a peak average relative error of 5.48%, which verify the validity and applicability of the model for the real-time prediction of the process of water accumulation. INDEX TERMS Urban flood, Deep learning, Water accumulation, Real-time prediction.

As typical prosumers, commercial buildings equipped with electric vehicle (EV) charging piles and solar photovoltaic panels require an effective energy management method. However, the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online execution. To address this issue, a long short-term memory (LSTM) recurrent neural network (RNN) based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building prosumers. Under the proposed system control structure, the LSTM algorithm can be separated into offline and online stages. At the offline stage, the LSTM is used to map states (inputs) to decisions (outputs) based on the network training. At the online stage, once the current state is input, the LSTM can quickly generate a solution without any additional prediction. A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM network. The simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.

No abstract

No abstract