An accurate short-term load forecasting system allows an optimum daily operation of the power system and a suitable process of decision-making, such as with regard to control measures, resource planning or initial investment, to be achieved. In a previous work, the authors demonstrated that an SVR model to forecast the electric load in a non-residential building using only the temperature and occupancy of the building as attributes is the one that gives the best balance of accuracy and computational cost for the cases under study. Starting from this conclusion, a simple, low-computational requirements and economical hourly consumption prediction method, based on SVR model and only the calculated occupancy indicator as attribute, is proposed. The method, unlike the others, is able to perform hourly predictions months in advance using only the occupancy indicator.Due to the relevance of the occupancy indicator in the model, this paper provides a complete study of the methods and data sources employed in the creation of the artificial occupancy attributes. Several occupancy indicators are defined, from the simplest one, using general information, to the most complex one, based on very detailed information. Then, a load forecasting performance discrimination between the artificial occupancy attributes is realized demonstrating that using the most complex indicator increases the workload and complexity while not improving the load prediction significantly. A real case study, applying the forecasting method to several non-residential buildings in the University of Girona, serve as a demonstration.

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 exampleThis research project has been partially funded through BR-UdGScholarship of the University of Girona granted to Joaquim MassanaRaurich. Work developed with the support of the research groupSITES awarded with distinction by the Generalitat de Catalunya(SGR 2014–2016), the MESC project funded by the Spanish MINECO (Ref. DPI2013-47450-C2-1-R) and the European Union’s Horizon2020 Research and Innovation Programme under grant agreementNo 68070

An accurate short-term load forecasting system allows an optimum daily operation of the power system and a suitable process of decision-making, such as with regard to control measures, resource planning or initial investment, to be achieved. In a previous work, the authors demonstrated that an SVR model to forecast the electric load in a non-residential building using only the temperature and occupancy of the building as attributes is the one that gives the best balance of accuracy and computational cost for the cases under study. Starting from this conclusion, a simple, low-computational requirements and economical hourly consumption prediction method, based on SVR model and only the calculated occupancy indicator as attribute, is proposed. The method, unlike the others, is able to perform hourly predictions months in advance using only the occupancy indicator. Due to the relevance of the occupancy indicator in the model, this paper provides a complete study of the methods and data sources employed in the creation of the artificial occupancy attributes. Several occupancy indicators are defined, from the simplest one, using general information, to the most complex one, based on very detailed information. Then, a load forecasting performance discrimination between the artificial occupancy attributes is realized demonstrating that using the most complex indicator increases the workload and complexity while not improving the load prediction significantly. A real case study, applying the forecasting method to seveThis research project has been partially funded through BR-UdG Scholarship ofthe University of Girona granted to Joaquim Massana Raurich. Work developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 2014-2016) and the MESC project funded by the Spanish MINECO (Ref. DPI2013-47450-C2-1-R

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