scholarly journals Forecasting of Wind Capacity Ramp Events Using Typical Event Clustering Identification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 176530-176539
Author(s):  
Jiang Li ◽  
Tianyu Song ◽  
Bo Liu ◽  
Haotian Ma ◽  
Jikai Chen ◽  
...  
2011 ◽  
Vol 6 (1) ◽  
pp. 55-58 ◽  
Author(s):  
C. Gallego ◽  
A. Costa ◽  
A. Cuerva

Abstract. Ramp events are large rapid variations within wind power time series. Ramp forecasting can benefit from specific strategies so as to particularly take into account these shifts in the wind power output dynamic. In the short-term context (characterized by prediction horizons from minutes to a few days), a Regime-Switching (RS) model based on Artificial Neural Nets (ANN) is proposed. The objective is to identify three regimes in the wind power time series: Ramp-up, Ramp-down and No-ramp regime. An on-line regime assessment methodology is also proposed, based on a local gradient criterion. The RS-ANN model is compared to a single-ANN model (without regime discrimination), concluding that the regime-switching strategy leads to significant improvements for one-hour ahead forecasts, mainly due to the improvements obtained during ramp-up events. Including other explanatory variables (NWP outputs, local measurements) during the regime assessment could eventually improve forecasts for further horizons.


1978 ◽  
Vol 4 (1) ◽  
pp. 45-58 ◽  
Author(s):  
Nick Roddick

The aims and ambitions of this article are initially fairly limited. I want to examine a series of events which occurred at the Comédie-Française in April and May of 1765, leading to a complete disruption of normal performances at the theatre, to the imprisonment of most of the company's leading actors, and to the temporary withdrawal from performance of what might otherwise have been eighteenth-century France's biggest ‘box-office hit’, Le Siège de Calais, a patriotic tragedy by Pierre-Laurent Buirette de Belloy. In themselves these events, sometimes known as l'affaire Dubois after the actor most directly involved in them, are little more than a bizarre and sporadically scurrilous footnote to the theatrical history of France in the eighteenth century. But the more one examines them, the more they illuminate certain rather murky areas of literary and social history, two areas in particular: firstly, the social relations of the acting profession at a time when it was, despite considerable pressure from numerous sources, still barred en bloc from the sacraments of the Catholic church; and secondly, the degree of autonomy which could be said to have existed for a company which was, legally, a kind of workers' co-operative but which, at any rate at that stage, operated within a rather ill-defined administrative limbo (it was simultaneously autonomous and totally subject to noble whim). The strike which brought about the cancellation of performances of Le siège de Calais in April 1765 is, then, a specific and in no way typical event, but one which draws together a number of historical strands – literary, theatrical, economic, moral and political – in a particularly interesting way. I want, in the course of this article, to deal with two questions – questions to which I do not really feel able to give definitive answers but which may, when examined, cast doubt upon one or two familiar preconceptions about the nature of the eighteenth-century theatre as a profession, and at the same time open up certain areas of enquiry with regard to the theatre as a material reality rather than a predominantly literary or artistic form. The questions are in themselves quite simple: why did the sociétaires of the Comédie-Française refuse, on Monday, 15th April 1765, to perform a play which, given its enormous success earlier in the year, it was very much in their economic interests to present? And why did the resulting situation become so irreducible that, far from the usual discreet pressures being brought to bear on the relevant authorities to resolve the dispute, it led to the imprisonment of three of the most popular ‘stars’ of the century, and to an effective lockout lasting for almost a month?


2014 ◽  
Vol 23 (1) ◽  
pp. 59-73
Author(s):  
E. Umamaheswari ◽  
T.V. Geetha

AbstractTraditional document clustering algorithms consider text-based features such as unique word count, concept count, etc. to cluster documents. Meanwhile, event mining is the extraction of specific events, their related sub-events, and the associated semantic relations from documents. This work discusses an approach to event mining through clustering. The Universal Networking Language (UNL)-based subgraph, a semantic representation of the document, is used as the input for clustering. Our research focuses on exploring the use of three different feature sets for event clustering and comparing the approaches used for specific event mining. In our previous work, the clustering algorithm used UNL-based event semantics to represent event context for clustering. However, this approach resulted in different events with similar semantics being clustered together. Hence, instead of considering only UNL event semantics, we considered assigning additional weights to similarity between event contexts with event-related attributes such as time, place, and persons. Although we get specific events in a single cluster, sub-events related to the specific events are not necessarily in a single cluster. Therefore, to improve our cluster efficiency, connective terms between two sentences and their representation as UNL subgraphs were also considered for similarity determination. By combining UNL semantics, event-specific arguments similarity, and connective term concepts between sentences, we were able to obtain clusters for specific events and their sub-events. We have used 112 000 Tamil documents from the Forum for Information Retrieval Evaluation data corpus and achieved good results. We have also compared our approach with the previous state-of-the-art approach for Router-RCV1 corpus and achieved 30% improvements in precision.


Author(s):  
Richard Clewley ◽  
Jim Nixon

AbstractSome safety events do not stabilise in a coherent state, presenting with transient or intermittent features. Such dynamism may pose problems for human performance, especially if combined with non-typical stimuli that are rarely encountered in everyday work. This may explain undesirable pilot behaviour and could be an important cognitive factor in recent aircraft accidents. Sixty-five airline pilots tested a real-world typicality gradient, composed of two cockpit events, a typical event, and a non-typical event, across two different forms of dynamism, a stable, single system transition, and an unstable, intermittent system transition. We found that non-typical event stimuli elicited a greater number of response errors and incurred an increased response latency when compared to typical event stimuli, replicating the typicality effect. These performance deteriorations were amplified when a form of unstable system dynamism was introduced. Typical stimuli were unaffected by dynamism. This indicates that dynamic, non-typical events are problematic for pilots and may lead to poor event recognition and response. Typical is advantageous, even if dynamic. Manufacturers and airlines should evolve pilot training and crew procedures to take account of variety in event dynamics.


2018 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Thomas M. Hamill ◽  
Julie K. Lundquist

Abstract. Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind energy production must be balanced by other power generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to twelve hours of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80-m wind speed observations from towers in Boulder, Colorado and near the Columbia River Gorge in eastern Oregon. We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill scores for each method at predicting up- and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake Shuffle method yields the highest skill at predicting ramp events for these data sets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO site using any of the multivariate methods, because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize decisions before ramp events occur.


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