Intelligent Load Forecasting for Building Energy Management Systems

Nichiforov, Cristina; Stâmâtescu, Grigore; Stamatescu, Iulia; Făgărăşan, Ioana; Iliescu, Sergiu Stelian

doi:10.1109/icca.2018.8444311

Cited by 9 publications

(3 citation statements)

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“…While defining the context of the current work, we also mentioned previous implementation that analyzed conventional system identification using Autoregressive Integrated Moving Average (ARIMA) models versus classical Artificial Neural Networks (ANN) [13]. Multiple ANN versions have been tested [14] in terms of number of hidden layers and number of neurons per layer. The deep-learning approach offers better results for our test scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…org). This includes active power load traces that are part of a data collection of several hundreds of nonresidential buildings, mainly academic venues, proposed for performance analysis and algorithm benchmarking to a common baseline [14,23].…”

Section: Benchmarking Datasetsmentioning

confidence: 99%

“…In our case, a 15th-order median filter was used by the preprocessing implementation. The key criteria for the selection were: medium-to-large building, mixed usage pattern-office, laboratory space, some classrooms-and nonextreme temperate climate, with four distinct seasons [14]. A guiding choice in selecting the four candidates from the 508 entities in the dataset was also similarity to a local university building from our campus, where a data-collection study is currently ongoing.…”

Section: Benchmarking Datasetsmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Benchmarking Datasetsmentioning

confidence: 99%

Section: Benchmarking Datasetsmentioning

confidence: 99%

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