The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the...The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.展开更多
After many years of exploitation,onshore oil and gas resources are about to enter a recession period.Oil and gas will mainly come from oceans in the future.Generally speaking,the exploration and production(E&P)cos...After many years of exploitation,onshore oil and gas resources are about to enter a recession period.Oil and gas will mainly come from oceans in the future.Generally speaking,the exploration and production(E&P)cost of oil from offshore is much higher than that of oil from onshore,so it is more sensitive to oil price.However,in recent years,oil price has been hovering at a low level for a long time,almost close to or even lower than the E&P cost of oil,which directly affects the development of oilfields.Besides the influence of oil price,some oilfields present the characteristics of marginal reserve scale,short peak production period and output rapidly declining.There leads to short economic life period and makes the economic benefit close to or lower than oilfield’s hurdle rate,which increases the difficulty of offshore oilfield development.As an important part of oilfield development,Floating Production Storage and Offloading unit,its investment mode and rent mode directly affect overall oilfield’s rate of return and the economic life.This paper chooses lease mode as the research object based on the analysis of investment mode,and further puts forward rent mode related with oil price through the analysis of traditional rent mode,and illustrates the advantages and disadvantages of various rent modes and their applicability so that the lessor chooses the right mode to achieve Win-Win with Oil Company and promotes the development of oilfields under low oil price.展开更多
本文借鉴复合型电商平台的运营现实,以FBP(fulfilled by POP),即商家进驻电商平台后将产品运入平台仓库,商家负责产品销售,平台负责发货和售后,及SOP(sale on POP),即电商平台只提供一个销售平台,商家自主运营、发货、售后两种进驻模式...本文借鉴复合型电商平台的运营现实,以FBP(fulfilled by POP),即商家进驻电商平台后将产品运入平台仓库,商家负责产品销售,平台负责发货和售后,及SOP(sale on POP),即电商平台只提供一个销售平台,商家自主运营、发货、售后两种进驻模式商家和平台的竞合为参考,分别构建了平台自营或FBP模式、自营+FBP模式、自营+FBP+SOP模式下的在线渠道竞争模型。通过构建价格和服务同时影响消费者选择的效用函数,综合采用博弈论和最优化方法来确定不同模式下的参与者定价和服务决策,并借助算例分析进行深入研究。结果表明;无论何种模式,自己提供服务的商家和平台最优收益的实现都会受众多条件的影响,将服务交由平台提供的商家反而总能实现最优;进驻模式中参与者数量的增加会降低在线渠道的服务水平,但对于提高总的市场需求具有正向作用。此外,尽管自营或FBP模式能够为平台或FBP商家带来更大收益,但当消费者对FBP商家产品偏好较小,自营+FBP模式能够为在线渠道带来较自营模式更多的整体收益。展开更多
Electricity prices have complex features,such as high frequency,multiple seasonality,and nonlinearity.These factors will make the prediction of electricity prices difficult.However,accurate electricity price predictio...Electricity prices have complex features,such as high frequency,multiple seasonality,and nonlinearity.These factors will make the prediction of electricity prices difficult.However,accurate electricity price prediction is important for energy producers and consumers to develop bidding strategies.To improve the accuracy of prediction by using each algorithms’advantages,this paper proposes a hybrid model that uses the Empirical Mode Decomposition(EMD),Autoregressive Integrated Moving Average(ARIMA),and Temporal Convolutional Network(TCN).EMD is used to decompose the electricity prices into low and high frequency components.Low frequency components are forecasted by the ARIMA model and the high frequency series are predicted by the TCN model.Experimental results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland(PJM)electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.展开更多
基金supported by China Southern Power Grid Science and Technology Innovation Research Project(000000KK52220052).
文摘The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.
文摘After many years of exploitation,onshore oil and gas resources are about to enter a recession period.Oil and gas will mainly come from oceans in the future.Generally speaking,the exploration and production(E&P)cost of oil from offshore is much higher than that of oil from onshore,so it is more sensitive to oil price.However,in recent years,oil price has been hovering at a low level for a long time,almost close to or even lower than the E&P cost of oil,which directly affects the development of oilfields.Besides the influence of oil price,some oilfields present the characteristics of marginal reserve scale,short peak production period and output rapidly declining.There leads to short economic life period and makes the economic benefit close to or lower than oilfield’s hurdle rate,which increases the difficulty of offshore oilfield development.As an important part of oilfield development,Floating Production Storage and Offloading unit,its investment mode and rent mode directly affect overall oilfield’s rate of return and the economic life.This paper chooses lease mode as the research object based on the analysis of investment mode,and further puts forward rent mode related with oil price through the analysis of traditional rent mode,and illustrates the advantages and disadvantages of various rent modes and their applicability so that the lessor chooses the right mode to achieve Win-Win with Oil Company and promotes the development of oilfields under low oil price.
文摘本文借鉴复合型电商平台的运营现实,以FBP(fulfilled by POP),即商家进驻电商平台后将产品运入平台仓库,商家负责产品销售,平台负责发货和售后,及SOP(sale on POP),即电商平台只提供一个销售平台,商家自主运营、发货、售后两种进驻模式商家和平台的竞合为参考,分别构建了平台自营或FBP模式、自营+FBP模式、自营+FBP+SOP模式下的在线渠道竞争模型。通过构建价格和服务同时影响消费者选择的效用函数,综合采用博弈论和最优化方法来确定不同模式下的参与者定价和服务决策,并借助算例分析进行深入研究。结果表明;无论何种模式,自己提供服务的商家和平台最优收益的实现都会受众多条件的影响,将服务交由平台提供的商家反而总能实现最优;进驻模式中参与者数量的增加会降低在线渠道的服务水平,但对于提高总的市场需求具有正向作用。此外,尽管自营或FBP模式能够为平台或FBP商家带来更大收益,但当消费者对FBP商家产品偏好较小,自营+FBP模式能够为在线渠道带来较自营模式更多的整体收益。
基金supported by the Sichuan Science and Technology Program under Grant 2020JDJQ0037 and 2020YFG0312.
文摘Electricity prices have complex features,such as high frequency,multiple seasonality,and nonlinearity.These factors will make the prediction of electricity prices difficult.However,accurate electricity price prediction is important for energy producers and consumers to develop bidding strategies.To improve the accuracy of prediction by using each algorithms’advantages,this paper proposes a hybrid model that uses the Empirical Mode Decomposition(EMD),Autoregressive Integrated Moving Average(ARIMA),and Temporal Convolutional Network(TCN).EMD is used to decompose the electricity prices into low and high frequency components.Low frequency components are forecasted by the ARIMA model and the high frequency series are predicted by the TCN model.Experimental results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland(PJM)electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.
