Deep Learning Techniques for Load Forecasting in Large Commercial Buildings

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

doi:10.1109/icstcc.2018.8540768

Cited by 31 publications

(12 citation statements)

References 11 publications

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“…Nichiforov at al. [12] applied deep recurrent neural networks for load forecasting in large commercial buildings using long and short-term memory (LSTM) networks. Several LSTM networks, comprising a single LSTM layer of varying size and a single fully-connected layer, were tested on the one-year energy consumption data of two university campus buildings, collected at a 60-minute frequency.…”

Section: Building Energy Consumption Predictionmentioning

confidence: 99%

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

et al. 2019

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With the rising focus on building energy big data analysis, there lacks a framework for raw data preprocessing to answer the question of how to handle the missing data in the raw data set. This study presents a methodology and framework for building energy consumption raw data forecasting. A case building is used to forecast the energy consumption by using deep recurrent neural networks. Four different methodologies to impute missing data in the raw data set are compared and implemented. The question of sensitivity of gap size and available data percentage on the imputation accuracy was tested. The cleaned data were then used for building energy forecasting. While the existing studies explored only the use of small recurrent networks of 2 layers and less, the question of whether a deep network of more than 2 layers would be performing better for building energy consumption forecasting should be explored. In addition, the problem of overfitting has been cited as a significant problem in using deep networks. In this study, the deep recurrent neural network is then used to explore the use of deeper networks and their regularization in the context of an energy load forecasting task. The results show a mean absolute error of 2.1 can be achieved through the 2*32 gated neural network model. In applying regularization methods to overcome model overfitting, the study found that weights regularization did indeed delay the onset of overfitting.

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Section: Building Energy Consumption Predictionmentioning

confidence: 99%

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

et al. 2019

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“…Additional contributions that extend the previous conference paper [3] are summarised. We provided, as the main goal for the extended version, new experiment results for recurrent neural-network modelling of large-commercial-building energy consumption.…”

Section: Introductionmentioning

confidence: 89%

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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“…The authors explored various techniques to impute missing data, after which GRU forecasting models were tested. References [70,71] explored LSTM models targeting electricity load estimations of educational buildings. For instance, in reference paper [70] the authors demonstrated the effectiveness of different LSTM-based models compared with SVR, DBN, and ARIMA-based models.…”

Section: Institutionalmentioning

confidence: 99%

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

Runge

Zmeureanu

2021

Energies

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Buildings account for a significant portion of our overall energy usage and associated greenhouse gas emissions. With the increasing concerns regarding climate change, there are growing needs for energy reduction and increasing our energy efficiency. Forecasting energy use plays a fundamental role in building energy planning, management and optimization. The most common approaches for building energy forecasting include physics and data-driven models. Among the data-driven models, deep learning techniques have begun to emerge in recent years due to their: improved abilities in handling large amounts of data, feature extraction characteristics, and improved abilities in modelling nonlinear phenomena. This paper provides an extensive review of deep learning-based techniques applied to forecasting the energy use in buildings to explore its effectiveness and application potential. First, we present a summary of published literature reviews followed by an overview of deep learning-based definitions and techniques. Next, we present a breakdown of current trends identified in published research along with a discussion of how deep learning-based models have been applied for feature extraction and forecasting. Finally, the review concludes with current challenges faced and some potential future research directions.

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Deep Learning Techniques for Load Forecasting in Large Commercial Buildings

Cited by 31 publications

References 11 publications

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

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

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

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