An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited i...An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.展开更多
Supply chain management usually faces problems such as high empty rate of transportation, unreasonable inventory management, and large material consumption caused by inaccurate market demand forecasts. To solve these ...Supply chain management usually faces problems such as high empty rate of transportation, unreasonable inventory management, and large material consumption caused by inaccurate market demand forecasts. To solve these problems, using artificial intelligence and big data technology to achieve market demand forecasting and intelligent decision-making is becoming a strategic technology trend of supply chain management in the future. Firstly, this paper makes a visual analysis of the historical data of the Stock Keeping Unit (SKU);Then, the characteristic factors affecting the future demand are constructed from the storage level, product level, historical usage of SKU, etc;Finally, a supply chain demand forecasting algorithm based on SSA-XGBoost model has proposed around three aspects of feature engineering, parameter optimization and model integration, and is compared with other machine learning models. The experiment shows that the forecasting result of SSA-XGBoost forecasting model is highly consistent with the actual value, so it is of practical significance to adopt this forecasting model to solve the supply chain demand forecasting problem.展开更多
Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales ...Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales data of a fresh food e-commerce enterprise as the logistics demand, analyzes the influence of time and meteorological factors on the demand, extracts the characteristic factors with greater influence, and proposes a logistics demand forecast scheme of fresh food e-commerce based on the Bi-LSTM model. The scheme is compared with other schemes based on the BP neural network and LSTM neural network models. The experimental results show that the Bi-LSTM model has good prediction performance on the problem of logistics demand prediction. This facilitates further research on some supply chain issues, such as business decision-making, inventory control, and logistics capacity planning.展开更多
Peer-to-peer (P2P) networking is a distributed architecture that partitions tasks or data between peer nodes. In this paper, an efficient Hypercube Sequential Matrix Partition (HS-MP) for efficient data sharing in P2P...Peer-to-peer (P2P) networking is a distributed architecture that partitions tasks or data between peer nodes. In this paper, an efficient Hypercube Sequential Matrix Partition (HS-MP) for efficient data sharing in P2P Networks using tokenizer method is proposed to resolve the problems of the larger P2P networks. The availability of data is first measured by the tokenizer using Dynamic Hypercube Organization. By applying Dynamic Hypercube Organization, that efficiently coordinates and assists the peers in P2P network ensuring data availability at many locations. Each data in peer is then assigned with valid ID by the tokenizer using Sequential Self-Organizing (SSO) ID generation model. This ensures data sharing with other nodes in large P2P network at minimum time interval which is obtained through proximity of data availability. To validate the framework HS-MP, the performance is evaluated using traffic traces collected from data sharing applications. Simulations conducting using Network simulator-2 show that the proposed framework outperforms the conventional streaming models. The performance of the proposed system is analyzed using energy consumption, average latency and average data availability rate with respect to the number of peer nodes, data size, amount of data shared and execution time. The proposed method reduces the energy consumption 43.35% to transpose traffic, 35.29% to bitrev traffic and 25% to bitcomp traffic patterns.展开更多
目的为满足动态化用户对产品数据管理的需求,解决现有产品数据管理效率低下,底层数据杂乱,数据管理平台技术受限的问题,对现有产品数据体系进行设计,并开发产品数据平台,保证产品数据的规范化与个性化要求。方法从动态客户需求的视角出...目的为满足动态化用户对产品数据管理的需求,解决现有产品数据管理效率低下,底层数据杂乱,数据管理平台技术受限的问题,对现有产品数据体系进行设计,并开发产品数据平台,保证产品数据的规范化与个性化要求。方法从动态客户需求的视角出发,提出结合模糊Kano模型和模糊层次分析法(FAHP)的产品数据体系设计方法,首先通过Voice of the Customer(VoC)法对用户需求进行收集与分析;其次通过模糊Kano模型对收集到的用户需求进行因子分类,找到关键需求因子;再次过FAHP模型对关键需求因子进行重要性排序;最后通过聚类分析需求关联性对因子进行修正。结果得到产品数据体系的底层设计依据,应用于技术数据管理平台设计中,以效率指标量化评价设计成果。结论该方法能够显著提升数据清洁度与有效性,满足多方用户对于产品数据的管理需求,同时也为产品数据管理平台国产化的建设提供一种可行且可靠的思路。展开更多
基金Project(51606225) supported by the National Natural Science Foundation of ChinaProject(2016JJ2144) supported by Hunan Provincial Natural Science Foundation of ChinaProject(502221703) supported by Graduate Independent Explorative Innovation Foundation of Central South University,China
文摘An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.
文摘Supply chain management usually faces problems such as high empty rate of transportation, unreasonable inventory management, and large material consumption caused by inaccurate market demand forecasts. To solve these problems, using artificial intelligence and big data technology to achieve market demand forecasting and intelligent decision-making is becoming a strategic technology trend of supply chain management in the future. Firstly, this paper makes a visual analysis of the historical data of the Stock Keeping Unit (SKU);Then, the characteristic factors affecting the future demand are constructed from the storage level, product level, historical usage of SKU, etc;Finally, a supply chain demand forecasting algorithm based on SSA-XGBoost model has proposed around three aspects of feature engineering, parameter optimization and model integration, and is compared with other machine learning models. The experiment shows that the forecasting result of SSA-XGBoost forecasting model is highly consistent with the actual value, so it is of practical significance to adopt this forecasting model to solve the supply chain demand forecasting problem.
文摘Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales data of a fresh food e-commerce enterprise as the logistics demand, analyzes the influence of time and meteorological factors on the demand, extracts the characteristic factors with greater influence, and proposes a logistics demand forecast scheme of fresh food e-commerce based on the Bi-LSTM model. The scheme is compared with other schemes based on the BP neural network and LSTM neural network models. The experimental results show that the Bi-LSTM model has good prediction performance on the problem of logistics demand prediction. This facilitates further research on some supply chain issues, such as business decision-making, inventory control, and logistics capacity planning.
文摘Peer-to-peer (P2P) networking is a distributed architecture that partitions tasks or data between peer nodes. In this paper, an efficient Hypercube Sequential Matrix Partition (HS-MP) for efficient data sharing in P2P Networks using tokenizer method is proposed to resolve the problems of the larger P2P networks. The availability of data is first measured by the tokenizer using Dynamic Hypercube Organization. By applying Dynamic Hypercube Organization, that efficiently coordinates and assists the peers in P2P network ensuring data availability at many locations. Each data in peer is then assigned with valid ID by the tokenizer using Sequential Self-Organizing (SSO) ID generation model. This ensures data sharing with other nodes in large P2P network at minimum time interval which is obtained through proximity of data availability. To validate the framework HS-MP, the performance is evaluated using traffic traces collected from data sharing applications. Simulations conducting using Network simulator-2 show that the proposed framework outperforms the conventional streaming models. The performance of the proposed system is analyzed using energy consumption, average latency and average data availability rate with respect to the number of peer nodes, data size, amount of data shared and execution time. The proposed method reduces the energy consumption 43.35% to transpose traffic, 35.29% to bitrev traffic and 25% to bitcomp traffic patterns.
文摘目的为满足动态化用户对产品数据管理的需求,解决现有产品数据管理效率低下,底层数据杂乱,数据管理平台技术受限的问题,对现有产品数据体系进行设计,并开发产品数据平台,保证产品数据的规范化与个性化要求。方法从动态客户需求的视角出发,提出结合模糊Kano模型和模糊层次分析法(FAHP)的产品数据体系设计方法,首先通过Voice of the Customer(VoC)法对用户需求进行收集与分析;其次通过模糊Kano模型对收集到的用户需求进行因子分类,找到关键需求因子;再次过FAHP模型对关键需求因子进行重要性排序;最后通过聚类分析需求关联性对因子进行修正。结果得到产品数据体系的底层设计依据,应用于技术数据管理平台设计中,以效率指标量化评价设计成果。结论该方法能够显著提升数据清洁度与有效性,满足多方用户对于产品数据的管理需求,同时也为产品数据管理平台国产化的建设提供一种可行且可靠的思路。
