Ultra-Short-Term Industrial Power Demand Forecasting Using LSTM Based Hybrid Ensemble Learning

Tan, Mao; Yuan, Siping; Li, Shuaihu; Su, Yongxin; Li, Hui; He, Feng

doi:10.1109/tpwrs.2019.2963109

Cited by 241 publications

(78 citation statements)

References 30 publications

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“…Uyar et al [34] presented one trained recurrent fuzzy neural system with the use of a genetic algorithm for improving long-term dry cargo freight rates prediction to be more accurate. As an efficient strategy to improve the forecasting ability of a single model, ensemble learning has been widely used to improve the model performance [35][36][37][38]. For example, Kamal et al [35] developed a deep ensemble recurrent network of recurrent neural network (RNN), long-short-term memory (LSTM), and gated rectified unit neural network (GRU) to improve the BDI predictive performance, and results showed that the ensemble method outperforms the single deep-learning approach.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction

Xiao

Liu

et al. 2021

Journal of Advanced Transportation

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China Coastal Bulk Coal Freight Index (CBCFI) reflects how the coastal coal transporting market’s freight rates in China are fluctuated, significantly impacting the enterprise’s strategic decisions and risk-avoiding. Though trend analysis on freight rate has been extensively conducted, the property of the shipping market, i.e., it varies over time and is not stable, causes CBCFI to be hard to be accurately predicted. A novel hybrid approach is developed in the paper, integrating Long Short-Term Memory (LSTM) and ensemble learning techniques to forecast CBCFI. The hybrid LSTM-based ensemble learning (LSTM-EL) approach predicts the CBCFI by extracting the time-dependent information in the original data and incorporating CBCFI-related data, e.g., domestic and overseas thermal coal spot prices, coal inventory, the prices of fuel oil, and crude oil. To demonstrate the applicability and generality of the proposed approach, different time-scale datasets (e.g., daily, weekly, and monthly) in a rolling forecasting experiment are conducted. Empirical results show that domestic and overseas thermal coal spot prices and crude oil prices have great influences on daily, weekly, and monthly CBCFI values. And in daily, weekly, and monthly forecasting cases, the LSMT-EL approaches have higher prediction accuracy and a greater trend complying ratio than the relevant single ensemble learning algorithm. The hybrid method outperforms others when it works with information involving a dramatic market recession, elucidating CBCFI’s predictable ability. The present work is of high significance to general commerce, commerce-related, and hedging strategic procedures within the coastal shipping market.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction

Xiao

Liu

et al. 2021

Journal of Advanced Transportation

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“…Rural environmental pollution management is a process in which the government and private organizations use large numbers of measures and methods to manage and change the rural environment to achieve a stable and sustainable socioeconomic development in rural areas. It includes not only the management of industrial, domestic, and agricultural pollution in rural areas but also the establishment of a sound governmental environmental pollution management mechanism, the introduction of some market management mechanisms, and the improvement of people's awareness of environmental protection and their participation in environmental pollution management [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Combination Evaluation Method of Rural Environmental Pollution Control Effect

2021

Complexity

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In recent years, the problem of environmental pollution has become more and more serious, and environmental pollution has become a topic of concern. PM2.5, PM10, hazy weather, and other words about environmental pollution have become hot words and topics for the media and the public to talk about, and environmental pollution control is called for by the media and the public. This reflects the rapid development of economy and the obvious changes in people’s living standards, and people have higher requirements and aspirations for their lives and the environment they live in, and their awareness of environmental protection is getting stronger and stronger; however, it also reflects that the rapid development of the economy comes at the cost of damaging the environment. This paper analyzes the current situation and governance dilemma of rural environment by means of field research from dynamic portfolio evaluation theory, explains the problems and causes of environmental pollution management under the requirement of polycentric governance, and proposes countermeasures to solve the problem of environmental pollution management in county. The paper aims to analyze the current state of environmental pollution in rural areas through literature review, villagers’ field interviews, and field research and to explain the application of polycentric governance to rural environmental pollution management in county in the region.

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“…DL techniques, especially Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) achieve accurate and defensible forecasts than conventional forecasting-based modeling [14]. Authors in [15] used LSTM based ensemble learning. The proposed model is employed for multi-step industrial power demand forecasting.…”

Section: Introductionmentioning

confidence: 99%

“…In [16], a multi-scale CNN with time-cognition is developed for STLF. A state-of-the-art time coding to emphasize the uniqueness of the moment in one period is implemented [15]. This gives more information about the samples periodicity [15].…”

Section: Introductionmentioning

confidence: 99%

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PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

et al. 2020

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This paper proposes an effective deep learning framework for Short-Term Load Forecasting (STLF) of multivariate time series. The proposed model consists of a hybrid Convolutional neural network-Bidirectional Long Short-Term Memory (CBiLSTM) based on the Evolution Strategy (ES) method and the Savitzky–Golay (SG) filter (SG-CBiLSTM). The adopted methodology incorporates the virtue of different prepossessing blocks to enhance the performance of the CBiLSTM model. In particular, a data-augmentation strategy is employed to synthetically improve the feature representation of the CBiLSTM model. The augmented data is forwarded to the Partial Least Square (PLS) method to select the most informative features above the predefined threshold. Next, the SG algorithm is computed for smoothing the load to enhance the learning capabilities of the underlying system. The structure of the SG-CBiLSTM for the ISO New England dataset is optimized using the ES technique. Finally, the CBiLSTM model generates output forecasts. The proposed approach demonstrates a remarkable improvement in the performance of the original CBiLSTM model. Furthermore, the experimental results strongly confirm the high effectiveness of the proposed SG-CBiLSTM model compared to the state-of-the-art techniques.

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Ultra-Short-Term Industrial Power Demand Forecasting Using LSTM Based Hybrid Ensemble Learning

Cited by 241 publications

References 30 publications

A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction

A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction

Dynamic Combination Evaluation Method of Rural Environmental Pollution Control Effect

PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

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