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Multi-step temperature prediction model based on surrounding cities and long-term memory neural networks

机译:基于周边城市的多步温预测模型和长期记忆神经网络

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In this paper, a multi-step temperature prediction model based on geographic location and long-term memory neural network is proposed. In order to verify the validity of the data of surrounding cities, two sets of comparative experiments are carried out which using 80-dimensional data of weather data added to surrounding cities and not joining the surrounding area. Urban weather data 8D data, 10D data added to surrounding city temperature data, and 1D data without temperature of surrounding cities. The test results show that the model of adding weather data of surrounding cities in most cases has better prediction effect than the model of weather data without surrounding cities. The results show that the model of weather data added to surrounding cities has better prediction effect.
机译:本文提出了一种基于地理位置和长期记忆神经网络的多步温预测模型。为了验证周围城市数据的有效性,进行了两组比较实验,该比较实验使用添加到周围城市的天气数据的80维数据,而不是加入周围区域。城市天气数据8D数据,10D数据添加到周围的城市温度数据和1D数据,无需周围城市的温度。测试结果表明,在大多数情况下添加周围城市的天气数据的模型比没有周围城市的天气数据模型更好的预测效果。结果表明,添加到周围城市的天气数据模型具有更好的预测效果。

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