Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supp...Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.展开更多
为充分挖掘供给侧发电机和需求侧柔性负荷的联合优化调度空间，实现分布式自律计算与集中协调的互动框架，满足供需互动快速决策的需求，最大化系统的整体效益，搭建了基于stackelberg博弈的电力系统实时供需互动模型，并提出了一种全...为充分挖掘供给侧发电机和需求侧柔性负荷的联合优化调度空间，实现分布式自律计算与集中协调的互动框架，满足供需互动快速决策的需求，最大化系统的整体效益，搭建了基于stackelberg博弈的电力系统实时供需互动模型，并提出了一种全新的深度迁移强化学习（deep transfer reinforcement leaming，DTRL）算法。该算法通过对历史优化任务的有效信息进行知识存储，利用深度学习实现高精度的非线性迁移学习，并借助分布式计算优势，可快速获得高质量的最优解。算例仿真表明：DTRL在保证最优解质量的同时，其求解速度可达其他6种对比算法的419倍以上，适合求解大规模电力系统的供需互动快速决策问题。展开更多
文摘Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.
文摘为充分挖掘供给侧发电机和需求侧柔性负荷的联合优化调度空间，实现分布式自律计算与集中协调的互动框架，满足供需互动快速决策的需求，最大化系统的整体效益，搭建了基于stackelberg博弈的电力系统实时供需互动模型，并提出了一种全新的深度迁移强化学习（deep transfer reinforcement leaming，DTRL）算法。该算法通过对历史优化任务的有效信息进行知识存储，利用深度学习实现高精度的非线性迁移学习，并借助分布式计算优势，可快速获得高质量的最优解。算例仿真表明：DTRL在保证最优解质量的同时，其求解速度可达其他6种对比算法的419倍以上，适合求解大规模电力系统的供需互动快速决策问题。
