Weather image-based short-term dense wind speed forecast with a ConvLSTM-LSTM deep learning model

Zheng Lang; Lu Weisheng; Zhou Qianyun

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Weather image-based short-term dense wind speed forecast with a ConvLSTM-LSTM deep learning model

机译：Weather image-based short-term dense wind speed forecast with a ConvLSTM-LSTM deep learning model

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摘要

Short-term wind speed predication is of great significance for scholars (e.g., understanding wind profiles), practitioners (e.g., building energy management), regulators (e.g., urban microclimate regulation), and even the general public. Current wind speed forecasting methods either generate sparse predictions or occur high cost. This paper reports a novel, inexpensive framework to forecast urban local dense wind speed. The central tenet is a convolutional long short-term memory (ConvLSTM) and LSTM combinatorial deep learning model to learn the features of input historical weather image series coupled with spatial-temporal correlations. The model was trained and tested using Hong Kong datasets. The feasibility and effectiveness of the proposed model are verified and compared with parallel models under different criteria, including mean absolute error (MAE), root mean square error (RMSE) and R-squared (R2). The experimental results show that: (1) the proposed ConvLSTM-LSTM deep learning model can effectively forecast wind speed regardless of location; (2) the overall MAE, RMSE, and R2 value of the proposed model are improved by 14.84, 15.04, and 7.51, respectively, compared to the ConvLSTM-full connected (ConvLSTM-FC) model, and by 22.12, 22.80, and 12.24, respectively, compared to the convolutional neural network-LSTM (CNN-LSTM) model; and (3) compared with parallel models, the proposed model has better performance in predicting wind speed series with large amplitude variations and rapid frequency changes.

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来源
《Building and environment》 |2023年第7期|1.1-1.12|共12页
作者
Zheng Lang; Lu Weisheng; Zhou Qianyun;
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作者单位

Univ Hong Kong||Univ Hong Kong;

Univ Hong Kong;

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原文格式 PDF
正文语种英语
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关键词
Dense wind speed forecast; Weather image; Deep learning; Convolutional long -short term memory; Spatial -temporal features;

Weather image-based short-term dense wind speed forecast with a ConvLSTM-LSTM deep learning model

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