Identifying services for short-term load forecasting using data driven models in a Smart City platform

The paper describes an ongoing work to embed several services
in a Smart City architecture with the aim of achieving a sustainable city. In
particular, the main goal is to identify services required in such framework
the requirements and features of a reference architecture to support
the data-driven methods for energy effciency monitoring or load prediction.
With this object in mind, a use case of short-term load forecasting in non-
residential buildings in the University of Girona is provided, in order to
practically explain the services embedded in the described general layers
architecture. In the work, classic data-driven models for load forecasting in
buildings are used as an example.

This paper is supplemented by a dataset accessible in the below link:

https://doi.org/10.5281/zenodo.3461703