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首页> 外文会议>IEEE International Conference on Power and Energy >Convolution integral based multivariable grey prediction model for solar energy generation forecasting
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Convolution integral based multivariable grey prediction model for solar energy generation forecasting

机译：基于卷积积分的太阳能发电量预测灰色多变量预测模型

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In recent years necessity of clean energy generation and deregulations by the power utilities have developed an interest in power engineers to develop the Renewable energy Generation as an alternative for fossil fuel based generation. Among all forms of renewable energy Solar energy is most abundantly available and easy to harness. But solar energy being a variable energy resource owing to its dependency on weather, is not reliable unless the generation forecasting is done a priori. This unexpected variation in solar power output increases operating cost and reduces the reliability of Power Plants. So the forecasting accuracy of photovoltaic power generation governs the efficient operations of the solar power plants. An accurate forecasting can only make the Solar Energy a viable alternative for fossil fuel based power generation. In this article solar power forecasting using Convolution Integral based Multivariable Grey Model, GMC (1, 3) has been proposed. The novelty of the proposed technique lies in its way of implementation. The result obtained using GMC (1, 3) have been compared with the results of multivariable Grey Model GM (1, 3). The efficiency of the technique has been tested by using it to forecast the energy generation over a year.
机译：近年来，电力公司对清洁能源的产生和放松管制的需求引起了电力工程师的兴趣，以开发可再生能源发电，以替代基于化石燃料的发电。在所有形式的可再生能源中，太阳能是最丰富的并且易于利用。但是太阳能由于依赖天气而成为可变能源，因此除非可靠地进行发电量预测，否则它是不可靠的。太阳能输出的这种意想不到的变化增加了运营成本，并降低了发电厂的可靠性。因此，光伏发电的预测准确性决定着太阳能发电厂的高效运行。准确的预测只能使太阳能成为化石燃料发电的可行替代方案。在本文中，已经提出了使用基于卷积积分的多变量灰色模型GMC（1，3）进行太阳能发电的预测。所提出的技术的新颖性在于其实施方式。将使用GMC（1，3）获得的结果与多变量灰色模型GM（1，3）的结果进行了比较。通过使用该技术预测一年内的发电量，已对该技术的效率进行了测试。

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