Identifying services for short-term load forecasting using data driven models in a Smart City platform. (January 2017)

• Record Type: Journal Article
• Authors: Massana, Joaquim; Pous, Carles; Burgas, Llorenç; Melendez, Joaquim; Colomer, Joan
• Is Part Of: Sustainable cities and society. Volume 28(2017)
• Page Start: 108
• Page End: 117
• Publication Date: 2017-01
• Subjects: Short-term load forecasting -- Data mining -- Services -- Building -- Smart City architecture; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2016.09.001
• Languages: English
• ISSNs: 2210-6707

Abstract

Abstract : Highlights: A framework to support monitoring procedures in Smart Cities is defined. Several services are identified according to the layers architecture. A short-term load forecasting in non-residential buildings use case is provided. The short-term load forecasting method is based on an AR model. The work is based on a real data of the University of Girona. Abstract: 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 to define the requirements and features of a reference architecture to support the data-driven methods for energy efficiency 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.