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SYSTEM AND METHOD FOR PREDICTING POWER PLANT OPERATIONAL PARAMETERS UTILIZING ARTIFICIAL NEURAL NETWORK DEEP LEARNING METHODOLOGIES

机译:利用人工神经网络深度学习方法预测电厂运行参数的系统和方法

摘要

A system and method of predicting future power plant operations is based upon an artificial neural network model including one or more hidden layers. The artificial neural network is developed (and trained) to build a model that is able to predict future time series values of a specific power plant operation parameter based on prior values. By accurately predicting the future values of the time series, power plant personnel are able to schedule future events in a cost-efficient, timely manner. The scheduled events may include providing an inventory of replacement parts, determining a proper number of turbines required to meet a predicted demand, determining the best time to perform maintenance on a turbine, etc. The inclusion of one or more hidden layers in the neural network model creates a prediction that is able to follow trends in the time series data, without overfitting.
机译:一种预测未来电厂运行的系统和方法是基于包括一个或多个隐藏层的人工神经网络模型。人工神经网络被开发(和训练)以建立一个模型,该模型能够根据先前的值预测特定电厂运行参数的未来时间序列值。通过准确地预测时间序列的未来值,发电厂人员能够以经济高效,及时的方式安排未来事件。计划的事件可能包括提供备件清单,确定满足预测需求所需的合适数量的涡轮机,确定对涡轮机进行维护的最佳时间等。在神经网络中包含一个或多个隐藏层模型创建的预测能够跟踪时间序列数据中的趋势而不会过度拟合。

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