Electrical consumption forecasting in hospital facilities: An application case

• Published in: Energy and buildings Vol. 103; pp. 261 - 270
• Main Authors: Bagnasco, A., Fresi, F., Saviozzi, M., Silvestro, F., Vinci, A.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15-09-2015
• Subjects: Building management systems; Electrical consumption in an hospital facility; Energy management systems; Load forecasting; Neural networks; Load forecasting; Electrical consumption in an hospital facility; Neural networks; Energy management systems; Building management systems
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2015.05.056

•We model the load consumption forecast of a private hospital facility.•A load-forecasting model is implemented with ANN and back-propagation algorithm.•An innovative formal procedure for the selection of the neural network parameters.•The proposed procedure can be easily implemented in BMS systems.•Results are very promising with limited forecast errors.
The topic of energy efficiency applied to buildings represents one of the key aspects in today's international energy policies. Emissions reduction and the achievement of the targets set by the Kyoto Protocol are becoming a fundamental concern in the work of engineers and technicians operating in the energy management field. Optimal energy management practices need to deal with uncertainties in generation and demand, hence the development of reliable forecasting methods is an important priority area of research in electric energy systems. This paper presents a load forecasting model and the way it was applied to a real case study, to forecast the electrical consumption of the Cellini medical clinic of Turin. The model can be easily integrated into a Building Management System or into a real time monitoring system. The load forecasting is performed through the implementation of an artificial neural network (ANN). The proposed multi-layer perceptron ANN, based on a back propagation training algorithm, is able to take as inputs: loads, data concerning the type of day (e.g. weekday/holiday), time of the day and weather data. In particular, this work focuses on providing a detailed analysis and an innovative formal procedure for the selection of all the ANN parameters.