The Forecastability of Underlying Building Electricity Demand from Time
  Series Data
Mohamad Khalil A. Stephen McGough Hussain Kazmi Sara Walker
Forecasting building energy consumption has become a promising solution in
Building Energy Management Systems for energy saving and optimization.
Furthermore, it can play an important role in the efficient management of the
operation of a smart grid. Different data-driven approaches to forecast the
future energy demand of buildings at different scale, and over various time
horizons, can be found in the scientific literature, including extensive
Machine Learning and Deep Learning approaches. However, the identification of
the most accurate forecaster model which can be utilized to predict the energy
demand of such a building is still challenging.In this paper, the design and
implementation of a data-driven approach to predict how forecastable the future
energy demand of a building is, without first utilizing a data-driven
forecasting model, is presented. The investigation utilizes a historical
electricity consumption time series data set with a half-hour interval that has
been collected from a group of residential buildings located in the City of
London, United Kingdom