scholarly journals Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3089 ◽  
Author(s):  
Ke Yan ◽  
Xudong Wang ◽  
Yang Du ◽  
Ning Jin ◽  
Haichao Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Power Consumption ◽  
Real World ◽  
Electricity Market ◽  
Moving Average ◽  
Short Term ◽  
Auto Regressive ◽  
Short Term Forecasting ◽  
Deep Learning Neural Network

Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage.

scholarly journals The use of machine translation algorithm based on residual and LSTM neural network in translation teaching

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0240663
Author(s):  
Beibei Ren
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Machine Translation ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Translation Model ◽  
Translation Quality ◽  
Long Short Term Memory ◽  
Deep Learning Neural Network

With the rapid development of big data and deep learning, breakthroughs have been made in phonetic and textual research, the two fundamental attributes of language. Language is an essential medium of information exchange in teaching activity. The aim is to promote the transformation of the training mode and content of translation major and the application of the translation service industry in various fields. Based on previous research, the SCN-LSTM (Skip Convolutional Network and Long Short Term Memory) translation model of deep learning neural network is constructed by learning and training the real dataset and the public PTB (Penn Treebank Dataset). The feasibility of the model’s performance, translation quality, and adaptability in practical teaching is analyzed to provide a theoretical basis for the research and application of the SCN-LSTM translation model in English teaching. The results show that the capability of the neural network for translation teaching is nearly one times higher than that of the traditional N-tuple translation model, and the fusion model performs much better than the single model, translation quality, and teaching effect. To be specific, the accuracy of the SCN-LSTM translation model based on deep learning neural network is 95.21%, the degree of translation confusion is reduced by 39.21% compared with that of the LSTM (Long Short Term Memory) model, and the adaptability is 0.4 times that of the N-tuple model. With the highest level of satisfaction in practical teaching evaluation, the SCN-LSTM translation model has achieved a favorable effect on the translation teaching of the English major. In summary, the performance and quality of the translation model are improved significantly by learning the language characteristics in translations by teachers and students, providing ideas for applying machine translation in professional translation teaching.

HUMAN ROBOT INTERACTIVE INTENTION PREDICTION USING DEEP LEARNING TECHNIQUES

2021 ◽  
pp. 1-12
Author(s):  
Thang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
The Neural Network ◽  
Learning Techniques ◽  
Long Short Term Memory ◽  
Deep Learning Neural Network

In this research, we propose a method of human robot interactive intention prediction. The proposed algorithm makes use of a OpenPose library and a Long-short term memory deep learning neural network. The neural network observes the human posture in a time series, then predicts the human interactive intention. We train the deep neural network using dataset generated by us. The experimental results show that, our proposed method is able to predict the human robot interactive intention, providing 92% the accuracy on the testing set.

scholarly journals Passenger Flow Prediction of Urban Rail Transit Based on Deep Learning Methods

Smart Cities ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 371-387 ◽  
Author(s):  
Zhi Xiong ◽  
Jianchun Zheng ◽  
Dunjiang Song ◽  
Shaobo Zhong ◽  
Quanyi Huang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Moving Average ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Short Term ◽  
Learning Methods ◽  
Autoregressive Integrated Moving Average ◽  
Passenger Flow ◽  
Urban Rail

The rapid development of urban rail transit brings high efficiency and convenience. At the same time, the increasing passenger flow also remarkably increases the risk of emergencies such as passenger stampedes. The accurate and real-time prediction of dynamic passenger flow is of great significance to the daily operation safety management, emergency prevention, and dispatch of urban rail transit systems. Two deep learning neural networks, a long short-term memory neural network (LSTM NN) and a convolutional neural network (CNN), were used to predict an urban rail transit passenger flow time series and spatiotemporal series, respectively. The experiments were carried out through the passenger flow of Beijing metro stations and lines, and the prediction results of the deep learning methods were compared with several traditional linear models including autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), and space–time autoregressive integrated moving average (STARIMA). It was shown that the LSTM NN and CNN could better capture the time or spatiotemporal features of the urban rail transit passenger flow and obtain accurate results for the long-term and short-term prediction of passenger flow. The deep learning methods also have strong data adaptability and robustness, and they are more ideal for predicting the passenger flow of stations during peaks and the passenger flow of lines during holidays.

Deep learning neural network for the prediction of asian tiger stock markets

2021 ◽  
pp. 2150040
Author(s):  
Kok-Leong Yap ◽  
Wee-Yeap Lau ◽  
Izlin Ismail
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Stock Prices ◽  
Stock Markets ◽  
Short Term Memory ◽  
Stock Market Index ◽  
Short Term ◽  
Asian Tiger ◽  
Deep Learning Neural Network

Motivated by the recent interest of stock traders and investors towards the deep learning neural network, this study employs the deep learning neural networks, namely, multilayer perceptron, long short-term memory, and convolutional neural network, to forecast the Asian Tiger stock markets. One of the challenges to using deep learning neural networks is to select the input variable. We propose to use multiple linear regression to select the input variable that is significant to the output. Besides, we construct a regional stock market index as a significant input to forecast the Asian Tiger stock markets. A comparison study on the forecasting model shows that the deep learning model can be used as a decision-making system that assists investors to predict short-term movement and trends of stock prices.

scholarly journals A Hybrid Spatiotemporal Deep Learning Model for Short-Term Metro Passenger Flow Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Jie He ◽  
Jie Bao ◽  
Qiong Hong ◽  
Xiaomeng Shi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Model ◽  
Primary Objective ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Deep Learning Neural Network ◽  
Deep Learning Model ◽  
Metro System

The primary objective of this study is to predict the short-term metro passenger flow using the proposed hybrid spatiotemporal deep learning neural network (HSTDL-net). The metro passenger flow data is collected from line 2 of Nanjing metro system to illustrate the study procedure. A hybrid spatiotemporal deep learning model is developed to predict both inbound and outbound passenger flows for every 10 minutes. The results suggest that the proposed HSTDL-net achieves better prediction performance on suburban stations than on urban stations, as well as generating the best prediction accuracy on transfer stations in terms of the lowest MAPE value. Moreover, a comparative analysis is conducted to compare the performance of proposed HSTDL-net with other typical methods, such as ARIMA, MLP, CNN, LSTM, and GBRT. The results indicate that, for both inbound and outbound passenger flow predictions, the HSTDL-net outperforms all the compared models on three types of stations. The results suggest that the proposed hybrid spatiotemporal deep learning neural network can more effectively and fully discover both spatial and temporal hidden correlations between stations for short-term metro passenger flow prediction. The results of this study could provide insightful suggestions for metro system authorities to adjust the operation plans and enhance the service quality of the entire metro system.

Efficiency of Using Artificial Neural Network for Short-Term Load Forecasting

2015 ◽  
Vol 792 ◽  
pp. 312-316 ◽  
Author(s):  
Svetlana Rodygina ◽  
Valentina Lyubchenko ◽  
Alexander Rodygin
Keyword(s):  
Neural Network ◽  
Electricity Market ◽  
Load Forecasting ◽  
Operating Conditions ◽  
Short Term ◽  
The Neural Network ◽  
Short Term Load Forecasting ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Short Term Forecasting

Using artificial neural networks (ANN) for short-term load forecasting is an efficient method to get the best result. Considered problem of short-term load forecasting shows that the accuracy of short-term forecasting models and methods significantly influences on the further planning of operating conditions at the modern electricity market. The obtained error for short-term load forecasting using the neural network algorithm is 2.78%.

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