Participants from the Data Science for Internet of Things course are working on some excellent projects.
Here is a great example -
Created by Vaijayanti – and other collaborators from the course
Project name: 4Wind
Domain: Renewables/Wind energy
Problem statement: To make day-ahead forecast for wind power (10 minute interval data)
Data: collected from around 5000 turbines
Duration: She has been working for more than a year and still ongoing
Programming Approach : Using R for data science. To evaluate various models: ARIMA, Holt-Winters forecast, neural networks ACF and PACF plots reveal 35 lags matter. Hence the data is recoded to have 35 columns of lags for both wind speed and wind power.
R packages: forecast, neuralnet, nnet
Challenges: Training neural networks has become heavy on a PC. Hence, experimenting with multiple models including Distributed(Spark) and AWS, Azure etc. 35 lags were based on ACF and PACF analysis. However, this number could be different. More analysis might yield different lags as predictors. Also, we could use not just consecutive lags but also the samples a day before and year before are also predictors.
UI: Using the Shiny package
Publication/Open source methodology: Contribution to Data Science for IoT methodology and to forthcoming book on Data Science for IoT
Image source: wikipedia