Hi!
Time to do a proper introduction:
Sorry I do not enjoy logging a lot, in fact thats why my github readme’s are done last and take weeks haha! But seriously. What is this long title all about? Well my name is Krupam and this is my passion. Wind. Specially Onshore winds. Onshore winds are also known as a “sea breeze”. They are winds that come from the ocean to the land. Caused by a temperature gradient by the land and the sea. Land heats up a lot faster than water, further increasing this gradient, leading to more onshore winds. They pose a dangerous threat to small watercrafts especially on the bay (lagoonal estuary) where I live. They provide a threat to boaters, tourists, passengers, or any one on the water. In fact, local boaters have become so attuned to their dangers, that they will intentionally go out of their way to avoid them. However, these winds are almost impossible to predict. Big weather forecasts, such as the National Weather Service (NWS) and National Oceanic and Atmospheric Administration (NOAA) can’t predict these winds on a local small level. Thus I set out on a mission to predict these winds myself using machine learning, mathematical models, and gathering my own data using local weather stations, buoys, and local boaters. Last year I was able to get accuracy down to 0.5 mph for wind speeds, ~1.0 mph for gusts, around 26° off for wind direction, and ~13% off for if it is an onshore wind or not. This project is running at https://wind.krupamlab.com on my homelab. You can take a look at the old project at https://github.com/Krupamc/Research-2026-LSTM. The project also has python as well as a .exe version available through the releases tab on GitHub.
This project:
Now that’s a lot to take in, but I’m going to make it so much worse haha! Now these systems are cool and all, but they could get so much more accurate. I want month by month representation for the entire year; not just the summer months. A huge part of science, especially data science, is that data has to be available throughout the entire year. I have personal experience with this. One of the buoys I was using to collect data; switched off without any indication. This really limited my project and I will be fixing this problem head one. I want to have predictions that are accurate for the safety of these boaters. But to make it more interesting this year, I will be using several machine learning (like LSTM), mathematical models (like Linear Regression, wind formulas, probalisitc models (e.g. 80% of winds above 10 mph), as well as compare against the NWS models. Then depending on which one of these approaches is most energy efficient and accurate, I will expand that approach to cover other weather parameters and be used as a input for a AI model to determine where a water drone should go in my bay to increase battery efficiency. Now what the hell is this drone I can hear you ask? Well I am cooperating with a local underwater search and rescue team that is creating a water drone that will go on the bay and collect water and weather quality parameters. This drone will be autonomous and creating this model will allow it to increase power efficiently.
My Buoy Buddy - MBB
Last year, one of the public buoys I depended on suddenly went offline and wiped out a huge chunk of my training data. It made my realize that having control to reliable data is crucial. Many organizations collect this data but do not share. If I really wanted to protect boaters out on the bay, I would need a way to procure my own data. That’s why I am building the MBB or the My Buoy Buddy.
MBB is a small, low-power buoy that floats in the bay and logs, water temperature and more! This data is sent to my homelab to predict. I want to sell this pre-made buoy to wind-based boaters (even nationaly!) together that will combine with my open-source program to create local predictions running on their own hardware.
