Research on Innovation of Floating Production Storage and Offloading Unit’s Rent Mode under Low Oil Price

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.

Price prediction of power transformer materials based on CEEMD and GRU

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.

基于Price-Mueller模型的互联网公司员工离职影响因素研究

基于Price-Mueller离职模型,从外部变量、个体变量、工作特征变量和工作回报变量四个维度构建员工离职倾向影响因素的研究模型,并以国内互联网公司研发等核心岗位员工为对象进行问卷调查和实证研究。结果发现,在样本范围内,外部机会、工作自主性、薪酬对互联网公司核心员工的离职倾向产生显著的正向影响,亲属责任、工作压力、同事支持、领导支持、员工培训和晋升机会具有显著的负向影响,个人价值观的影响不显著。此外,年龄的大小、跳槽次数的多少和月收入水平的高低,使得互联网公司核心员工离职倾向呈现出显著的差异。为此,在外部环境、工作特征和工作回报等方面,互联网公司减弱核心员工离职倾向的人力资源管理优化策略与建议。

Oil Price Forecasting Based on EMD and BP_AdaBoost Neural Network

Empirical mode decomposition (EMD) and BP_AdaBoost neural network are used in this paper to model the oil price. Based on the benefits of these two methods, we predict the oil price by using them. To a certain extent, it effectively improves the accuracy of short-term price forecasting. Forecast results of this model are compared with the results of the ARIMA model, BP neural network and EMD-BP combined model. The experimental result shows that the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and Theil inequality (U) of EMD and BP_AdaBoost model are lower than other models, and the combined model has better prediction accuracy.

Goal and Progress of Power Tariff Reform——An interview with Liu Zhenqiu,Vice Director ofDepartment of Price,NDRC

● Power tariffreform has made many important and substantialprogresses in respect ofmarketization● The benchmark tariff is a revolutionary progress made in China's power tariff reform● Coal-electricity price linkage is a transitional approach which needs further improvement● The principle of T&D pricing is "cost plus profit," but it can also be based on pro rata control● China's electricity market should choose the mode of bilateral contracting combined withcentralized market● The direction of the power tariff reform is that electricity price shall be controlled mainly by the re-lationship between supply and demand in the

白条猪价格预测模型构建

【目的】增强农产品价格预测准确度,为农产品价格的有效预测提供参考。【方法】以河南省白条猪每周平均批发价格为研究对象,提出一种基于序列分解、主成分分析和神经网络(CEEMDAN-PCA-CNN-LSTM)的白条猪价格预测方法。首先,使用自适应白噪声完全集合模态分解方法(CEEMDAN)对白条猪价格序列进行分解;其次,选用皮尔逊相关系数筛选影响价格波动的相关因素;再次,利用主成分分析(PCA)对影响因素及分解得到的子序列降维处理并作为原始价格序列的特征值,并行输入到作为编码器的卷积神经网络(CNN)中进行特征提取;最后,引入长短期记忆网络(LSTM)作为解码器输出得到预测结果。将该方法应用于河南省白条猪每周平均价格数据,与LSTM、门控循环单元(GRU)、CNN、基于卷积的长短期记忆网络(ConvLSTM)模型进行比较。【结果】CEEMDAN-PCA-CNN-LSTM组合模型预测方法得到的平均绝对误差分别降低了44.95%、27.30%、28.13%、43.17%。【结论】CEEMDAN-PCA-CNN-LSTM模型对于河南省白条猪市场价格的预测性能更优,有助于相关部门针对河南省白条猪价格波动做出科学决策。

From Chinese Price to Chinese Value-Innovation does magic

Right after the spring festival,six batches of experts were appointed by China National Textile and Apparel Council respectively to conduct field- depth investigations in terms of textile trades and operations throughout the country.Indeed,the energy shortage this year,coupled with anti-dumping, appreciation of the renminbi,rising labor cost,raw materials prices as well as policy adjustments on export tax rebate and textile factories are posing harsher pressures on the whole industry,especially the export-oriented enterprises or the ones with OEM as their main product mode,so the price of things "made in China" are less competitive in international market, however,every way out comes from the forge of hard and bitter struggle, and at present,more and more enterprises realize that they have to get rid of low-level production,update their capability to enhance value of product and seek new spirit sustains, such as innovation to enhance their product

价格竞争、保鲜努力与农产品电商冷链物流模式演化

科学合理的冷链物流模式是确保农产品电商价格、新鲜度竞争力的关键。针对涉及两农产品电商、电商自营冷链物流和第三方冷链物流商的供应链,构建两电商竞争、第三方冷链物流商主导的Stackelberg博弈模型及两电商冷链物流模式选择的演化博弈模型,并通过仿真揭示交叉价格弹性、新鲜度需求弹性、自营和第三方保鲜投入成本系数对演化路径的影响。研究表明:若交叉价格弹性、新鲜度需求弹性和第三方保鲜投入成本系数分别增大,自营冷链物流为演化稳定策略,电商倾向自营冷链物流。若自营保鲜投入成本系数增大,第三方冷链物流为演化稳定策略,电商倾向与第三方冷链物流商合作。

An Analysis of China's Overall Price Trend During the 12th Five-year Plan Period and Suggestions

In this paper,the author first summarizes China’s price operation situation and characteristics since the 1990s,and thenanalyzes inflation shaping factors and types.Based on that,the author raises six inflation early-warning indexes andfully dissects influencing factors of the overall price trend and inflation risks during the 12th Five-year Plan period.Afterthat,the author explains some aspects of price fluctuation that warrants attention during the 12th Five-year Plan period.Finally,the author puts forward policies and suggestions for stabilizing the overall price during the transformation of theeconomic development mode based on our actual situation.

供应受限下考虑采购环节的订货-价格竞合决策模型

在上游供应受限的背景下,为保障供应链的有效运转,在品牌商采用“控制”采购模式的代工供应链中,引入一个存在产能限制及规模经济效应的供应商,构建制造商-品牌商的订货-价格决策模型(策略1)。以经典竞合模型为基础,建立制造商开通直销渠道的博弈模型(策略2)。通过对博弈模型进行分析,得到了供应结构中的均衡结果。结合解析和数值分析,说明了制造商接受极限高价采购(零部件)和搭便车采购两种情境下,竞合博弈的效果以及制造商的策略偏好。与以往模型不同,由于考虑了制造商和品牌商的零部件采购环节,从而形成了环形供应结构,并发现了以渠道交换订单的效应。研究结论可以为供应受限条件下制造商的转型升级提供建议。

北京市大白菜生产现状、存在问题及发展对策

为促进大白菜产业的健康发展,总结了北京地区大白菜生产现状,分析了存在的关键问题,并提出了进一步发展的建议。北京地区大白菜生产面积稳步增长,通过茬口搭配实现了周年生产与供应,栽培品种日益丰富,同时,农业机械化得到初步应用,有机生产进一步提升了种植收益,种植模式逐步优化;但也存在着茬口过于集中、品种类型结构单一、市场价格浮动较大及栽培技术不规范等问题;应通过深化品种结构调整、推行政策性农业保险、调动收储企业积极性、积极加强产销对接确保生产者效益,通过引进研发适用农业机械、加强技术集成研究与培训推广等促进大白菜产业高质量发展。

基于BOPPPS的线上线下混合教学模式在“工程计量与计价”中的应用

BOPPPS教学模式涵盖导言、目标、前测、参与式学习、后测和总结6个教学环节,充分遵循以目标为导向、以学生为主体的教学规律,高度重视学生的参与度。“工程计量与计价”是一门知识点综合性和实践性都较强的科目,针对传统教学中教学模式单一、学生兴趣点不高等问题,结合“工程计量与计价”网络平台在线课程,基于BOPPPS构建线上线下混合教学模式,体现授课方式灵活性和多样性,旨在提高学生的兴趣、更新教学内容、优化教学设计、改善教学方法、提高教学质量。

公共数据有偿服务的正当性与实践路径研究

公共数据有偿服务是公共数据价值化和要素化的必由之路,但是目前对于公共数据有偿服务的正当性、有效路径和风险防范都还缺乏充分的研究和切实可行的方案。本文研究为持续健康推动公共数据有偿服务提供参考。本文剖析了公共数据的概念,分析了公共数据有偿服务的范围,从社会经济价值、数据权属和收费的合理性与必要性三个方面切入,论证了公共数据有偿服务的正当性。通过深入分析开展公共数据有偿服务的有效路径,本文明确建议在授权和运营模式、服务交付方式、定价策略、利益补偿机制这四个关键环节应当采取的策略和方案。通过讨论公共数据有偿服务中可能的风险和防范措施,本文总结提炼了六条具体建议。

模态分解及混合模型在比特币价格预测中的应用

独特的生产、发行和交易机制等多种因素的影响下,比特币价格表现出极端的波动性,导致了预测任务的复杂性.为此提出了基于改进的自适应噪声完备集合经验模态分解(ICEEMDAN)的混合预测模型,将复杂的原始序列分解成多个简单固有模态函数(IMFs),并通过重构算法将IMFs集成为不同频率的分量.根据各分量的不同数据模式,选取不同机器学习模型分别进行预测,叠加各分量预测结果得到最终比特币价格预测结果.对比结果表明,该模型在各评价指标上均优于单一预测模型,混合模型可以优化预测结果,较好地减小预测误差.

边际定价与经济调度深度融合的边际成本比较型竞价模式

随着新能源快速发展、新型市场主体不断涌现,以成本最小化为目标的经济调度机制和以边际定价为基础的策略型竞价模式,难以在电力供应充裕性不足时兼顾公平和效率。为此,在考虑中国电力工业基本国情的基础上,提出了一种深度融合边际定价与经济调度的边际成本比较型竞价模式,并从保障电力供应、提升市场经济效率、降低市场风险和促进市场公平四方面,探讨了该竞价模式在中国式电力市场建设中的潜在应用价值。最后,基于电力市场仿真平台开展算例分析,对边际成本比较型竞价模式的有效性进行了验证。

A Temporal Convolutional Network Based Hybrid Model for Short-term Electricity Price Forecasting

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.

考虑时序二维变化的日前市场电价预测模型

电价预测对电力市场参与者的运营决策及电力系统安全稳定运行关系重大。针对日前市场电价预测问题,本文提出一种考虑时序二维变化的日前市场电价预测模型和方法。首先采用改进的带自适应噪声的完全集成经验模式分解对日前市场电价历史数据进行分解,然后对其高、低频子序列分别采用考虑时序二维变化的Ti⁃mesNet和基于统计分析的差分自回归移动平均进行预测,二者结果之和构成日前市场电价的预测值。仿真结果表明,所提方法相较于现有单一或组合模型具有较高的预测精度。

考虑众包场景的电动车动态需求车辆路径问题

针对企业自有车辆和社会车辆共同取送货的场景,以及国家节能环保的政策背景,考虑分时电价、部分充电、软时间窗、以及动态需求等因素,以最小化配送总成本为目标,建立考虑众包场景的电动车动态需求车辆路径问题(EDDVRP-CD)的两阶段整数规划模型。考虑动态需求的时效性,设计了启发式算法——改进的禁忌自适应大规模邻域搜索算法(IALNS-TS),增加了新的删除算子和修复算子,同时提出了加速策略。分别与两种算法——自适应大规模邻域搜索算法(ALNS)以及禁忌搜索算法(TS)进行对比,通过大量算例验证了IALNS-TS算法能够快速响应动态需求,并有效降低总配送费用。

融合三支聚类与分解集成学习的股票价格预测模型

准确的趋势判断与实时价格预测是获得理想投资收益的有效途径。现实的金融市场受客观经济环境变化,投资者预期回报以及其他潜在因素影响,使得传统预测方法面临较多的挑战和压力。如何在不确定的环境中发现一种可靠的预测工具,提高预测的准确性,将是值得深入探讨的科学问题。为了获得准确的预测,帮助投资者赢得最大利润,本文引入分解集成思想和三支决策理论,提出了一种基于三支聚类和分解集成的复合预测方法。首先,使用互补集成经验模态分解方法将原始时间序列分解成若干个相对平稳的子序列,实现降低原始时间序列复杂性的同时挖掘了隐藏的信息。其次,为了针对性地处理不同属性的子序列,构建了基于贝叶斯风险决策的概率粗糙集进行三支聚类。接着,为了避免输入信息的欠缺或者冗余信息的干扰,采用基于相空间重构的特征选择方法确定不同神经网络的输入结构。最后,将提出的方法应用于美股ANY价格预测和国际、国内的重要股票指数以及其成分股预测验证其有效性和实用性。同时为把粒计算思想方法与分解集成融合,构建复杂动态数据预测决策模型与方法进行了有益的尝试和探讨。此外,研究结果将为投资者的实际投资决策提供科学的支持与参考。

经验模态分解:理论发展与金融实证进展

综述了经验模态分解方法的理论基础及其演进过程,从初始的EMD到其改进版本包括集合经验模态分解(EEMD)、完全集合经验模态分解(CEEMD)及结合噪声自适应方法(CEEMDAN)。这些方法通过逐步引入噪声辅助、集合平均和自适应噪声控制等技术,克服了原始EMD在端点效应和模式混叠等方面的不足,提高了分解的稳定性和精确性。此外,还探讨了EMD及其改进方法在金融领域的广泛应用,特别是在金融资产价格的预测、金融资产定价机制、金融风险管理等方面的实际进展。通过EMD及其衍生方法,学者能够有效地分解金融时间序列数据,揭示不同时间尺度上的市场波动特征,识别资产价格形成的非线性趋势和波动性聚集现象。这些应用不仅提升了金融资产价格预测的准确性和稳健性,还为复杂金融市场的风险评估和管理提供了更为细致的分析工具。研究结果表明,随着EMD方法的不断改进和成熟,其在金融领域的应用前景将更加广阔,并为未来的金融市场研究提供了强有力的工具和方法支持。