A Smart Grid approach to electric distribution system management needs to front uncertainties in generation and demand thus making forecasting an up-to-date area of research in electric energy systems. This works aims to propose a day-ahead load forecasting procedure for a medium voltage customer. The load forecasting is performed through the implementation of an artificial neural network (ANN). The proposed multi-layer perceptron ANN, based on backpropagation 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 (e.g. temperature, humidity). This procedure has been tested to predict the loads of a large university hospital facility located in Rome.

Artificial Neural Network Application to Load Forecasting in a Large Hospital Facility

GRILLO, SAMUELE;
2014

Abstract

A Smart Grid approach to electric distribution system management needs to front uncertainties in generation and demand thus making forecasting an up-to-date area of research in electric energy systems. This works aims to propose a day-ahead load forecasting procedure for a medium voltage customer. The load forecasting is performed through the implementation of an artificial neural network (ANN). The proposed multi-layer perceptron ANN, based on backpropagation 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 (e.g. temperature, humidity). This procedure has been tested to predict the loads of a large university hospital facility located in Rome.
Proceedings of the International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2014
9781479935611
Load forecasting, artificial neural network, distributed generation, smart grids, elettrici
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/818322
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