Short-Term Load Forecasts Using LSTM Networks

Muzaffar, Shahzad; Afshari, Afshin

doi:10.1016/j.egypro.2019.01.952

Cited by 236 publications

(125 citation statements)

References 6 publications

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“…The nature of this chain-link reveals the close relationship between sequences. Because LSTM is used to solve the gradient disappearance problem when RNN is employed to process the long-sequence data, LSTM is suitable for processing events with relatively long intervals and delays, such as speech recognition, machine translation, and time series prediction, in the time series [20][21][22]. Figure 2a presents the internal work unit of the LSTM network.…”

Section: Lstm Methodsmentioning

confidence: 99%

Prediction Model of Suspension Density in the Dense Medium Separation System Based on LSTM

et al. 2020

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In the dense medium separation system of coal preparation plant, the fluctuation of raw coal ash and lag of suspension density adjustment often causes the instability of product quality. To solve this problem, this study established a suspension density prediction model for the dense medium separation system based on Long Short-Term Memory (LSTM). First, the historical data in the dense medium separation system of a coal preparation plant were collected and preprocessed. Moving average and cubic exponential smoothing methods were used to replace abnormal data and to fill in the missing data, respectively. Second, a LSTM network was used to construct the density prediction model, and the optimal number of time steps, hidden layers, and nodes was determined. Finally, the model was employed on a testing set for prediction, and a Back-Propagation (BP) network without a time series was used for comparison. Root Mean Squared Error (RMSE) were the minimum when the number of the hidden layers, nodes, and time steps was 6, 12, and 5, respectively. In this case, the RMSE and Mean Absolute Percent Error (MAPE) of the LSTM method were 0.009 and 0.007, respectively, while those of the BP method were 0.019 and 0.015, respectively. Therefore, the model established using LSTM can be used to accurately predict the suspension density of the dense medium separation system.

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Section: Lstm Methodsmentioning

confidence: 99%

Prediction Model of Suspension Density in the Dense Medium Separation System Based on LSTM

et al. 2020

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“…The weights of the six pollutants. According to the above calculation results, the MAPE value of each city is low, indicating that the LSTM forecast model can predict the future trend of air quality in cities in Chengdu-Chongqing region well [93,94].…”

Section: Conflicts Of Interestmentioning

confidence: 98%

Analysis of the Effectiveness of Air Pollution Control Policies Based on Historical Evaluation and Deep Learning Forecast: A Case Study of Chengdu-Chongqing Region in China

et al. 2020

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Air pollution is a common problem for many countries around the world in the process of industrialization as well as a challenge to sustainable development. This paper has selected Chengdu-Chongqing region of China as the research object, which suffers from severe air pollution and has been actively involved in air pollution control in recent years to achieve sustainable development. Based on the historical data of 16 cities in this region from January 2015 to November 2019 on six major air pollutants, this paper has first conducted evaluation on the monthly air quality of these cities within the research period by using Principal Component Analysis and the Technique for Order Preference by Similarity to an Ideal Solution. Based on that, this paper has adopted the Long Short-Term Memory neural network model in deep learning to forecast the monthly air quality of various cities from December 2019 to November 2020. The aims of this paper are to enrich existing literature on air pollution control, and provide a novel scientific tool for design and formulation of air pollution control policies by innovatively integrating commonly used evaluation models and deep learning forecast methods. According to the research results, in terms of historical evaluation, the air quality of cities in the Chengdu-Chongqing region was generally moving in the same trend in the research period, with distinct characteristics of cyclicity and convergence. Year- on-year speaking, the effectiveness of air pollution control in various cities has shown a visible improvement trend. For example, Ya’an’s lowest air quality evaluation score has improved from 0.3494 in 2015 to 0.4504 in 2019; Zigong’s lowest air quality score has also risen from 0.4160 in 2015 to 0.6429 in 2019. Based on the above historical evaluation and deep learning forecast results, this paper has proposed relevant policy recommendations for air pollution control in the Chengdu-Chongqing region.

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“…In [16], a WaveNet based on a dilated causal residual convolutional neural network (CNN) and long short-term memory (LSTM) layers was proposed for load prediction. In [18], a multilayer LSTM network was used for load prediction.…”

Section: Related Workmentioning

confidence: 99%

Multiple Wavelet Convolutional Neural Network for Short-Term Load Forecasting

Liao

Pan

Fan

et al. 2021

IEEE Internet Things J.

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Although the accuracy of load forecasting has been studied by many works, the actual deployability of a model is rarely considered. In this work, we consider the actual deployability of a model from four aspects: the prediction performance of the model, the robustness of the model, the dependence of the model on external data and the storage size of the model. From these four aspects, we propose a multiple wavelet convolutional neural network (MWCNN) for load prediction. On two public datasets, we verified the performance performance and robustness of the MWCNN. The MWCNN only uses load data, and the storage size of the model is only 497 KB, which shows that MWCNN has good deployability. In addition, our MWCNN prediction results are interpretable. The experimental results show that the MWCNN can effectively capture the periodic characteristics of load data.

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Short-Term Load Forecasts Using LSTM Networks

Cited by 236 publications

References 6 publications

Prediction Model of Suspension Density in the Dense Medium Separation System Based on LSTM

Prediction Model of Suspension Density in the Dense Medium Separation System Based on LSTM

Analysis of the Effectiveness of Air Pollution Control Policies Based on Historical Evaluation and Deep Learning Forecast: A Case Study of Chengdu-Chongqing Region in China

Multiple Wavelet Convolutional Neural Network for Short-Term Load Forecasting

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