Large intermittent generations have grown the influence on the grid security, system operation, and market economics. Although wind energy may not be dispatched, the cost impacts of wind can be substantially reduced if the wind energy can be scheduled using accurate wind forecasting. In other words, the improvement of the performance of wind power forecasting tool has significant technology and economic impact on the system operation with increased wind power penetration.Forecasting has been a vital part of business planning in today s competitive environment, especially in areas characterized by a high concentration of wind generation and a limited capacity of network. The target of this paper is to present a critical literature review and an up-to-date bibliography on wind forecasting technologies over the world. Various forecasting aspects concerning the wind speed and power have been highlighted. These technologies based on numeric weather prediction (NWP) methods, statistical methods, methods based upon artificial neural networks (ANNs), and hybrid forecasting approaches will be discussed. Furthermore, the difference between wind speed and power forecasting, the lead time of forecasting, and the further research will also be discussed in this paper.Index Terms wind power forecasting, numeric weather prediction, statistical methods
In this study, various concentrations of caffeic acid (CA) were used to synthesize gold nanoparticles (CA-AuNPs) in order to evaluate their catalytic activity in the 4-nitrophenol reduction reaction. To facilitate catalytic activity, caffeic acid was removed by centrifugation after synthesizing CA-AuNPs. The catalytic activity of CA-AuNPs was compared with that of centrifuged CA-AuNPs (cf-CA-AuNPs). Notably, cf-CA-AuNPs exhibited up to 6.41-fold higher catalytic activity compared with CA-AuNPs. The catalytic activity was dependent on the caffeic acid concentration, and the lowest concentration (0.08 mM) produced CA-AuNPs with the highest catalytic activity. The catalytic activities of both CA-AuNPs and cf-CA-AuNPs decreased with increasing caffeic acid concentration. Furthermore, a conversion yield of 4-nitrophenol to 4-aminophenol in the reaction mixture was determined to be 99.8% using reverse-phase high-performance liquid chromatography. The product, 4-aminophenol, was purified from the reaction mixture, and its structure was confirmed by 1H-NMR. It can be concluded that the removal of the reducing agent, caffeic acid in the present study, significantly enhanced the catalytic activity of CA-AuNPs in the 4-nitrophenol reduction reaction.
In this paper, the impact of outer loop and partial cycling with the Weather Research and Forecasting Model’s (WRF) three-dimensional variational data assimilation system (3DVAR) is evaluated by analyzing 78 forecasts for three typhoons during 2008 for which Taiwan’s Central Weather Bureau (CWB) issued typhoon warnings, including Sinlaku, Hagupit, and Jangmi. The use of both the outer loop and the partial cycling approaches in WRF 3DVAR are found to reduce typhoon track forecast errors by more than 30%, averaged over a 72-h period. The improvement due to the outer loop approach, which can be more than 42%, was particularly significant in the early phase of the forecast. The use of the outer loop allows more observations to be assimilated and produces more accurate analyses. The assimilation of additional nonlinear GPS radio occultation (RO) observations over the western North Pacific Ocean, where traditional observational data are lacking, is particularly useful. With the lack of observations over the tropical and subtropical oceans, the error in the first-guess field (which is based on a 6-h forecast of the previous cycle) will continue to grow in a full-cycling limited-area data assimilation system. Even though the use of partial cycling only shows a slight improvement in typhoon track forecast after 12 h, it has the benefit of suppressing the growth of the systematic model error. A typhoon prediction model using the Advanced Research core of the WRF (WRF-ARW) and the WRF 3DVAR system with outer loop and partial cycling substantially improves the typhoon track forecast. This system, known as Typhoon WRF (TWRF), has been in use by CWB since 2010 for operational typhoon predictions.
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