Building Electrical Load Forecasting through Neural Network Models with Exogenous Inputs

Cristina Nichiforov, Grigore Stamatescu, Iulia Stamatescu, Ioana Fagarasan, Sergiu Stelian Iliescu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As buildings become key actors in the economic and sustainable operation of future electrical grids and smart cities, reliable models which capture the underlying electrical energy consumption become an important factor for robust control algorithms. Current ubiquitous field devices supported by complex data infrastructures allow generation, storage and online analysis of large quantities of data for deriving usable black-box models of building energy patterns. The paper presents an approach to model the energy consumption of medium and large sized buildings using Non-linear Autoregressive Neural Networks with eXogenous Input (NARX). We show that the chosen network architectures offers good performance for time series prediction from historical values and external input signals such as outdoor temperature in comparison to a baseline approach. Model evaluation and validation are carried out on public dataset for replicable research outcomes.
Original languageEnglish
Title of host publication2019 23nd International Conference on System Theory, Control and Computing (ICSTCC)
PublisherInstitute of Electrical and Electronics Engineers
Pages474-479
Number of pages6
ISBN (Electronic)978-1-7281-0699-1
DOIs
Publication statusPublished - 2019
EventICSTCC 2019: 23rd International Conference on System Theory, Control and Computing, - Sinaia, Romania
Duration: 9 Oct 201911 Oct 2019

Conference

ConferenceICSTCC 2019
CountryRomania
CitySinaia
Period9/10/1911/10/19

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