Seasonal Effects
Process the weather data for the centres of Auckland and Hamilton in the given dataset (hourly readings) and experiment with various data mining techniques to see if a model can be generated that predicts power consumption between summer (January data) and winter (July data). Is it easier to predict the power usage for one of these seasons over the other?
The central theme to this project-shared across all the projects being run in this course-is to investigate the relationship between power usage in New Zealand, and chronological data (the time of day and the time of year) and meteorological data (the weather!) to see if any patterns exist; more specifically, to see whether the latter information helps predict the former. Each project investigates a separate aspect within this theme, applying Data Mining techniques to publically available data produced by both Transpower and the National Institute of Water and Atmospheric Research (NIWA), from which a range of visualizations will be generated.