Short-term load forecasting in a non-residential building contrasting models and attributes

Abstract

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.

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“…A majority of the literature focuses on a single building or a small set of buildings case studies. Other ML reviews in the building industry show similar libraries of work [11,12].…”

Overview of the use of artificial neural networks for energy‐related applications in the building sector

“…Wang and Srinivasan came to a similar conclusion in their review since they found that ensemble models have better prediction accuracy than single models (multiple linear regression [MLR], ANN, and SVR). Massana et al found similar results comparing ANN and linear regression for electric load forecasting of a nonresidential building, but in their application, SVR was slightly better than ANN. Zhao et al focused on the energy consumption prediction of office buildings using variable refrigerant volume (VRV) cooling systems.…”

A Hybrid Model of EMD and PSO-SVR for Short-Term Load Forecasting in Residential Quarters

“…As an integral part of the daily operational management of power utility, accuracy prediction of the short-term electric load in residential quarters is of great significance to urban power network planning and the electric power market operating. The overestimation will raise the operating cost while underestimation will lead to electricity shortage [3]. Because of the limited capacities on the user side, the load characteristics have less smoothing effect.…”