Climate Research

1911 Submissions

[2] viXra:1911.0298 [pdf] submitted on 2019-11-16 20:25:13

Weather Event Severity Prediction Using Buoy Data and Machine Learning

Authors: Vikas Ramachandra
Comments: 9 Pages.

In this paper, we predict severity of extreme weather events (tropical storms, hurricanes, etc.) using buoy data time series variables such as wind speed and air temperature. The prediction/forecasting method is based on various forecasting and machine learning models. The following steps are used. Data sources for the buoys and weather events are identified, aggregated and merged. For missing data imputation, we use Kalman filters as well as splines for multivariate time series. Then, statistical tests are run to ascertain increasing trends in weather event severity. Next, we use machine learning to predict/forecast event severity using buoy variables, and report good accuracies for the models built.
Category: Climate Research

[1] viXra:1911.0162 [pdf] submitted on 2019-11-09 10:07:59

Forecasting the Effect of Heat Stress Index and Climate Change on Cloud Data Center Energy Consumption

Authors: Vikas Ramachandra
Comments: 5 Pages.

In this paper, we estimate the effect of heat stress index (a measure which takes into account rising temperatures as well as humidity) on data center energy consumption. We use forecasting models to predict future energy use by data centers, taking into account rising temperature scenarios. We compare those estimates with baseline forecasted energy consumption (without heat stress index or rising temperature correction) and present the result that there is a sizeable and significant difference in the two forecasts. We show that rising temperatures will cause a negative impact on data center energy consumption, increasing it by about 8 percent, and conclude that data center energy consumption analyses and forecasts must include the effects of heat stress index and rising temperatures and other climate change related effects.
Category: Climate Research