Short-term load forecasting for non-residential buildings contrasting artificial occupancy attributes

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 arti cial occupancy attributes. Several occupancy indicators are de ned, 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 arti cial occupancy attributes is realized demonstrating that using the most complex indicator increases the workload and complexity while not improving the load prediction signi cantly. A real case study, applying the forecasting method to several non-residential buildings in the University of Girona, serve as a demonstration. This paper is complemented by the dataset accessible in the below link: https://doi.org/10.5281/zenodo.3461728This research project has been partially funded through BR-UdG Scholarship of the 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)