Automatic construction and natural-language description of nonparametric regression models

Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural- language text. Our approach treats unknown regression functions non- parametrically using Gaussian processes, which has two important consequences. First, Gaussian processes can model functions in terms of high-level properties (e.g. smoothness, trends, periodicity, changepoints). Taken together with the compositional structure of our language of models this allows us to automatically describe functions in simple terms. Second, the use of flexible nonparametric models and a rich language for composing them in an open-ended manner also results in state- of-the-art extrapolation performance evaluated over 13 real time series data sets from various domains.

A bottom-up energy analysis across a diverse urban building portfolio: Retrofits for the buildings at the Royal Botanic Gardens, Kew, UK

A methodology for the analysis of building energy retrofits has been developed for a diverse set of buildings at the Royal Botanic Gardens (RBG), Kew in southwest London, UK. The methodology requires selection of appropriate building simulation tools dependent on the nature of the principal energy demand. This has involved the development of a stand-alone model to simulate the heat flow in botanical glasshouses, as well as stochastic simulation of electricity demand for buildings with high equipment density and occupancy-led operation. Application of the methodology to the buildings at RBG Kew illustrates the potential reduction in energy consumption at the building scale achievable from the application of retrofit measures deemed appropriate for heritage buildings and the potential benefit to be gained from onsite generation and supply of energy. © 2014 Elsevier Ltd.