代写-风力发电的预测模型。预测模型是风力发电的重要一步,因为它们改变了能源市场的面貌。长期以来,能源市场一直依赖于不可再生能源的采购,许多情况下的短缺使得能源市场不得不应对高成本和更多的问题(Karki et al, 2006)。火力发电厂和其他电网系统只能支持在应对能源短缺时出现的一定程度的赤字。考虑到初期的基础设施、成本等因素,使用太阳能等其他不可再生能源的成本相当高昂。

The most common risk faced by operators here are how the wind power ramps up and down. The wind power issues as seen here lead to multiple risks. Primarily, there are financial risks for the company. Because of the uncertainty in wind power generation, it is possible that companies have to have alternate supplies in energy. This means that they have to invest in two forms of energy supplies and this increases the overall cost as well. Secondly, in the electricity market, it is easier for the industries in the market to face the demands of customer, if they know how much energy they would have to meet it. The uncertainties in wind power energy results in the issues of mismanagement and problems of allocation based on demand and capacity. Now the use of smart grid technologies changes how intelligent load management is done. Therefore, having a proper forecast model will actually result in better integration of wind power for the existing energy markets (Wang et al, 2008; Buizza 2003.)

Finally, forecast models are a significant step in wind energy generation because of how they change the face of the energy markets. Energy markets for long have relied on non-renewable energy sourcing and the shortages in many contexts have left the energy market to deal with high costs and more (Karki et al, 2006). Thermal generation plants and other grid systems are able to support only a certain extent of the deficit that occurs when dealing with energy shortages. The use of other non-renewable energy sourcing such as the solar energy quite expensive given the initial infrastructure, costs and more. The use of conventional thermal systems is furthermore becoming outdated. There is a growing need for replacement. The search for renewable energy sourcing’s are also taking place. Given this context, the discussions on wind energy and the form of energy portfolio mix that wind energy brings to the energy market is indeed interesting. From offshore wind, tidal plants and more, wind power generation is considered to be operationally less intensive if they are set in the right place in the right weather conditions. The use of the appropriate forecasting models would hence add to the use of this energy source in better terms.

Some of the key models used in current research that are classified based on the underlying math design are as follows. 1) Prediktor model: this is a model developed by Landberg in Denmark and it is a physical model and is currently used in countries such as Spain, Denmark, Ireland, France, etc. 2) WPPT is a model that makes use of mathematical modelling techniques and statistics and was developed as a collaborative project with the Danmarks Tekniske University. This model is once again made use of in Denmark, Holland, Canada, etc. The Zephyr, the WPS are some of the other models used (Juben et al 2007; Landberg, 2001).

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