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

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, t...

Full description

Saved in:
Bibliographic Details
Published in:Information (Basel) 2019-06, Vol.10 (6), p.189
Main Authors: Nichiforov, Cristina, Stamatescu, Grigore, Stamatescu, Iulia, Făgărăşan, Ioana
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Building management systems
Buildings
Business metrics
Case studies
Complexity
Computer architecture
Cost control
Deep learning
Energy consumption
Energy management
energy modelling
Energy sources
Forecasting
Mathematical models
Neural networks
Recurrent neural networks
sequence models
smart buildings
Smart grid
Software
Time series
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2078-2489
2078-2489
DOI:10.3390/info10060189