Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models.Especially,we need the adequate model to foreca...Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models.Especially,we need the adequate model to forecast the maximum load duration based on time-of-use,which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid.However,the existing single machine learning or deep learning forecasting cannot easily avoid overfitting.Moreover,a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.To overcome these limitations,we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use.Experimental results indicate that this architecture could achieve the highest average of recall and accuracy(83.43%)compared to benchmark models.To verify the effectiveness of the architecture,another experimental result shows that energy storage system(ESS)scheme in accordance with the forecast results of the proposed model(LSTM-MATO)in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the method.Therefore,the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.展开更多
Global atmospheric and oceanic perturbations and local weather variability induced factors highly alter the rainfall pattern of a region. Such factors result in extreme events of devastating nature to mankind. Rainfal...Global atmospheric and oceanic perturbations and local weather variability induced factors highly alter the rainfall pattern of a region. Such factors result in extreme events of devastating nature to mankind. Rainfall Intensity Duration Frequency (IDF) is one of the most commonly used tools in water resources engineering particularly to identify design storm event of various magnitude, duration and return period simultaneously. In light of this, the present study is aimed at developing rainfall IDF relationship for entire Rwanda based on selected twenty six (26) rainfall gauging stations. The gauging stations have been selected based on reliable rainfall records representing the different geographical locations varying from 14 to 83 years of record length. Daily annual maximum rainfall data has been disaggregated into sub-daily values such as 0.5 hr, 1 hr, 3 hr, 6 hr and 12 hr and fitted to the probability distributions. Quantile estimation has been made for different return periods and best fit distribution is identified based on least square standard error of estimate. At-site and regional IDF parameters were computed and subsequent curves were established for different return period. The moment ratio diagram (MRD) and L-moment ratio diagram (LMRD) methods have been used to fit frequency distributions and identify homogeneous regions for observed 24-hr maximum annual rainfall. The rainfall stations have been divided into five homogeneous rainfall regions for all 26 stations. The results of present analysis can be used as useful information for future water resources development planning purposes.展开更多
This study aims at establishing if climate change exists in the Niger Delta environment using non-stationary rainfall Intensity-Duration-Frequency (IDF) modelling incorporating time-variant parameters. To compute the ...This study aims at establishing if climate change exists in the Niger Delta environment using non-stationary rainfall Intensity-Duration-Frequency (IDF) modelling incorporating time-variant parameters. To compute the intensity levels, the open-access R-studio software was used based on the General Extreme Value (GEV) distribution function. Among the four linear parameter models adopted for integrating time as a covariate, the fourth linear model incorporating scale and location with the shape function constant produced the least corrected Akaike Information Criteria (AICc), varying between 306.191 to 101.497 for 15 and 1440 minutes, respectively, selected for calibration of the GEV distribution equation. The non-stationary intensities yielded higher values above those of stationary models, proving that the assumption of stationary IDF models underestimated extreme events. The difference of 13.71 mm/hr (22.71%) to 14.26 mm/hr (17.0%) intensities implies an underestimation of the peak flood from a stationary IDF curve. The statistical difference at a 95% confidence level between stationary and non-stationary models was significant, confirming evidence of climatic change influenced by time-variant parameters. Consequently, emphasis should be on applying shorter-duration storms for design purposes occurring with higher intensities to help reduce the flood risk and resultant infrastructural failures.展开更多
目的:探讨持续性心房颤动(房颤)导管射频消融术后P波时限及离散度与术后早期与晚期复发房颤的关系。方法:连续入选80例首次行导管射频消融术的持续性房颤患者,其中男性46例,女性34例,平均年龄(60.6±8.1)岁,平均随访(9.3±2.9)...目的:探讨持续性心房颤动(房颤)导管射频消融术后P波时限及离散度与术后早期与晚期复发房颤的关系。方法:连续入选80例首次行导管射频消融术的持续性房颤患者,其中男性46例,女性34例,平均年龄(60.6±8.1)岁,平均随访(9.3±2.9)个月。根据3个月之内(早期)和之外(晚期)复发房颤情况,分为两个对比组:早期复发组和早期未复发组;晚期复发组和晚期未复发组。所有患者术后24 h内均记录12导联心电图,并测量P波时限,计算出最长P波时限(Pmax),最短P波时限(Pmin),以及两值之差即P波离散度(Pd),分析P波时限及离散度与术后早期与晚期复发房颤的关系。结果:17例患者早期复发房颤,与早期未复发组患者比较,Pd明显增长[(74.35±17.78) ms vs (60.73±18.37) ms,P=0.008];12例早期复发患者(70.6%)出现延迟恢复,未发现早期复发为晚期复发预测因子。14例患者晚期复发房颤,与晚期未复发组患者相比,Pd明显增长[(75.71±16.49)ms vs (61.06±18.59) ms,P=0.008]。通过观察不同临界值,发现Pd≥60 ms对预测术后晚期复发房颤有一定价值,敏感度85%、特异度50%、阳性预测值26.7%、阴性预测值94.3%。结论:Pd与持续性房颤导管射频消融术后早期及晚期复发有关,其中Pd≥60 ms对预测持续性房颤术后晚期复发有一定的价值,其中阴性预测价值更高。展开更多
基金supported by Institute for Information&communications Technology Planning&Evaluation(IITP)funded by the Korea government(MSIT)(No.2019-0-01343,Training Key Talents in Industrial Convergence Security)Research Cluster Project,R20143,by Zayed University Research Office.
文摘Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models.Especially,we need the adequate model to forecast the maximum load duration based on time-of-use,which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid.However,the existing single machine learning or deep learning forecasting cannot easily avoid overfitting.Moreover,a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.To overcome these limitations,we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use.Experimental results indicate that this architecture could achieve the highest average of recall and accuracy(83.43%)compared to benchmark models.To verify the effectiveness of the architecture,another experimental result shows that energy storage system(ESS)scheme in accordance with the forecast results of the proposed model(LSTM-MATO)in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the method.Therefore,the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.
文摘Global atmospheric and oceanic perturbations and local weather variability induced factors highly alter the rainfall pattern of a region. Such factors result in extreme events of devastating nature to mankind. Rainfall Intensity Duration Frequency (IDF) is one of the most commonly used tools in water resources engineering particularly to identify design storm event of various magnitude, duration and return period simultaneously. In light of this, the present study is aimed at developing rainfall IDF relationship for entire Rwanda based on selected twenty six (26) rainfall gauging stations. The gauging stations have been selected based on reliable rainfall records representing the different geographical locations varying from 14 to 83 years of record length. Daily annual maximum rainfall data has been disaggregated into sub-daily values such as 0.5 hr, 1 hr, 3 hr, 6 hr and 12 hr and fitted to the probability distributions. Quantile estimation has been made for different return periods and best fit distribution is identified based on least square standard error of estimate. At-site and regional IDF parameters were computed and subsequent curves were established for different return period. The moment ratio diagram (MRD) and L-moment ratio diagram (LMRD) methods have been used to fit frequency distributions and identify homogeneous regions for observed 24-hr maximum annual rainfall. The rainfall stations have been divided into five homogeneous rainfall regions for all 26 stations. The results of present analysis can be used as useful information for future water resources development planning purposes.
文摘This study aims at establishing if climate change exists in the Niger Delta environment using non-stationary rainfall Intensity-Duration-Frequency (IDF) modelling incorporating time-variant parameters. To compute the intensity levels, the open-access R-studio software was used based on the General Extreme Value (GEV) distribution function. Among the four linear parameter models adopted for integrating time as a covariate, the fourth linear model incorporating scale and location with the shape function constant produced the least corrected Akaike Information Criteria (AICc), varying between 306.191 to 101.497 for 15 and 1440 minutes, respectively, selected for calibration of the GEV distribution equation. The non-stationary intensities yielded higher values above those of stationary models, proving that the assumption of stationary IDF models underestimated extreme events. The difference of 13.71 mm/hr (22.71%) to 14.26 mm/hr (17.0%) intensities implies an underestimation of the peak flood from a stationary IDF curve. The statistical difference at a 95% confidence level between stationary and non-stationary models was significant, confirming evidence of climatic change influenced by time-variant parameters. Consequently, emphasis should be on applying shorter-duration storms for design purposes occurring with higher intensities to help reduce the flood risk and resultant infrastructural failures.
文摘目的:探讨持续性心房颤动(房颤)导管射频消融术后P波时限及离散度与术后早期与晚期复发房颤的关系。方法:连续入选80例首次行导管射频消融术的持续性房颤患者,其中男性46例,女性34例,平均年龄(60.6±8.1)岁,平均随访(9.3±2.9)个月。根据3个月之内(早期)和之外(晚期)复发房颤情况,分为两个对比组:早期复发组和早期未复发组;晚期复发组和晚期未复发组。所有患者术后24 h内均记录12导联心电图,并测量P波时限,计算出最长P波时限(Pmax),最短P波时限(Pmin),以及两值之差即P波离散度(Pd),分析P波时限及离散度与术后早期与晚期复发房颤的关系。结果:17例患者早期复发房颤,与早期未复发组患者比较,Pd明显增长[(74.35±17.78) ms vs (60.73±18.37) ms,P=0.008];12例早期复发患者(70.6%)出现延迟恢复,未发现早期复发为晚期复发预测因子。14例患者晚期复发房颤,与晚期未复发组患者相比,Pd明显增长[(75.71±16.49)ms vs (61.06±18.59) ms,P=0.008]。通过观察不同临界值,发现Pd≥60 ms对预测术后晚期复发房颤有一定价值,敏感度85%、特异度50%、阳性预测值26.7%、阴性预测值94.3%。结论:Pd与持续性房颤导管射频消融术后早期及晚期复发有关,其中Pd≥60 ms对预测持续性房颤术后晚期复发有一定的价值,其中阴性预测价值更高。
