Visual Weather Temperature Prediction

In this paper, we attempt to employ convolutional recurrent neural networks for weather temperature estimation using only image data. We study ambient temperature estimation based on deep neural networks in two scenarios a) estimating temperature of a single outdoor image, and b) predicting temperature of the last image in an image sequence. In the first scenario, visual features are extracted by a convolutional neural network trained on a large-scale image dataset. We demonstrate that promising performance can be obtained, and analyze how volume of training data influences performance. In the second scenario, we consider the temporal evolution of visual appearance, and construct a recurrent neural network to predict the temperature of the last image in a given image sequence. We obtain better prediction accuracy compared to the state-of-the-art models. Further, we investigate how performance varies when information is extracted from different scene regions, and when images are captured in different daytime hours. Our approach further reinforces the idea of using only visual information for cost efficient weather prediction in the future.Comment: 8 pages, accepted to WACV 201

Weather Forecasting Using Merged Long Short-term Memory Model

Over decades, weather forecasting has attracted researchers from worldwide communities due to itssignificant effect to global human life ranging from agriculture, air trafic control to public security. Although formal study on weather forecasting has been started since 19th century, research attention to weather forecasting tasks increased significantly after weather big data are widely available. This paper proposed merged-Long Short-term Memory for forecasting ground visibility at the airpot using timeseries of predictor variable combined with another variable as moderating variable. The proposed models were tested using weather timeseries data at Hang Nadim Airport, Batam. The experiment results showedthe best average accuracy for forecasting visibility using merged Long Short-term Memory model and temperature and dew point as a moderating variable was (88.6%); whilst, using basic Long Short-term Memory without moderating variablewasonly (83.8%) respectively (increased by 4.8%)

Supervised Contrastive Regression

Deep regression models typically learn in an end-to-end fashion and do not explicitly try to learn a regression-aware representation. Their representations tend to be fragmented and fail to capture the continuous nature of regression tasks. In this paper, we propose Supervised Contrastive Regression (SupCR), a framework that learns a regression-aware representation by contrasting samples against each other based on their target distance. SupCR is orthogonal to existing regression models, and can be used in combination with such models to improve performance. Extensive experiments using five real-world regression datasets that span computer vision, human-computer interaction, and healthcare show that using SupCR achieves the state-of-the-art performance and consistently improves prior regression baselines on all datasets, tasks, and input modalities. SupCR also improves robustness to data corruptions, resilience to reduced training data, performance on transfer learning, and generalization to unseen targets.Comment: The first two authors contributed equally to this pape

Neural network learns from mock-up operation experience:implementing on a solar energy community distribution

Inspired by Imitation Learning, this paper trained a LSTM network by a mock-up operation experience of a solar energy community distribution system. Unlike the conventional method that implements LSTM only to predict features for the control programme to calculate an operation action according to a strategy, the LSTM of the proposed model integrates the strategy into its structure and thus can outputs actions directly. To examine whether the proposed model outperforms the conventional model, this paper first describes an operation strategy, adopted by both models, that aims to decrease total operation cost. Since the strategy needs accurate predictions to work effectively, an expert who can perfectly predict the future is created by historical data. The behaviours of the expert that follows the strategy are used as the training data of the LSTM in the proposed model. During simulation, the proposed model has better performance and computation efficiency than the conventional LSTM model by 25% higher and 75 times faster. Many researches have proposed control models for different systems and implemented LSTM only to predict key uncertainty in those models. To these researches, this paper demonstrates a promising result that the performance of a control model can be improved by integrating the strategy of that model into a neural network with mock-up operation experience

Generation of Images Simulating Different Meteorological Conditions Using Generative Adversarial Networks (GANs)

RESUMEN: Las redes generativas antagónicas, comunmente conocidas por su acrónimo en inglés como GANs, son un tipo de modelos generativos de aprendizaje profundo que están formadas por un sistema de dos redes neuronales que compiten mutuamente en un juego de suma cero. En este trabajo, un parte fundamental será comprender el funcionamiento de este tipo de modelos y sus diferentes variantes entre los que se encuentran la cycle GAN, cuya función es aprender a traducir una imagen de un dominio de origen X a un dominio de destino Y en ausencia de ejemplos emparejados. Este modelo será utilizado para modificar las condiciones meteorológicas de una imagen dada, por ejemplo, transformar una imagen de un día soleado a un día lluvioso o nublado o viceversa.ABSTRACT: Las redes generativas antagónicas, comunmente conocidas por su acrónimo en inglés como GANs, son un tipo de modelos generativos de aprendizaje profundo que están formadas por un sistema de dos redes neuronales que compiten mutuamente en un juego de suma cero. En este trabajo, un parte fundamental será comprender el funcionamiento de este tipo de modelos y sus diferentes variantes entre los que se encuentran la cycle GAN, cuya función es aprender a traducir una imagen de un dominio de origen X a un dominio de destino Y en ausencia de ejemplos emparejados. Este modelo será utilizado para modificar las condiciones meteorológicas de una imagen dada, por ejemplo, transformar una imagen de un día soleado a un día lluvioso o nublado o viceversa.Máster en Ciencia de Dato

Visual Weather Temperature Prediction

In this paper, we attempt to employ convolutional recurrent neural networks for weather temperature estimation using only image data. We study ambient temperature estimation based on deep neural networks in two scenarios a) estimating temperature of a single outdoor image, and b) predicting temperature of the last image in an image sequence. In the first scenario, visual features are extracted by a convolutional neural network trained on a large-scale image dataset. We demonstrate that promising performance can be obtained, and analyze how volume of training data influences performance. In the second scenario, we consider the temporal evolution of visual appearance, and construct a recurrent neural network to predict the temperature of the last image in a given image sequence. We obtain better prediction accuracy compared to the state-of-the-art models. Further, we investigate how performance varies when information is extracted from different scene regions, and when images are captured in different daytime hours. Our approach further reinforces the idea of using only visual information for cost efficient weather prediction in the future