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.
Best Answer
Monte-Carlo methods may work for you. In a nutshell: define the boundaries of the results you are looking for, for example energy consumption(derived from measured system parameters) within a range, and run a simulation where the free variables are allowed to vary to simulate a physical system. The sets that fall within the solution space will define the acceptable input ranges.
If your search space is manageable (depends on your system constraints), then you can fit curves to these parameters.
Alternately, you can use a minmax approach if you can define your search space in a way suitable for local minimization.