I'm working on a project where I'm developing an interface that learns how you typically use a space, and tries to create the most appropriate control strategy for heating/lighting. I've done some research into the area of machine learning techniques, but I was wondering if there were any recommendations on which learning algorithm would work best for this scenario. I have a lot of different input parameters: I designed a low-cost wireless sensor which reports light, temperature, humidity, and motion detection every 8 seconds... I also tap into live weather feeds through the internet for exterior conditions... And I'm also storing all of the different UI changes (toggles, sliders, etc...) so hopefully I can tell when people are actually changing certain settings and adapt accordingly. As far as learning algorithms... there's a lot of different options including (to name just a few):
- K-Means
- Decision Tree
- Naïve Bayes
- Neural Networks
- Hidden Markov Models
- Nearest Neighbors
Which of these would be most ideal for the scenario I'm referring to of: Having multiple data sources and correlating them with user input to predict future desires and plan accordingly.