A Microforecasting Module for Energy Management in Residential and Tertiary Buildings †

Sergio Bruno, Gabriella Dellino, Massimo La Scala and Carlo Meloni
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Sergio Bruno: Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, 70125 Bari, Italy
Gabriella Dellino: Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, 70125 Bari, Italy
Massimo La Scala: Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, 70125 Bari, Italy
Carlo Meloni: Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, 70125 Bari, Italy

Energies, 2019, vol. 12, issue 6, 1-20

Abstract: 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.

Keywords: forecasting; home energy management systems; control; optimization; cloud-computing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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