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Energies 2019, 12(6), 1006;

A Microforecasting Module for Energy Management in Residential and Tertiary Buildings

Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, 70125 Bari, Italy
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in 18th IEEE - EEEIC, Palermo, Italy, 2018.
All the authors contributed equally to this work.
Received: 24 January 2019 / Revised: 9 March 2019 / Accepted: 11 March 2019 / Published: 15 March 2019
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The paper describes the methodology used for developing an electric load microforecasting module to be integrated in the Energy Management System (EMS) architecture designed and tested within the “Energy Router” (ER) project. This Italian R&D project is aimed at providing non-industrial active customers and prosumers with a monitoring and control device that would enable demand response through optimization of their own distributed energy resources (DERs). The optimal control of resources is organized with a hierarchical control structure and performed in two stages. A cloud-based computation platform provides global control functions based on model predictive control whereas a closed-loop local device manages actual monitoring and control of field components. In this architecture, load forecasts on a small scale (a single residential or tertiary building) are needed as inputs of the predictive control problem. The microforecasting module aimed at providing such inputs was designed to be flexible, adaptive, and able to treat data with low time resolution. The module includes alternative forecasting techniques, such as autoregressive integrated moving average (ARIMA), neural networks, and exponential smoothing, allowing the application of the right forecasting strategy each time. The presented test results are based on a dataset acquired during a monitoring campaign in two pilot systems, installed during the ER Project in public buildings. View Full-Text
Keywords: forecasting; home energy management systems; control; optimization; cloud-computing forecasting; home energy management systems; control; optimization; cloud-computing

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Bruno, S.; Dellino, G.; La Scala, M.; Meloni, C. A Microforecasting Module for Energy Management in Residential and Tertiary Buildings . Energies 2019, 12, 1006.

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