Wholesale and retail markets for electricity and power require consumers to forecast electricity consumption at different time intervals. The study aims to</span><span style="font-family:Verdana;"&g...Wholesale and retail markets for electricity and power require consumers to forecast electricity consumption at different time intervals. The study aims to</span><span style="font-family:Verdana;"> increase economic efficiency of the enterprise through the introduction of algorithm for forecasting electric energy consumption unchanged in technological process. Qualitative forecast allows you to essentially reduce costs of electrical </span><span style="font-family:Verdana;">energy, because power cannot be stockpiled. Therefore, when buying excess electrical power, costs can increase either by selling it on the balancing energy </span><span style="font-family:Verdana;">market or by maintaining reserve capacity. If the purchased power is insufficient, the costs increase is due to the purchase of additional capacity. This paper illustrates three methods of forecasting electric energy consumption: autoregressive integrated moving average method, artificial neural networks and classification and regression trees. Actual data from consuming of electrical energy was </span><span style="font-family:Verdana;">used to make day, week and month ahead prediction. The prediction effect of</span><span> </span><span style="font-family:Verdana;">prediction model was proved in Statistica simulation environment. Analysis of estimation of the economic efficiency of prediction methods demonstrated that the use of the artificial neural networks method for short-term forecast </span><span style="font-family:Verdana;">allowed reducing the cost of electricity more efficiently. However, for mid-</span></span><span style="font-family:""> </span><span style="font-family:Verdana;">range predictions, the classification and regression tree was the most efficient method for a Jerky Enterprise. The results indicate that calculation error reduction allows decreases expenses for the purchase of electric energy.展开更多
固体火箭发动机声不稳定燃烧源于推进剂的燃烧增益与燃烧室声学空间相互耦合,并以声能的形式积聚形成声共振,所以抑制声不稳定燃烧的关键在于对声能的有效耗散。声学黑洞(Acoustic Black Hole,ABH)作为一种新型的波操纵技术,利用阻抗的...固体火箭发动机声不稳定燃烧源于推进剂的燃烧增益与燃烧室声学空间相互耦合,并以声能的形式积聚形成声共振,所以抑制声不稳定燃烧的关键在于对声能的有效耗散。声学黑洞(Acoustic Black Hole,ABH)作为一种新型的波操纵技术,利用阻抗的变化实现降低波速、增加波幅的目的,为吸声结构的设计提供了新思路。本文针对某试验发动机不稳定燃烧问题,提出应用声学黑洞的发动机声能耗散设计方法(ABH声陷阱),以提升燃烧稳定性。基于理论方法分析了ABH声陷阱中声波随传播距离的变化规律,提出了发动机中的应用方案。通过有限元仿真方法研究了含有三种几何参数ABH声陷阱发动机声学特性,结果表明:ABH声陷阱宽频范围内可以有效降低发动机内的声压,结合实际发动机燃面声激励,0~3000 Hz内33.50%~43.12%的声能可被ABH声陷阱吸收。该研究为新型固体火箭发动机抑制声不稳定燃烧提供了新思路。展开更多
文摘Wholesale and retail markets for electricity and power require consumers to forecast electricity consumption at different time intervals. The study aims to</span><span style="font-family:Verdana;"> increase economic efficiency of the enterprise through the introduction of algorithm for forecasting electric energy consumption unchanged in technological process. Qualitative forecast allows you to essentially reduce costs of electrical </span><span style="font-family:Verdana;">energy, because power cannot be stockpiled. Therefore, when buying excess electrical power, costs can increase either by selling it on the balancing energy </span><span style="font-family:Verdana;">market or by maintaining reserve capacity. If the purchased power is insufficient, the costs increase is due to the purchase of additional capacity. This paper illustrates three methods of forecasting electric energy consumption: autoregressive integrated moving average method, artificial neural networks and classification and regression trees. Actual data from consuming of electrical energy was </span><span style="font-family:Verdana;">used to make day, week and month ahead prediction. The prediction effect of</span><span> </span><span style="font-family:Verdana;">prediction model was proved in Statistica simulation environment. Analysis of estimation of the economic efficiency of prediction methods demonstrated that the use of the artificial neural networks method for short-term forecast </span><span style="font-family:Verdana;">allowed reducing the cost of electricity more efficiently. However, for mid-</span></span><span style="font-family:""> </span><span style="font-family:Verdana;">range predictions, the classification and regression tree was the most efficient method for a Jerky Enterprise. The results indicate that calculation error reduction allows decreases expenses for the purchase of electric energy.
文摘固体火箭发动机声不稳定燃烧源于推进剂的燃烧增益与燃烧室声学空间相互耦合,并以声能的形式积聚形成声共振,所以抑制声不稳定燃烧的关键在于对声能的有效耗散。声学黑洞(Acoustic Black Hole,ABH)作为一种新型的波操纵技术,利用阻抗的变化实现降低波速、增加波幅的目的,为吸声结构的设计提供了新思路。本文针对某试验发动机不稳定燃烧问题,提出应用声学黑洞的发动机声能耗散设计方法(ABH声陷阱),以提升燃烧稳定性。基于理论方法分析了ABH声陷阱中声波随传播距离的变化规律,提出了发动机中的应用方案。通过有限元仿真方法研究了含有三种几何参数ABH声陷阱发动机声学特性,结果表明:ABH声陷阱宽频范围内可以有效降低发动机内的声压,结合实际发动机燃面声激励,0~3000 Hz内33.50%~43.12%的声能可被ABH声陷阱吸收。该研究为新型固体火箭发动机抑制声不稳定燃烧提供了新思路。
