Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network (BP-ANN) based method for short-term load forecasting is presented ...Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network (BP-ANN) based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid (China) based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.展开更多
This paper describes an application of combined model of extrapolation and correlation techniques for short term load forecasting of an Indian substation. Here effort has been given to improvise the accuracy of elec-t...This paper describes an application of combined model of extrapolation and correlation techniques for short term load forecasting of an Indian substation. Here effort has been given to improvise the accuracy of elec-trical load forecasting considering the factors, past data of the load, respective weather condition and finan-cial growth of the people. These factors are derived by curve fitting technique. Then simulation has been conducted using MATLAB tools. Here it has been suggested that consideration of 20 years data for a devel-oping country should be ignored as the development of a country is highly unpredictable. However, the im-portance of the past data should not be ignored. Here, just previous five years data are used to determine the above factors.展开更多
In this paper, a technical review with recent advances of the microstrip antennas loaded with shorting posts is presented. The overall size of the antenna is significantly reduced by a single shorting posts and the ef...In this paper, a technical review with recent advances of the microstrip antennas loaded with shorting posts is presented. The overall size of the antenna is significantly reduced by a single shorting posts and the effect of the various parameters of shorting posts on short-circuit microstrip antenna is also discussed.展开更多
The seismic ductility of reinforced very-high-strength-concrete (VHSC) short columns was studied by combinatively applying axial load and low cyclic lateral load on specimens to simulate seismic impact. Twelve specime...The seismic ductility of reinforced very-high-strength-concrete (VHSC) short columns was studied by combinatively applying axial load and low cyclic lateral load on specimens to simulate seismic impact. Twelve specimens with concrete compressive strength ranging from 95.6 MPa to 118.6 MPa and a shear-span ratio of 2.0 were tested for shear failure pattern and fear force-displacement hysteretic responses. Combinative application of axial load and low cyclic lateral load to VHSC short columns incurs shear failure. The displacement ductility is much smaller when the axial load ratio is larger;whereas a larger stirrup ratio is accompanied with a better displacement ductility. The relationship of displacement ductility factor, μ?, with stirrup characteristic value, λv, and test axial load ratio, nt, is μ?=(1+8λv)/(0.33+nt). By this relationship and relevant codes for aseismatic design, the axial load ratio limits for aseismatic design of reinforced VHSC (C95 to C100) short columns for frame construction are respectively 0.5, 0.6, and 0.7 for seismic classes I, II, and Ⅲ;corresponding minimum characteristic values of stirrups are calculated according to the required characteristic values of at least 1.273 times of experimental results. These data are very useful to aseismatic engineering.展开更多
Short-term load forecasting is a basis of power system dispatching and operation. In order to improve the short term power load precision, a novel approach for short-term load forecasting is presented based on local m...Short-term load forecasting is a basis of power system dispatching and operation. In order to improve the short term power load precision, a novel approach for short-term load forecasting is presented based on local mean decomposition (LMD) and the radial basis function neural network method (RBFNN). Firstly, the decomposition of LMD method based on characteristics of load data then the decomposed data are respectively predicted by using the RBF network model and predicted by using the BBO-RBF network model. The simulation results show that the RBF network model optimized by using BBO algorithm is optimized in error performance index, and the prediction accuracy is higher and more effective.展开更多
The wavelet power system short term load forecasting(STLF) uses a mulriple periodical autoregressive integrated moving average(MPARIMA) model to model the mulriple near periodicity, nonstationarity and nonlinearity ex...The wavelet power system short term load forecasting(STLF) uses a mulriple periodical autoregressive integrated moving average(MPARIMA) model to model the mulriple near periodicity, nonstationarity and nonlinearity existed in power system short term quarter hour load time series, and can therefore accurately forecast the quarter hour loads of weekdays and weekends, and provide more accurate results than the conventional techniques, such as artificial neural networks and autoregressive moving average(ARMA) models test results. Obtained with a power system networks in a city in Northeastern part of China confirm the validity of the approach proposed.展开更多
The fuzzy neural network is applied to the short-term load forecasting. The fuzzy rules and fuzzy membership functions of the network are obtained through fuzzy neural network learming. Three inference algorithms, i.e...The fuzzy neural network is applied to the short-term load forecasting. The fuzzy rules and fuzzy membership functions of the network are obtained through fuzzy neural network learming. Three inference algorithms, i.e. themultiplicative inference, the maximum inference and the minimum inference, are used for comparison. The learningalgorithms corresponding to the inference methods are derived from back-propagation algorithm. To validate the fuzzyneural network model, the network is used to Predict short-term load by compaing the network output against the realload data from a local power system supplying electricity to a large steel manufacturer. The experimental results aresatisfactory.展开更多
Electric load forecasting is essential for developing a power supply strategy to improve the reliability of the ac power line data network and provide optimal load scheduling for developing countries where the demand ...Electric load forecasting is essential for developing a power supply strategy to improve the reliability of the ac power line data network and provide optimal load scheduling for developing countries where the demand is increased with high growth rate. In this paper, a short-term load forecasting realized by a generalized neuron–wavelet method is proposed. The proposed method consists of wavelet transform and soft computing technique. The wavelet transform splits up load time series into coarse and detail components to be the features for soft computing techniques using Generalized Neurons Network (GNN). The soft computing techniques forecast each component separately. The modified GNN performs better than the traditional GNN. At the end all forecasted components is summed up to produce final forecasting load.展开更多
An accurate short-term forecasting method for load of electric power system can help the electric power system’s operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis ...An accurate short-term forecasting method for load of electric power system can help the electric power system’s operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis function (RBF) neural network method to forecast the short-term load of electric power system. To demonstrate the effectiveness of the proposed method, the method is tested on the practical load data information of the Tai power system. The good agreements between the realistic values and forecasting values are obtained;the numerical results show that the proposed forecasting method is accurate and reliable.展开更多
This paper presents a wavelet neural network (WNN) model combining wavelet transform and artificial neural networks for short term load forecast (STLF). Both historical load and temperature data having important impac...This paper presents a wavelet neural network (WNN) model combining wavelet transform and artificial neural networks for short term load forecast (STLF). Both historical load and temperature data having important impacts on load level were used in the proposed forecasting model. The model used the three-layer feed forward network trained by the error back-propagation algorithm. To enhance the forecast- ing accuracy by neural networks, wavelet multi-resolution analysis method was introduced to pre-process these data and reconstruct the predicted output. The proposed model has been evaluated with actual data of electricity load and temperature of Hunan Province. The simulation results show that the model is capable of providing a reasonable forecasting accuracy in STLF.展开更多
The glass fiber reinforced polymer (GFRP) tube is an effective material that can increase the bearing capacity and ductility of concrete.To study the mechanical behavior of this composite structure,twenty-one concrete...The glass fiber reinforced polymer (GFRP) tube is an effective material that can increase the bearing capacity and ductility of concrete.To study the mechanical behavior of this composite structure,twenty-one concrete-filled GFRP tubular short columns were tested under an eccentric load.The principle influencing factors,such as the eccentricity ratio,concrete strength and ratio of longitudinal reinforcement were also studied.In addition,the course of deformation,failure mode,and failure mechanism were analyzed by observing the phenomena and summarizing the data.The test results indicated that the strength and deformation characteristics of core concrete increase as a result of the addition of the GFRP tube.However,the gain in strength due to the addition of the GFRP tube decreases as the ratio of e /d increases.An increase in the longitudinal steel ratio can improve the bearing capacity of the composite short column effectively.Furthermore,the study showed that the constraint effect of the GFRP tube on high-strength concrete is not as effective as that on common concrete.The reason is that the lateral deformation of the high-strength concrete is less than that of the common concrete when the concrete column was tested under the same axial compression ratio.展开更多
In this article, we are initiating the hypothesis that improvements in short term energy load forecasting may rely on inclusion of data from new information sources generated outside the power grid and weather related...In this article, we are initiating the hypothesis that improvements in short term energy load forecasting may rely on inclusion of data from new information sources generated outside the power grid and weather related systems. Other relevant domains of data include scheduled activities on a grid, large events and conventions in the area, equipment duty cycle schedule, data from call centers, real-time traffic, Facebook, Twitter, and other social networks feeds, and variety of city or region websites. All these distributed data sources pose information collection, integration and analysis challenges. Our approach is concentrated on complex non-cyclic events detection where detected events have a human crowd magnitude that is influencing power requirements. The proposed methodology deals with computation, transformation, modeling, and patterns detection over large volumes of partially ordered, internet based streaming multimedia signals or text messages. We are claiming that traditional approaches can be complemented and enhanced by new streaming data inclusion and analyses, where complex event detection combined with Webbased technologies improves short term load forecasting. Some preliminary experimental results, using Gowalla social network dataset, confirmed our hypothesis as a proof-of-concept, and they paved the way for further improvements by giving new dimensions of short term load forecasting process in a smart grid.展开更多
Accurate load prediction plays an important role in smart power management system, either for planning, facing the increasing of load demand, maintenance issues, or power distribution system. In order to achieve a rea...Accurate load prediction plays an important role in smart power management system, either for planning, facing the increasing of load demand, maintenance issues, or power distribution system. In order to achieve a reasonable prediction, authors have applied and compared two features extraction technique presented by kernel partial least square regression and kernel principal component regression, and both of them are carried out by polynomial and Gaussian kernels to map the original features’ to high dimension features’ space, and then draw new predictor variables known as scores and loadings, while kernel principal component regression draws the predictor features to construct new predictor variables without any consideration to response vector. In contrast, kernel partial least square regression does take the response vector into consideration. Models are simulated by three different cities’ electric load data, which used historical load data in addition to weekends and holidays as common predictor features for all models. On the other hand temperature has been used for only one data as a comparative study to measure its effect. Models’ results evaluated by three statistic measurements, show that Gaussian Kernel Partial Least Square Regression offers the more powerful features and significantly can improve the load prediction performance than other presented models.展开更多
Short carbon fiber preform reinforced geopolymer composites containing different contents of α-Al2O3 filler (Cf(α-Al2O3)/geopolymer composites) were fabricated,and the effects of heat treatment temperatures up to 1 ...Short carbon fiber preform reinforced geopolymer composites containing different contents of α-Al2O3 filler (Cf(α-Al2O3)/geopolymer composites) were fabricated,and the effects of heat treatment temperatures up to 1 200 ℃ on the thermal-mechanical properties were studied. The results show that the thermal shrinkage in the direction perpendicular to the lamination of the composites gradually increases with the increase of the heat treatment temperatures from room temperature (25 ℃) to 1 000 ℃. However,the composites in the direction parallel to the lamination show an expansion behavior. Beyond 1 000 ℃,in the two directions the composites exhibit a larger degree of shrinkage due to the densification and crystallization. The mechanical properties of the composites show the minimum values in the temperature range from 600 to 800 ℃ as the hydration water of geopolymer matrix is lost. The addition of α-Al2O3 particle filler into the composites clearly increases the onset crystalline temperature of leucite (KAlSi2O6) from the amorphous geopolymer matrix. In addition,the addition of α-Al2O3 particles into the composites can not only help to keep volume stable at high temperatures but also effectively improve the mechanical properties of the composites subjected to thermal load to a certain extent. The main toughening mechanisms of the composites subjected to thermal load are attributed to fiber pulling-out.展开更多
针对分布式电源和新型负荷容量累积造成负荷影响因素多元化和不确定性特性增强的问题,文中提出一种采用记忆神经网络和曲线形状修正的负荷预测方法。在负荷峰值预测中,采用最大信息系数计算负荷峰值与影响因素的非线性相关性,实现对输...针对分布式电源和新型负荷容量累积造成负荷影响因素多元化和不确定性特性增强的问题,文中提出一种采用记忆神经网络和曲线形状修正的负荷预测方法。在负荷峰值预测中,采用最大信息系数计算负荷峰值与影响因素的非线性相关性,实现对输入特征的筛选;综合考虑负荷峰值序列的长短期自相关性和输入特征与负荷峰值的不同程度相关性,结合Attention机制和双向长短时记忆(bidirectional long short-term memory,BiLSTM)神经网络建立负荷峰值预测模型。在负荷标幺曲线预测中,通过误差倒数法组合相似日和相邻日,建立负荷标幺曲线预测模型;针对预测偏差的非平稳特征,利用自适应噪声的完全集成经验模态分解和BiLSTM网络建立误差预测模型,对曲线形状进行修正。应用中国北方某城市的区域电网负荷数据为算例,验证了所提模型的有效性。展开更多
文摘Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network (BP-ANN) based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid (China) based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.
文摘This paper describes an application of combined model of extrapolation and correlation techniques for short term load forecasting of an Indian substation. Here effort has been given to improvise the accuracy of elec-trical load forecasting considering the factors, past data of the load, respective weather condition and finan-cial growth of the people. These factors are derived by curve fitting technique. Then simulation has been conducted using MATLAB tools. Here it has been suggested that consideration of 20 years data for a devel-oping country should be ignored as the development of a country is highly unpredictable. However, the im-portance of the past data should not be ignored. Here, just previous five years data are used to determine the above factors.
文摘In this paper, a technical review with recent advances of the microstrip antennas loaded with shorting posts is presented. The overall size of the antenna is significantly reduced by a single shorting posts and the effect of the various parameters of shorting posts on short-circuit microstrip antenna is also discussed.
基金the key project of the National Natural Science Foundation of China (No.50438010)
文摘The seismic ductility of reinforced very-high-strength-concrete (VHSC) short columns was studied by combinatively applying axial load and low cyclic lateral load on specimens to simulate seismic impact. Twelve specimens with concrete compressive strength ranging from 95.6 MPa to 118.6 MPa and a shear-span ratio of 2.0 were tested for shear failure pattern and fear force-displacement hysteretic responses. Combinative application of axial load and low cyclic lateral load to VHSC short columns incurs shear failure. The displacement ductility is much smaller when the axial load ratio is larger;whereas a larger stirrup ratio is accompanied with a better displacement ductility. The relationship of displacement ductility factor, μ?, with stirrup characteristic value, λv, and test axial load ratio, nt, is μ?=(1+8λv)/(0.33+nt). By this relationship and relevant codes for aseismatic design, the axial load ratio limits for aseismatic design of reinforced VHSC (C95 to C100) short columns for frame construction are respectively 0.5, 0.6, and 0.7 for seismic classes I, II, and Ⅲ;corresponding minimum characteristic values of stirrups are calculated according to the required characteristic values of at least 1.273 times of experimental results. These data are very useful to aseismatic engineering.
文摘Short-term load forecasting is a basis of power system dispatching and operation. In order to improve the short term power load precision, a novel approach for short-term load forecasting is presented based on local mean decomposition (LMD) and the radial basis function neural network method (RBFNN). Firstly, the decomposition of LMD method based on characteristics of load data then the decomposed data are respectively predicted by using the RBF network model and predicted by using the BBO-RBF network model. The simulation results show that the RBF network model optimized by using BBO algorithm is optimized in error performance index, and the prediction accuracy is higher and more effective.
文摘The wavelet power system short term load forecasting(STLF) uses a mulriple periodical autoregressive integrated moving average(MPARIMA) model to model the mulriple near periodicity, nonstationarity and nonlinearity existed in power system short term quarter hour load time series, and can therefore accurately forecast the quarter hour loads of weekdays and weekends, and provide more accurate results than the conventional techniques, such as artificial neural networks and autoregressive moving average(ARMA) models test results. Obtained with a power system networks in a city in Northeastern part of China confirm the validity of the approach proposed.
文摘The fuzzy neural network is applied to the short-term load forecasting. The fuzzy rules and fuzzy membership functions of the network are obtained through fuzzy neural network learming. Three inference algorithms, i.e. themultiplicative inference, the maximum inference and the minimum inference, are used for comparison. The learningalgorithms corresponding to the inference methods are derived from back-propagation algorithm. To validate the fuzzyneural network model, the network is used to Predict short-term load by compaing the network output against the realload data from a local power system supplying electricity to a large steel manufacturer. The experimental results aresatisfactory.
文摘Electric load forecasting is essential for developing a power supply strategy to improve the reliability of the ac power line data network and provide optimal load scheduling for developing countries where the demand is increased with high growth rate. In this paper, a short-term load forecasting realized by a generalized neuron–wavelet method is proposed. The proposed method consists of wavelet transform and soft computing technique. The wavelet transform splits up load time series into coarse and detail components to be the features for soft computing techniques using Generalized Neurons Network (GNN). The soft computing techniques forecast each component separately. The modified GNN performs better than the traditional GNN. At the end all forecasted components is summed up to produce final forecasting load.
文摘An accurate short-term forecasting method for load of electric power system can help the electric power system’s operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis function (RBF) neural network method to forecast the short-term load of electric power system. To demonstrate the effectiveness of the proposed method, the method is tested on the practical load data information of the Tai power system. The good agreements between the realistic values and forecasting values are obtained;the numerical results show that the proposed forecasting method is accurate and reliable.
文摘This paper presents a wavelet neural network (WNN) model combining wavelet transform and artificial neural networks for short term load forecast (STLF). Both historical load and temperature data having important impacts on load level were used in the proposed forecasting model. The model used the three-layer feed forward network trained by the error back-propagation algorithm. To enhance the forecast- ing accuracy by neural networks, wavelet multi-resolution analysis method was introduced to pre-process these data and reconstruct the predicted output. The proposed model has been evaluated with actual data of electricity load and temperature of Hunan Province. The simulation results show that the model is capable of providing a reasonable forecasting accuracy in STLF.
文摘The glass fiber reinforced polymer (GFRP) tube is an effective material that can increase the bearing capacity and ductility of concrete.To study the mechanical behavior of this composite structure,twenty-one concrete-filled GFRP tubular short columns were tested under an eccentric load.The principle influencing factors,such as the eccentricity ratio,concrete strength and ratio of longitudinal reinforcement were also studied.In addition,the course of deformation,failure mode,and failure mechanism were analyzed by observing the phenomena and summarizing the data.The test results indicated that the strength and deformation characteristics of core concrete increase as a result of the addition of the GFRP tube.However,the gain in strength due to the addition of the GFRP tube decreases as the ratio of e /d increases.An increase in the longitudinal steel ratio can improve the bearing capacity of the composite short column effectively.Furthermore,the study showed that the constraint effect of the GFRP tube on high-strength concrete is not as effective as that on common concrete.The reason is that the lateral deformation of the high-strength concrete is less than that of the common concrete when the concrete column was tested under the same axial compression ratio.
文摘In this article, we are initiating the hypothesis that improvements in short term energy load forecasting may rely on inclusion of data from new information sources generated outside the power grid and weather related systems. Other relevant domains of data include scheduled activities on a grid, large events and conventions in the area, equipment duty cycle schedule, data from call centers, real-time traffic, Facebook, Twitter, and other social networks feeds, and variety of city or region websites. All these distributed data sources pose information collection, integration and analysis challenges. Our approach is concentrated on complex non-cyclic events detection where detected events have a human crowd magnitude that is influencing power requirements. The proposed methodology deals with computation, transformation, modeling, and patterns detection over large volumes of partially ordered, internet based streaming multimedia signals or text messages. We are claiming that traditional approaches can be complemented and enhanced by new streaming data inclusion and analyses, where complex event detection combined with Webbased technologies improves short term load forecasting. Some preliminary experimental results, using Gowalla social network dataset, confirmed our hypothesis as a proof-of-concept, and they paved the way for further improvements by giving new dimensions of short term load forecasting process in a smart grid.
文摘Accurate load prediction plays an important role in smart power management system, either for planning, facing the increasing of load demand, maintenance issues, or power distribution system. In order to achieve a reasonable prediction, authors have applied and compared two features extraction technique presented by kernel partial least square regression and kernel principal component regression, and both of them are carried out by polynomial and Gaussian kernels to map the original features’ to high dimension features’ space, and then draw new predictor variables known as scores and loadings, while kernel principal component regression draws the predictor features to construct new predictor variables without any consideration to response vector. In contrast, kernel partial least square regression does take the response vector into consideration. Models are simulated by three different cities’ electric load data, which used historical load data in addition to weekends and holidays as common predictor features for all models. On the other hand temperature has been used for only one data as a comparative study to measure its effect. Models’ results evaluated by three statistic measurements, show that Gaussian Kernel Partial Least Square Regression offers the more powerful features and significantly can improve the load prediction performance than other presented models.
基金Project supported by the Science Fund for Distinguished Young Scholars of Heilongjiang Province, ChinaProject supported by the Program for Excellent Team in Harbin Institute of Technology
文摘Short carbon fiber preform reinforced geopolymer composites containing different contents of α-Al2O3 filler (Cf(α-Al2O3)/geopolymer composites) were fabricated,and the effects of heat treatment temperatures up to 1 200 ℃ on the thermal-mechanical properties were studied. The results show that the thermal shrinkage in the direction perpendicular to the lamination of the composites gradually increases with the increase of the heat treatment temperatures from room temperature (25 ℃) to 1 000 ℃. However,the composites in the direction parallel to the lamination show an expansion behavior. Beyond 1 000 ℃,in the two directions the composites exhibit a larger degree of shrinkage due to the densification and crystallization. The mechanical properties of the composites show the minimum values in the temperature range from 600 to 800 ℃ as the hydration water of geopolymer matrix is lost. The addition of α-Al2O3 particle filler into the composites clearly increases the onset crystalline temperature of leucite (KAlSi2O6) from the amorphous geopolymer matrix. In addition,the addition of α-Al2O3 particles into the composites can not only help to keep volume stable at high temperatures but also effectively improve the mechanical properties of the composites subjected to thermal load to a certain extent. The main toughening mechanisms of the composites subjected to thermal load are attributed to fiber pulling-out.
文摘针对分布式电源和新型负荷容量累积造成负荷影响因素多元化和不确定性特性增强的问题,文中提出一种采用记忆神经网络和曲线形状修正的负荷预测方法。在负荷峰值预测中,采用最大信息系数计算负荷峰值与影响因素的非线性相关性,实现对输入特征的筛选;综合考虑负荷峰值序列的长短期自相关性和输入特征与负荷峰值的不同程度相关性,结合Attention机制和双向长短时记忆(bidirectional long short-term memory,BiLSTM)神经网络建立负荷峰值预测模型。在负荷标幺曲线预测中,通过误差倒数法组合相似日和相邻日,建立负荷标幺曲线预测模型;针对预测偏差的非平稳特征,利用自适应噪声的完全集成经验模态分解和BiLSTM网络建立误差预测模型,对曲线形状进行修正。应用中国北方某城市的区域电网负荷数据为算例,验证了所提模型的有效性。