A Spatio-Temporal Deep Learning Network for the Short-Term Energy Consumption Prediction of Multiple Nodes in Manufacturing Systems

Guo, Jianhua; Han, Mingdong; Zhan, Guozhi; Liu, Shaopeng

doi:10.3390/pr10030476

Cited by 9 publications

(4 citation statements)

References 37 publications

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“…Their proposed scheme captures the fused spatial features using CNN, LSTM for the temporal information, and variable-weighted calculations using the attention mechanism. Likewise, Guo et al [82] employed historical energy consumption time series and previous knowledge of material flow to propose a spatial-temporal deep learning network (STDLN) framework, which merges a GCN and a GRU and forecasts the energy consumption of nodes.…”

Section: Predictive Maintenancementioning

confidence: 99%

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

et al. 2023

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Cyber-physical systems (CPS) refer to systems that integrate communication, control, and computational elements into physical processes to facilitate the control of physical systems and effective monitoring. The systems are designed to interact with the physical world, monitor and control the physical processes while in operation, and generate data. Deep Neural Networks (DNN) comprise multiple layers of interconnected neurons that process input data to produce predictions. Spatial-temporal data represents the physical world and its evolution over time and space. The generated spatial-temporal data is used to make decisions and control the behavior of CPS. This paper systematically reviews the applications of DNNs, namely convolutional, recurrent, and graphs, in handling spatial-temporal data in CPS. An extensive literature survey is conducted to determine the areas in which DNNs have successfully captured spatial-temporal data in CPS and the emerging areas that require attention. The research proposes a three-dimensional framework that considers: CPS (transportation, manufacturing, and others), Target (spatial-temporal data processing, anomaly detection, predictive maintenance, resource allocation, real-time decisions, and multi-modal data fusion), and DNN schemes (CNNs, RNNs, and GNNs). Finally, research areas that need further investigation are identified, such as performance and security. Addressing data quality, strict performance assurance, reliability, safety, and security resilience challenges are the areas that are required for further research.

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Section: Predictive Maintenancementioning

confidence: 99%

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

et al. 2023

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“…They performed a solar analysis to construct a directed-weighted graph of the project, where nodes represent buildings and edges represent the solar impacts. Guo et al [24] combined GCN and GRU to predict the future energy consumption of 140 locations in a large-scale aluminum profile plant located in Guangdong, China. Lu et al [25] proposed a GCN-based model for the estimation of the design loads of complex-shaped buildings.…”

Section: Introductionmentioning

confidence: 99%

Estimating Spatio-Temporal Building Power Consumption Based on Graph Convolution Network Method

Vontzos,

Laitsos,

Charakopoulos

et al. 2024

Dynamics

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Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson’s correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R2), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures.

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“…Most previous literature on energy prediction is based on several regression models with a few parameters [8][9][10][11][12], although some of these studies analyze the relationship between energy consumption and climate change [13][14][15][16][17]. Other studies analyze the energy consumption in Korean buildings [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Regression Framework for Energy Consumption in Smart Cities with Encoder-Decoder Recurrent Neural Networks

Carrera,

Kim

2023

Energies

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Currently, a smart city should ideally be environmentally friendly and sustainable, and energy management is one method to monitor sustainable use. This research project investigates the potential for a “smart city” to improve energy management by enabling the adoption of various types of intelligent technology to improve the energy sustainability of a city’s infrastructure and operational efficiency. In addition, the South Korean smart city region of Songdo serves as the inspiration for this case study. In the first module of the proposed framework, we place a strong emphasis on the data capabilities necessary to generate energy statistics for each of the numerous structures. In the second phase of the procedure, we employ the collected data to conduct a data analysis of the energy behavior within the microcities, from which we derive characteristics. In the third module, we construct baseline regressors to assess the proposed model’s varying degrees of efficacy. Finally, we present a method for building an energy prediction model using a deep learning regression model to solve the problem of 48-hour-ahead energy consumption forecasting. The recommended model is preferable to other models in terms of R2, MAE, and RMSE, according to the study’s findings.

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A Spatio-Temporal Deep Learning Network for the Short-Term Energy Consumption Prediction of Multiple Nodes in Manufacturing Systems

Cited by 9 publications

References 37 publications

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

Estimating Spatio-Temporal Building Power Consumption Based on Graph Convolution Network Method

A Regression Framework for Energy Consumption in Smart Cities with Encoder-Decoder Recurrent Neural Networks

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