Deep Learning in the Web Browser for Wind Speed Forecasting using TensorFlow.js

Sammanfattning: Deep Learning is a powerful and rapidly advancing technology that has shown promising results within the field of weather forecasting. Implementing and using deep learning models can however be challenging due to their complexity. One approach to potentially overcome the challenges with deep learning is to run deep learning models directly in the web browser. This approach introduces several advantages, including accessibility, data privacy, and the ability to access device sensors. The ability to run deep learning models on the web browser thus opens new possibilities for research and development in areas such as weather forecasting. In this thesis, two deep learning models that run in the web browser are implemented using JavaScript and TensorFlow.js to predict wind speed in the near future. Specifically, the application of Long Short-Term Memory and Gated Recurrent Units models are investigated. The results demonstrate that both the Long Short-Term Memory and Gated Recurrent Units models achieve similar performance and are able to generate predictions that closely align with the expected patterns when the variations in the data are less significant. The best performing Long Short-Term Memory model achieved a mean squared error of 0.432, a root mean squared error of 0.657 and a mean average error of 0.459. The best performing Gated Recurrent Units model achieved a mean squared error of 0.435, a root mean squared error of 0.660 and a mean average error of 0.461.