A cooling load prediction method using improved CEEMDAN and Markov Chains correction

Gao, Yuefen; Yang, Hang; Yang, Mengliang

doi:10.1016/j.jobe.2021.103041

Cited by 13 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CEEMDAN algorithm has its advantages and is suitable for feature extraction and signal noise reduction during time domain analysis of nonsmooth, nonlinear data [27][28][29] but still suffers from small amounts of noise, low effective signal loss, and slow iteration speed [30,31]. The ICA algorithm can improve the accuracy and speed of signal separation, and CEEMDA-N-ICA joint noise reduction combines the advantages of both CEEMDAN and ICA to build on their strengths and avoid their weaknesses.…”

Section: Ceemdan-ica Noise Reduction Modelmentioning

confidence: 99%

Prediction model of land surface settlement deformation based on improved LSTM method: CEEMDAN-ICA-AM-LSTM (CIAL) prediction model

Zhu,

Qin,

Meng

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

The uneven settlement of the surrounding ground surface caused by subway construction is not only complicated but also liable to cause casualties and property damage, so a timely understanding of the ground settlement deformation in the subway excavation and its prediction in real time is of practical significance. Due to the complex nonlinear relationship between subway settlement deformation and numerous influencing factors, as well as the existence of a time lag effect and the influence of various factors in the process, the prediction performance and accuracy of traditional prediction methods can no longer meet industry demands. Therefore, this paper proposes a surface settlement deformation prediction model by combining noise reduction and attention mechanism (AM) with the long short-term memory (LSTM). The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA) methods are used to denoise the input original data and then combined with AM and LSTM for prediction to obtain the CEEMDAN-ICA-AM-LSTM (CIAL) prediction model. Taking the settlement monitoring data of the construction site of Urumqi Rail Transit Line 1 as an example for analysis reveals that the model in this paper has better effectiveness and applicability in the prediction of surface settlement deformation than multiple prediction models. The RMSE, MAE, and MAPE values of the CIAL model are 0.041, 0.033 and 0.384%; R2 is the largest; the prediction effect is the best; the prediction accuracy is the highest; and its reliability is good. The new method is effective for monitoring the safety of surface settlement deformation.

show abstract

Section: Ceemdan-ica Noise Reduction Modelmentioning

confidence: 99%

Prediction model of land surface settlement deformation based on improved LSTM method: CEEMDAN-ICA-AM-LSTM (CIAL) prediction model

Zhu,

Qin,

Meng

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The Markov chain model can effectively obtain the different factors to predict the stock futures to yield meaningful insights at different time intervals. The Markov chain is a completely random process with no after-effects that are used to solve prediction issues with volatility (Gao et al 2021 ). With conditions known at a given moment, the Markov chain solves the probability distribution at the following moment.…”

Section: Trading System Designmentioning

confidence: 99%

H3O-LGBM: hybrid Harris hawk optimization based light gradient boosting machine model for real-time trading

Gupta

Kumar

2023

Artif Intell Rev

View full text Add to dashboard Cite

The gross domestic product (GDP) of a country is mainly dependent on its trade and external sector which improves the country's income. According to FY2021–2022, India's nominal GDP is estimated to be 3.12 trillion US dollars. Overall exports and imports have a year-over-year increase of 49.6% and 68% respectively. Machine learning techniques have the potential to improve India's Current gross value by up to 15% by the year 2035. The integration of data, Technology, and talent helps to create intelligent models that enhance artificial intelligence growth. This paper presents an optimized light gradient boosting machine (Light GBM) model using the hybrid Harris hawk optimization (H 3 O) algorithm for trade forecasting. The overfitting problem in the conventional Harris Hawk Optimization is overcome using the exclusive feature bundling (EFB) and the gradient-based one-side sampling (GOSS) methodologies. The H 3 O optimization algorithm offers fast convergence by optimizing different lightGBM parameters such as a number of training iterations, maximum depth, minimal data in the leaf, etc. To improve the performance, one step further, the residual errors of the optimized lightGBM model are corrected using the Markov Chain model. The main aim of the optimized lightGBM model is to extract the crucial input values of certain variables such as imports and exports of goods and services, service trade, and merchandise trade and predict the price movement decision. The proposed model identifies the interrelationship with the external market and future market growth along with analyzing the variation in market conditions. The prediction decision is mainly to hold, but, or sell the stocks. When evaluated using the precious metal price forecast and stock market datasets, the proposed methodology shows that the hybrid approach can enhance the prediction performance. The results show that the input parameters were efficient in predicting the economic growth regarding the Intermarket trading system (ITS) and services with higher accuracy.

show abstract

“…Step 4. Select the effective IMF components based on the Spearman correlation coefficient [34][35][36] as in Equation ( 12) Step 1. Add n groups of noise β 0 E 1 (W(i)) to the original signal X to obtain the noiseadded signal x(i) = x + β 0 E 1 (W(i)), and perform EMD on the noise-added signal to obtain n local means M(x(i)).…”

Section: Combined Asvd-iceemdan Noise Reductionmentioning

confidence: 99%

“…Step 4. Select the effective IMF components based on the Spearman correlation coefficient [34][35][36] as in Equation ( 12)…”

Section: Numerical Simulationmentioning

confidence: 99%

A Combined Noise Reduction Method for Floodgate Vibration Signals Based on Adaptive Singular Value Decomposition and Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Wang,

Zhu,

Cheng

et al. 2023

Water

View full text Add to dashboard Cite

To address the issue of the vibration characteristic signals of floodgates being affected by background white noise and low-frequency water flow noise, a noise reduction method combining the improved adaptive singular value decomposition algorithm (ASVD) and the improved complete ensemble EMD with adaptive noise (ICEEMDAN) is proposed. Firstly, a Hankel matrix is constructed based on the collected discrete time signals. After performing SVD on the Hankel matrix, the ASVD algorithm is used to automatically select the effective singular values to filter out most of the background white noise and retain the useful frequency components with similar energy in the signal. Then, ICEEMDAN combined with the Spearman correlation coefficient method is used to further filter out residual white noise and low-frequency water flows. The noise reduction performance of this combined method is verified through simulation experiments. Filtered by the ASVD-ICEEMDAN method, the signal-to-noise ratio of the simulation signal (50% noise level) is increased from 4.417 to 16.237, and the root mean square error is reduced from 2.286 to 0.586. Based on the practically measured vibration signals of a floodgate at a large hydropower station, the result shows that the ASVD-ICEEMDAN method exhibits good noise reduction performance and feature information extraction abilities for floodgate vibration signals, and can provide support for operational mode analysis and damage identification of practical structures under complex interference conditions.

show abstract

A cooling load prediction method using improved CEEMDAN and Markov Chains correction

Cited by 13 publications

References 24 publications

Prediction model of land surface settlement deformation based on improved LSTM method: CEEMDAN-ICA-AM-LSTM (CIAL) prediction model

Prediction model of land surface settlement deformation based on improved LSTM method: CEEMDAN-ICA-AM-LSTM (CIAL) prediction model

H3O-LGBM: hybrid Harris hawk optimization based light gradient boosting machine model for real-time trading

A Combined Noise Reduction Method for Floodgate Vibration Signals Based on Adaptive Singular Value Decomposition and Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Contact Info

Product

Resources

About