Prophet-EEMD-LSTM based method for predicting energy consumption in the paint workshop

Lu, Yingkang; Sheng, Buyun; Fu, Gaocai; Luo, Ruiping; Chen, Geng; Huang, Yuzhe

doi:10.1016/j.asoc.2023.110447

Cited by 15 publications

(7 citation statements)

References 26 publications

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“…In ref. [54], the authors broadly classify research methods in the field of energy consumption prediction into statistical methods (autoregressive integrated moving average model (ARIMA) and its hybrid models-ARIMA, SVR, and PSO (particle swarm optimization); ARIMA-graphical model (GM); ARIMA and SVR for short-term power load forecasting; and exponential smoothing (ES)), which are able to predict time series data using a relatively small number of samples), machine learning, and, separately, deep learning.…”

Section: Reference Scientific Noveltymentioning

confidence: 99%

“…In ref. [54] the authors proposed an energy consumption prediction model based on Prophet-EEMD-LSTM.…”

Section: Reference Scientific Noveltymentioning

confidence: 99%

“…There are two basic conditions which need to be satisfied for the modal function. They are: that "in the entire data segment the number of extreme points and zero points is the same or differ by one" and "the upper and lower envelopes of energy consumption data are locally symmetric about the time axis" [54].…”

Section: Reference Scientific Noveltymentioning

confidence: 99%

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Review of Energy-Related Machine Learning Applications in Drying Processes

Đaković,

Kljajić,

Milivojević

et al. 2023

Energies

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Drying processes are among the most energy-intensive industrial processes. There is a need for development of the efficient methods needed for estimating, measuring, and reducing energy use. Different machine learning algorithms might provide some of the answers to these issues in a faster and less costly way, without the need for time-consuming and expensive experiments done at different scales of the dryers. The aim of this paper was to provide a comprehensive overview of machine learning applications for addressing energy-related challenges by exploration of different energy types and energy reduction opportunities. Also, the analysis of the applied algorithms, their specific applications and a critical evaluation of the obtained results are provided. The paper is based on the necessity of the improvements in energy use needed for drying related on the existing data. The overview of the ways for such achievements, and a general classification of machine learning algorithm are the background of the paper. The methods used are the machine learning techniques employed in different energy-related issues for drying processes. The paper focuses on the applications of artificial neural networks and other machine learning algorithms and models for different energy-related issues, including different energy types applications, challenges associated with energy consumption, and opportunities for energy reduction. Not only the applied algorithms, but also their specific applications, and the statistical analysis of the obtained results are also overviewed. Finally, a critical evaluation of the findings highlighting the potentials of machine learning algorithms in addressing energy-related challenges (such as estimation of energy consumption, opportunities for energy reduction, and use of different energy sources) is provided. The presented analysis underscored the effectiveness of machine learning applications for these purposes.

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Section: Reference Scientific Noveltymentioning

confidence: 99%

“…In ref. [54] the authors proposed an energy consumption prediction model based on Prophet-EEMD-LSTM.…”

Section: Reference Scientific Noveltymentioning

confidence: 99%

See 1 more Smart Citation

Review of Energy-Related Machine Learning Applications in Drying Processes

Đaković,

Kljajić,

Milivojević

et al. 2023

Energies

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“…Data-driven modeling refers to relying on data mining methods to find the mapping relationship between variables and outputs in data, without relying on expert experience and defining assumptions, without analyzing internal mechanisms, which belongs to black box modeling and strong flexibility, suitable for handling nonlinear process. Therefore, artificial neural network (ANN) [7], convolutional neural network (CNN) [8], extreme learning machines (ELMs) [9], least squares support vector regression (LSSVR) [10], and longshort term memory network (LSTM) [11] are widely used in chemical production process [12]. Especially, LSTM is widely used in modeling methods due to its advantages in processing long-time series and long-term memory information.…”

Section: Introductionmentioning

confidence: 99%

Research on soft sensor modeling method for complex chemical processes based on local semi-supervised selective ensemble learning

Liu,

Li,

Zhang

et al. 2024

Meas. Sci. Technol.

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To address the adverse effects of nonlinearity and dynamic time-varying in complex chemical processes on the accuracy of the soft sensor model, a local-semi-supervised ensemble learning for soft sensor modeling (LS-SEL-LSTM) method is proposed in this article. Firstly, a hierarchical clustering method incorporating spatiotemporal criteria is proposed to reduce the influence of nonlinearity in global model prediction accuracy. The method considers the dynamic time-varying characteristics of temporal data and generates multiple local datasets. Then, to address the issue of multi-rate between auxiliary variables and dominant variables, a semi-supervised weight fusion mechanism based on temporal correlation is proposed, which effectively utilizes auxiliary variables to reconstruct local semi-supervised datasets and establishes local soft sensing models using LSTM. Concurrently, the parameters of the established model were optimized using the Flower Pollination Algorithm (FPA). Subsequently, a selective ensemble learning method based on sub-model prediction accuracy and an adaptive combination weight calculation method for sub-models were proposed to improve the prediction accuracy. Finally, the effectiveness of the proposed method was verified through the actual dataset of the sulfur recovery process. The results indicate that LS-SEL-LSTM performs well in handling complex chemical processes with nonlinear and dynamic time-varying characteristics.

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“…Then, the Gated Recurrent Unit (GRU) method was employed to learn the nonlinear relationship between the input data and forecasted result. Lu et al [33] used the Prophet method to extract periodic and trend components from electricity load data. They then applied Ensemble Empirical Mode Decomposition (EEMD) to extract specific modal components from the residuals.…”

Section: Introductionmentioning

confidence: 99%

Prophet–CEEMDAN–ARBiLSTM-Based Model for Short-Term Load Forecasting

Yang,

Zhang,

Chen

et al. 2024

Future Internet

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Accurate short-term load forecasting (STLF) plays an essential role in sustainable energy development. Specifically, energy companies can efficiently plan and manage their generation capacity, lessening resource wastage and promoting the overall efficiency of power resource utilization. However, existing models cannot accurately capture the nonlinear features of electricity data, leading to a decline in the forecasting performance. To relieve this issue, this paper designs an innovative load forecasting method, named Prophet–CEEMDAN–ARBiLSTM, which consists of Prophet, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the residual Bidirectional Long Short-Term Memory (BiLSTM) network. Specifically, this paper firstly employs the Prophet method to learn cyclic and trend features from input data, aiming to discern the influence of these features on the short-term electricity load. Then, the paper adopts CEEMDAN to decompose the residual series and yield components with distinct modalities. In the end, this paper designs the advanced residual BiLSTM (ARBiLSTM) block as the input of the above extracted features to obtain the forecasting results. By conducting multiple experiments on the New England public dataset, it demonstrates that the Prophet–CEEMDAN–ARBiLSTM method can achieve better performance compared with the existing Prophet-based ones.

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Prophet-EEMD-LSTM based method for predicting energy consumption in the paint workshop

Cited by 15 publications

References 26 publications

Review of Energy-Related Machine Learning Applications in Drying Processes

Review of Energy-Related Machine Learning Applications in Drying Processes

Research on soft sensor modeling method for complex chemical processes based on local semi-supervised selective ensemble learning

Prophet–CEEMDAN–ARBiLSTM-Based Model for Short-Term Load Forecasting

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