Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
In 2001 three earthquakes occurred in Shidian in Yunnan Province, which were the MS=5.2 on April 10, the MS=5.9 on April 12 and the MS=5.3 on June 8. Based on the data from the station Baoshan of Yunnan Telemetry Digi...In 2001 three earthquakes occurred in Shidian in Yunnan Province, which were the MS=5.2 on April 10, the MS=5.9 on April 12 and the MS=5.3 on June 8. Based on the data from the station Baoshan of Yunnan Telemetry Digital Seismograph Network, the variational characteristics of shear-wave splitting on these series of strong earthquakes has been studied by using the systematic analysis method (SAM) of shear-wave splitting. The result shows the time delays of shear-wave splitting basically increase with earthquake activity intensifying. However the time delays abruptly decrease immediately before strong aftershocks. It accords with the stress relaxation before earthquakes, which was found recently in study on shear-wave splitting. The result suggests it is significant for reducing the harm degree of earthquakes to develop the stress-forecasting on earthquake in strong active tectonic zones and economic developed regions or big cities under the danger of strong earthquakes.展开更多
During the Twelfth Five-Year plan,large-scale construction of smart grid with safe and stable operation requires a timely and accurate short-term load forecasting method.Moreover,along with the full-scale smart grid c...During the Twelfth Five-Year plan,large-scale construction of smart grid with safe and stable operation requires a timely and accurate short-term load forecasting method.Moreover,along with the full-scale smart grid construction,the power supply mode and consumption mode of the whole system can be optimized through the accurate short-term load forecasting;and the security,stability and cleanness of the system can be guaranteed.展开更多
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
基金National Natural Science Foundation of China (40274011 40074020) MOST (2001BA601B02) and Joint Seis-mological Science Foundation of China (102068).
文摘In 2001 three earthquakes occurred in Shidian in Yunnan Province, which were the MS=5.2 on April 10, the MS=5.9 on April 12 and the MS=5.3 on June 8. Based on the data from the station Baoshan of Yunnan Telemetry Digital Seismograph Network, the variational characteristics of shear-wave splitting on these series of strong earthquakes has been studied by using the systematic analysis method (SAM) of shear-wave splitting. The result shows the time delays of shear-wave splitting basically increase with earthquake activity intensifying. However the time delays abruptly decrease immediately before strong aftershocks. It accords with the stress relaxation before earthquakes, which was found recently in study on shear-wave splitting. The result suggests it is significant for reducing the harm degree of earthquakes to develop the stress-forecasting on earthquake in strong active tectonic zones and economic developed regions or big cities under the danger of strong earthquakes.
文摘During the Twelfth Five-Year plan,large-scale construction of smart grid with safe and stable operation requires a timely and accurate short-term load forecasting method.Moreover,along with the full-scale smart grid construction,the power supply mode and consumption mode of the whole system can be optimized through the accurate short-term load forecasting;and the security,stability and cleanness of the system can be guaranteed.
