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Open AccessArticle

Evaluation of Sequence-Learning Models for Large-Commercial-Building Load Forecasting

1
Department of Automatic Control and Industrial Informatics, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 313 Splaiul Independentei, 06004 Bucharest, Romania
2
Institute of Technical Informatics, Technical University of Graz, 16 Inffeldgasse, 8010 Graz, Austria
*
Author to whom correspondence should be addressed.
Information 2019, 10(6), 189; https://doi.org/10.3390/info10060189
Received: 15 April 2019 / Revised: 16 May 2019 / Accepted: 30 May 2019 / Published: 1 June 2019
(This article belongs to the Special Issue ICSTCC 2018: Advances in Control and Computers)
Buildings play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large-scale energy-management strategies from the supply side to the consumer side. When buildings integrate local renewable-energy generation in the form of renewable-energy resources, they become prosumers, and this adds more complexity to the operation of interconnected complex energy systems. A class of methods of modelling the energy-consumption patterns of the building have recently emerged as black-box input–output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produced by nondeterministic processes underlying energy consumption. We present an application of a class of neural networks, namely, deep-learning techniques for time-series sequence modelling, with the goal of accurate and reliable building energy-load forecasting. Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects, and are considered suitable for further use in future in situ energy management at the building and neighborhood levels. View Full-Text
Keywords: sequence models; recurrent neural networks; energy modelling; smart buildings sequence models; recurrent neural networks; energy modelling; smart buildings
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Nichiforov, C.; Stamatescu, G.; Stamatescu, I.; Făgărăşan, I. Evaluation of Sequence-Learning Models for Large-Commercial-Building Load Forecasting. Information 2019, 10, 189.

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