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

Text Complet
AM-ShortTermLoad.pdf embargoed access
Sol·licita còpia a l'autor de l'article
En omplir aquest formulari esteu demanant una còpia de l'article dipositat al repositori institucional (DUGiDocs) al seu autor o a l'autor principal de l'article. Serà el mateix autor qui decideixi lliurar una còpia del document a qui ho sol•liciti si ho creu convenient. En tot cas, la Biblioteca de la UdG no intervé en aquest procés ja que no està autoritzada a facilitar articles quan aquests són d'accés restringit.
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 seve ​
​Tots els drets reservats