Weather station

About: Weather station is a research topic. Over the lifetime, 1789 publications have been published within this topic receiving 42864 citations. The topic is also known as: meteorological station & meteorological observation post.

Performance of the ATMOS41 All-in-One Weather Station for Weather Monitoring.

Abstract: Affordable and accurate weather monitoring systems are essential in low-income and developing countries and, more recently, are needed in small-scale research such as precision agriculture and urban climate studies. A variety of low-cost solutions are available on the market, but the use of non-standard technologies raises concerns for data quality. Research-grade all-in-one weather stations could present a reliable, cost effective solution while being robust and easy to use. This study evaluates the performance of the commercially available ATMOS41 all-in-one weather station. Three stations were deployed next to a high-performance reference station over a three-month period. The ATMOS41 stations showed good performance compared to the reference, and close agreement among the three stations for most standard weather variables. However, measured atmospheric pressure showed uncertainties >0.6 hPa and solar radiation was underestimated by 3%, which could be corrected with a locally obtained linear regression function. Furthermore, precipitation measurements showed considerable variability, with observed differences of ±7.5% compared to the reference gauge, which suggests relatively high susceptibility to wind-induced errors. Overall, the station is well suited for private user applications such as farming, while the use in research should consider the limitations of the station, especially regarding precise precipitation measurements.

Modeling the effects of the environment on wind turbine failure modes using neural networks

Abstract: Neural networks were used to investigate if any relationship existed between maximum daily gust speed, average daily wind speed and temperature and wind turbine failure modes. Using five years of weather station data a typical site characteristic was determined using the neural network. This was then compared to a characteristic produced using only weather data for days when failures occurred. These failure and normal characteristics were then compared to determine if any relationships existed. It was found that in some sub-assemblies the failure trends differed to the normal conditions trend, suggesting that there may be relationships.

A gradient boosting regression based approach for energy consumption prediction in buildings

Abstract: This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.

Weather station device

Abstract: This invention describes a density altitude measuring device whereby the density altitude is calculated based on atmospheric inputs The computer of the density altitude measuring device is connected to temperature, pressure and humidity sensors The sensors send input to the computer describing the ambient atmosphere The computer then takes sufficient samples from the sensors and calculates the density altitude Finally, the data is displayed on a screen, storage device, or printer