Short-term load forecasting (STLF) is crucial for intelligent energy and power scheduling. The time series of power load exhibits high volatility and complexity in its components (typically seasonality, trend, and residuals), which makes forecasting a challenge. To reduce the volatility of the power load sequence and fully explore the important information within it, a three-stage short-term power load forecasting model based on CEEMDAN-TGA is proposed in this paper. Firstly, the power load dataset is divided into the following three stages: historical data, prediction data, and the target stage. The CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) decomposition is applied to the first- and second-stage load sequences, and the reconstructed intrinsic mode functions (IMFs) are classified based on their permutation entropies to obtain the error for the second stage. After that, the TCN (temporal convolutional network), GRU (gated recurrent unit), and attention mechanism are combined in the TGA model to predict the errors for the third stage. The third-stage power load sequence is predicted by employing the TGA model in conjunction with the extracted trend features from the first and second stages, as well as the seasonal impact features. Finally, it is merged with the error term. The experimental results show that the forecast performance of the three-stage forecasting model based on CEEMDAN-TGA is superior to those of the TCN-GRU and TCN-GRU-Attention models, with a reduction of 42.77% in MAE, 46.37% in RMSE, and 45.0% in MAPE. In addition, the R2 could be increased to 0.98. It is evident that utilizing CEEMDAN for load sequence decomposition reduces volatility, and the combination of the TCN and the attention mechanism enhances the ability of GRU to capture important information features and assign them higher weights. The three-stage approach not only predicts the errors in the target load sequence, but also extracts trend features from historical load sequences, resulting in a better overall performance compared to the TCN-GRU and TCN-GRU-Attention models.

针对煤矿辅助运输人工调度处理突发事件不够及时,智能化程度低等问题,设计了一款煤矿辅助运输智能调度平台.首先根据煤矿实际情况,搭建辅助运输平台架构;然后结合UWB定位技术,实时定位辅助运输设备;使用特定接口协议传输所收集到的数据,平台处理分析后,得到有效数据,通过TCP/IP协议,对整个矿井的辅助运输设备及物料进行调度.该平台实现了井上智能调度井下设备运输物料、井上调度室实时监测井下物料、人员及设备的状态、设备数据统计分析、数字化管理.

针对煤矿井下环境复杂、电机车运输过程中有线监控部署不便、成本高、线路易损坏等问题,基于ZigBee、WiFi等无线通信技术,构建了无线异构通信网络.首先搭建了无线异构通信网络架构,在该架构基础上设计了电机车远程监测和控制系统,包括系统硬件连接组成和软件功能组成.对系统监测模块进行电机车的环境检测和车载监控无线传输测试,结果表明电机车监测和控制系统可实现电机车环境监测和监控视频无线传输,保证了电机车安全、可靠和高效的运输.