scholarly journals Research on Short-Term Load Prediction Based on Seq2seq Model

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3199 ◽  
Author(s):  
Gangjun Gong ◽  
Xiaonan An ◽  
Nawaraj Kumar Mahato ◽  
Shuyan Sun ◽  
Si Chen ◽  
...  
Keyword(s):  
Power System ◽  
Prediction Model ◽  
Short Term Memory ◽  
Predictive Performance ◽  
Weather Conditions ◽  
Attention Mechanism ◽  
Load Prediction ◽  
Short Term ◽  
System Load ◽  
The Impact

Electricity load prediction is the primary basis on which power-related departments to make logical and effective generation plans and scientific scheduling plans for the most effective power utilization. The perpetual evolution of deep learning has recommended advanced and innovative concepts for short-term load prediction. Taking into consideration the time and nonlinear characteristics of power system load data and further considering the impact of historical and future information on the current state, this paper proposes a Seq2seq short-term load prediction model based on a long short-term memory network (LSTM). Firstly, the periodic fluctuation characteristics of users’ load data are analyzed, establishing a correlation of the load data so as to determine the model’s order in the time series. Secondly, the specifications of the Seq2seq model are given preference and a coalescence of the Residual mechanism (Residual) and the two Attention mechanisms (Attention) is developed. Then, comparing the predictive performance of the model under different types of Attention mechanism, this paper finally adopts the Seq2seq short-term load prediction model of Residual LSTM and the Bahdanau Attention mechanism. Eventually, the prediction model obtains better results when merging the actual power system load data of a certain place. In order to validate the developed model, the Seq2seq was compared with recurrent neural network (RNN), LSTM, and gated recurrent unit (GRU) algorithms. Last but not least, the performance indices were calculated. when training and testing the model with power system load data, it was noted that the root mean square error (RMSE) of Seq2seq was decreased by 6.61%, 16.95%, and 7.80% compared with RNN, LSTM, and GRU, respectively. In addition, a supplementary case study was carried out using data for a small power system considering different weather conditions and user behaviors in order to confirm the applicability and stability of the proposed model. The Seq2seq model for short-term load prediction can be reported to demonstrate superiority in all areas, exhibiting better prediction and stable performance.

Air Conditioning Load Prediction of an Office Building based on Long Short Term Memory Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Mengxiang Zhuang ◽  
Qixin Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Air Conditioning ◽  
Short Term Memory ◽  
Load Prediction ◽  
Short Term ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Long Short Term Memory ◽  
Grey Relational

Background: Energy conservation has always been a major issue in our country, and the air conditioning energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load can be predicted and the air conditioning equipment can respond in advance, it can not only save energy, but also extend the life of the equipment. Introduction: The Neural network proposed in this paper can deeply analyze the load characteristics through three gate structures, which is helpful to improve the prediction accuracy. Combined with grey relational degree method, the prediction speed can be accelerated. Method: This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and selects the best ones. A Long Short Term Memory Neural Network (LSTMNN) prediction model was established. In this paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and the predicted results are compared with the real values. Results: Compared with Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine (SVM) prediction model, the simulation results show that this method has better effect on air conditioning load prediction. Conclusion: Grey relational degree analysis can extract the main factors from the numerous data, which is more convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air conditioning load on time series, but also considers the nonlinear relation between load and other factors. This model has higher prediction accuracy, shorter prediction time and great application potential.

scholarly journals Short-term load prediction of integrated energy system based on neural network

2021 ◽  
Vol 2087 (1) ◽  
pp. 012016
Author(s):  
Yao Wang ◽  
Xuxia Li ◽  
Yan Liang ◽  
Yingying Hu ◽  
Xiaoming Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Short Term Memory ◽  
Energy System ◽  
Load Prediction ◽  
Short Term ◽  
Nonlinear Characteristics ◽  
Integrated Energy System ◽  
Long Short Term Memory ◽  
Grey Relation

Abstract Considering the correlation and nonlinear characteristics of multiple types of loads in the integrated energy system, grey relation analysis (GRA) and long short term Memory (LSTM) neural network are selected to establish the short-term load prediction model of the integrated energy system. The model uses GRA method to analyze the coupling between multiple types of loads and the meteorological factors, and then obtains the load forecast results through the LSTM prediction model. Finally, a practical example is given to verify the validity of the model.

scholarly journals Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

2021 ◽  
Vol 11 (14) ◽  
pp. 6625
Author(s):  
Yan Su ◽  
Kailiang Weng ◽  
Chuan Lin ◽  
Zeqin Chen
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Impact Factors ◽  
Nonlinear Prediction ◽  
Time Dimension ◽  
Short Term ◽  
Deformation Prediction ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Dam Deformation

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.

scholarly journals Renewable Energy Deployment and COVID-19 Measures for Sustainable Development

2021 ◽  
Vol 13 (8) ◽  
pp. 4418
Author(s):  
Miraj Ahmed Bhuiyan ◽  
Jaehyung An ◽  
Alexey Mikhaylov ◽  
Nikita Moiseev ◽  
Mir Sayed Shah Danish
Keyword(s):  
Renewable Energy ◽  
Short Term Memory ◽  
External Environment ◽  
Energy Markets ◽  
Energy Market ◽  
Short Term ◽  
The United Kingdom ◽  
Long Short Term Memory ◽  
Restrictive Measures ◽  
The Impact

The main goal of this study is to evaluate the impact of restrictive measures introduced in connection with COVID-19 on consumption in renewable energy markets. The study will be based on the hypothesis that similar changes in human behavior can be expected in the future with the further spread of COVID-19 and/or the introduction of additional quarantine measures around the world. The analysis also yielded additional results. The strongest reductions in energy generation occurred in countries with a high percentage (more than 80%) of urban population (Brazil, USA, the United Kingdom and Germany). This study uses two models created with the Keras Long Short-Term Memory (Keras LSTM) Model, and 76 and 10 parameters are involved. This article suggests that various restrictive strategies reduced the sustainable demand for renewable energy and led to a drop in economic growth, slowing the growth of COVID-19 infections in 2020. It is unknown to what extent the observed slowdown in the spread from March 2020 to September 2020 due to the policy’s impact and not the interaction between the virus and the external environment. All renewable energy producers decreased the volume of renewable energy market supply in 2020 (except China).

Captions and reduced forms instruction: The impact on EFL students’ listening comprehension

ReCALL ◽  
2013 ◽  
Vol 26 (1) ◽  
pp. 44-61 ◽  
Author(s):  
Jie Chi Yang ◽  
Peichin Chang
Keyword(s):  
Language Learners ◽  
Listening Comprehension ◽  
Short Term Memory ◽  
Limited Capacity ◽  
Short Term ◽  
Term Memory ◽  
Efl Learners ◽  
Efl Students ◽  
The Impact ◽  
Reduced Forms

AbstractFor many EFL learners, listening poses a grave challenge. The difficulty in segmenting a stream of speech and limited capacity in short-term memory are common weaknesses for language learners. Specifically, reduced forms, which frequently appear in authentic informal conversations, compound the challenges in listening comprehension. Numerous interventions have been implemented to assist EFL language learners, and of these, the application of captions has been found highly effective in promoting learning. Few studies have examined how different modes of captions may enhance listening comprehension. This study proposes three modes of captions: full, keyword-only, and annotated keyword captions and investigates their contribution to the learning of reduced forms and overall listening comprehension. Forty-four EFL university students participated in the study and were randomly assigned to one of the three groups. The results revealed that all three groups exhibited improvement on the pre-test while the annotated keyword caption group exhibited the best performance with the highest mean score. Comparing performances between groups, the annotated keyword caption group also emulated both the full caption and the keyword-only caption groups, particularly in the ability to recognize reduced forms. The study sheds light on the potential of annotated keyword captions in enhancing reduced forms learning and overall listening comprehension.

Sign in / Sign up

Export Citation Format

Share Document