A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm

He, Feifei; Zhou, Jianzhong; Feng, Zhong-kai; Liu, Guangbiao; Yang, Yuqi

doi:10.1016/j.apenergy.2019.01.055

Cited by 275 publications

(86 citation statements)

References 34 publications

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“…For the time series including wind speed, water inflow and so on, whose frequency scales are difficult to determine in advance due to the characteristics of randomness, intermittence and volatility, the mode number of VMD will be difficult to determine. Following the previous literature [23], [31]- [33], the mode number of VMD is predetermined by the subjective experience of scholars and optimized by swarm intelligence algorithms, which are not adaptive to different data as well as being time-consuming when adopting the iterative based algorithm. By observing the centre frequency distribution of each component with various mode number K, it can be seen that the centre frequencies of adjacent components will be convergent as the value of K increases, thus resulting in mode mixing problem [23].…”

Section: Structure Of the Proposed Model A Select Appropriate Mmentioning

confidence: 99%

A Sequential Structure for Water Inflow Forecasting in Coal Mines Integrating Feature Selection and Multi-Objective Optimization

Chen

Dong

2020

IEEE Access

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To construct an accurate and stable approach for water inflow forecasting, a series of advanced and effective techniques, such as variational mode decomposition (VMD), outlier robust extreme learning machine (ORELM) and multi-objective grey wolf optimizer (MOGWO), are appropriately integrated into this study. Considering that the influence of the mode number on the VMD decomposition effectiveness, such an argument is determined by observing the converged centre frequency distribution among the components. Then the characteristic items of water inflow series are extracted by VMD, thus obtaining a series of sub-components. Afterwards, ORELM is applied to predict each component, where the parameters of ORELM are optimized by MOGWO with multi-objective functions including forecasting accuracy and stability. Correspondingly, the aggregation of all components' prediction values is considered as the final results. The experimental results obtained by performing eight various models on real-time data demonstrate that the supplementary modules achieve positive effects on the improvement of prediction accuracy, where the proposed model implements an average performance promotion of 48.43% compared with the contrastive models. INDEX TERMS Water inflow prediction, variational mode decomposition, centre frequency distribution, outlier robust extreme learning machine, multi-objective grey wolf optimizer

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Section: Structure Of the Proposed Model A Select Appropriate Mmentioning

confidence: 99%

A Sequential Structure for Water Inflow Forecasting in Coal Mines Integrating Feature Selection and Multi-Objective Optimization

Chen

Dong

2020

IEEE Access

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“…This method has attracted much attention due to its solid theoretical foundation, strong noise robustness and precise component separation [41]. The hybrids AI and VMD models have successfully been employed in power quality events recognition [42], short-term load forecasting [43], time frequency analysis of Mirnov coil [44], stock price and movement prediction [45], short-term wind power generation forecasting [46], wind speed forecasting [47], and solar radiation forecasting [48]. In the hydrological domain, runoff and rainfall-runoff predictions were mainly focused upon.…”

Section: Introductionmentioning

confidence: 99%

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Feng

Wen

et al. 2021

Water

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Precise multi-time scales prediction of groundwater level is essential for water resources planning and management. However, credible and reliable predicting results are hard to achieve even to extensively applied artificial intelligence (AI) models considering the uncontrollable error, indefinite inputs and unneglectable uncertainty during the modelling process. The AI model ensembled with the data pretreatment technique, the input selection method, or uncertainty analysis has been successfully used to tackle this issue, whereas studies about the comprehensive deterministic and uncertainty analysis of hybrid models in groundwater level forecast are rarely reported. In this study, a novel hybrid predictive model combining the variational mode decomposition (VMD) data pretreatment technique, Boruta input selection method, bootstrap based uncertainty analysis, and the extreme learning machine (ELM) model named VBELM was developed for 1-, 2- and 3-month ahead groundwater level prediction in a typical arid oasis area of northwestern China. The historical observed monthly groundwater level, precipitation and temperature data were used as inputs to train and test the model. Specifically, the VMD was used to decompose all the input-outputs into a set of intrinsic mode functions (IMFs), the Boruta method was applied to determine input variables, and the ELM was employed to forecast the value of each IMF. In order to ascertain the efficiency of the proposed VBELM model, the performance of the coupled model (VELM) hybridizing VMD with ELM algorithm and the single ELM model were estimated in comparison. The results indicate that the VBELM performed best, while the single ELM model performed the worst among the three models. Furthermore, the VBELM model presented lower uncertainty than the VELM model with more observed groundwater level values falling inside the confidence interval. In summary, the VBELM model demonstrated an excellent performance for both certainty and uncertainty analyses, and can serve as an effective tool for multi-scale groundwater level forecasting.

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“…Both techniques are directed by performance metrics that are evaluated by cross-validation on the training and validation sets. Bayesian optimization [15], [17], [32]- [34] is used to optimize parameters by creating a probabilistic model of the functional mapping from the values of the parameters to the objective that is evaluated on a validation set.…”

Section: Introductionmentioning

confidence: 99%

Locational Marginal Price Forecasting Using Deep Learning Network Optimized by Mapping-Based Genetic Algorithm

2020

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The convolutional neural network (CNN) is commonly used in visual recognitions and classifications. However, CNN can also be applied as a forecaster that can extract features from spatiotemporal data. This paper proposes a 24h ahead electricity price forecasting method, which integrates CNN with an evolutionary algorithm and utilizes spatiotemporal data. The optimal structure of the CNN network for the locational marginal price (LMP) forecasting was obtained using a genetic algorithm (GA). A gene mapping scheme was initially encoded to represent the search space and the process of selection, mutation, and crossover eliminated structures that did not satisfy the validation fitness function and then competitive individuals were generated. The evolution process uses the root mean square error (RMSE) as the validation fitness function, which is optimzed by training the created CNN network. The proposed gene mapping scheme can be used to design an optimal CNN structure once the mapping between gene binary bits and parameters/hyperparameters of CNN is given. Day-ahead LMP and demand datasets from Pennsylvania-New Jersey-Maryland (PJM) power market were used to demonstrate the evolutionary capability of the proposed method and the finding of optimal CNN structures. Each studied dataset was grouped into 4 subsets corresponding to various seasonal characteristics (different types of situations in real life). Experimental results revealed that the proposed GA-CNN always yielded a higher forecasting accuracy and lower error rates than other forecasting methods. INDEX TERMS Convolutional neural network, deep learning, electricity price forecasting, genetic algorithm, locational marginal price.

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A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm

Cited by 275 publications

References 34 publications

A Sequential Structure for Water Inflow Forecasting in Coal Mines Integrating Feature Selection and Multi-Objective Optimization

A Sequential Structure for Water Inflow Forecasting in Coal Mines Integrating Feature Selection and Multi-Objective Optimization

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Locational Marginal Price Forecasting Using Deep Learning Network Optimized by Mapping-Based Genetic Algorithm

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