  In this paper, the process of forecasting household energy consumption is
studied within the framework of the nonparametric Gaussian Process (GP), using
multiple short time series data. As we begin to use smart meter data to paint a
clearer picture of residential electricity use, it becomes increasingly
apparent that we must also construct a detailed picture and understanding of
consumer's complex relationship with gas consumption. Both electricity and gas
consumption patterns are highly dependent on various factors, and the intricate
interplay of these factors is sophisticated. Moreover, since typical gas
consumption data is low granularity with very few time points, naive
application of conventional time-series forecasting techniques can lead to
severe over-fitting. Given these considerations, we construct a stacked GP
method where the predictive posteriors of each GP applied to each task are used
in the prior and likelihood of the next level GP. We apply our model to a
real-world dataset to forecast energy consumption in Australian households
across several states. We compare intuitively appealing results against other
commonly used machine learning techniques. Overall, the results indicate that
the proposed stacked GP model outperforms other forecasting techniques that we
tested, especially when we have a multiple short time-series instances.
