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Smart city article details
| Title | Comparative Analysis Of Lstm, Rf And Svm Architectures For Predicting Wind Nature For Smart City Planning |
|---|---|
| ID_Doc | 14998 |
| Authors | Harbola S.; Coors V. |
| Year | 2019 |
| Published | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 4/W9 |
| DOI | http://dx.doi.org/10.5194/isprs-annals-IV-4-W9-65-2019 |
| Abstract | Meteorological data and its effect have been the attention of the researchers of the smart city planning for thorough utilization and management of resources, that help in effective government management, convenient public services and sustainable industrial development. Renewable sources of energy like wind, solar, are being integrated into city planning to improve environmental quality. Wind energy is utilized through wind turbines and requires foreknowledge of wind parameters like speed and direction. The aim of this paper is to predict dominant wind speed and direction for time-series wind dataset, that can be incorporated into city planning for selecting suitable sites for wind turbines. This paper proposes three one-dimensional (1D) algorithms using Long Short Term Memory (LSTM), Random Forest (RF) and Support Vector Machine (SVM) for dominant wind speed and direction prediction. The proposed 1D LSTM (1DLSTM), RF (1DRF) and SVM (1DSVM) take successive time values in terms of wind speed and direction as input and predict the future dominant speed and direction, separately. The proposed algorithms are trained and tested using historical wind dataset of Stuttgart and Netherlands respectively. Prediction using 1DLSTM results in total accuracies reaching up to 93.9% and 94.7%, up to 92.8% and 93.8% using 1DSVM and up to 88.7% and 89.3% using 1DRF for speed and direction, respectively. Thus, prediction of wind nature using the proposed algorithms, will give city planners advanced knowledge of wind conditions. © 2020 Authors. |
| Author Keywords | Cities planning; energy conversion; machine learning; meteorological data; wind prediction |
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