scholarly journals An Optimal Dispatch Framework of Electric and Heating Networks Based on Controllable Electric and Thermostatically Controlled Loads

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Yiming Ma ◽  
Jian Dong ◽  
Xiran Zhou ◽  
Guanfeng Zhang ◽  
Haixin Wang ◽  
...  
Keyword(s):  
Wind Power ◽  
Short Term Memory ◽  
Support Vector ◽  
Power Generators ◽  
Optimal Dispatch ◽  
Weather Factor ◽  
Thermostatically Controlled Loads ◽  
The Stability ◽  
Behavior Characteristics ◽  
Heating Networks

With the increasing capacity of wind power generators (WTGs), the volatility of wind power could significantly challenge the stability and economy of electric and heating networks. To tackle this challenge, this paper proposes an optimal dispatch framework based on controllable load (including controllable electric load and controllable thermostatically load) to reduce wind power curtailment. A forecasting model is developed for the controllable load, which comprehensively considers autocorrelation, weather factor, and consumers’ behavior characteristics. With adjusting controllable load, an optimal dispatch model of power system is then established and resolved by Sequential Least Squares Programming (SLSQP) method. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the root mean square error with the proposed long short-term memory can be reduced by 0.069 and 0.044, respectively. Compared with conventional method, the peak wind power curtailment with dispatching controllable load is reduced by nearly 10% and 5% in two cases, respectively.

scholarly journals Ultra-Short-Term Prediction of Wind Power Based on Error Following Forget Gate-Based Long Short-Term Memory

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5400
Author(s):  
Pei Zhang ◽  
Chunping Li ◽  
Chunhua Peng ◽  
Jiangang Tian
Keyword(s):  
Wind Power ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Support Vector ◽  
Power Prediction ◽  
Short Term ◽  
Term Memory ◽  
Wind Power Prediction ◽  
Long Short Term Memory ◽  
The Impact

To improve the accuracy of ultra-short-term wind power prediction, this paper proposed a model using modified long short-term memory (LSTM) to predict ultra-short-term wind power. Because the forget gate of standard LSTM cannot reflect the correction effect of prediction errors on model prediction in ultra-short-term, this paper develops the error following forget gate (EFFG)-based LSTM model for ultra-short-term wind power prediction. The proposed EFFG-based LSTM model updates the output of the forget gate using the difference between the predicted value and the actual value, thereby reducing the impact of the prediction error at the previous moment on the prediction accuracy of wind power at this time, and improving the rolling prediction accuracy of wind power. A case study is performed using historical wind power data and numerical prediction meteorological data of an actual wind farm. Study results indicate that the root mean square error of the wind power prediction model based on EFFG-based LSTM is less than 3%, while the accuracy rate and qualified rate are more than 90%. The EFFG-based LSTM model provides better performance than the support vector machine (SVM) and standard LSTM model.

scholarly journals Support Vector Machine and Long Short-term Memory using Multivariate Models for Wind Power Forecasting

2020 ◽  
Vol 9 (4) ◽  
pp. 2364-2369
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Short Term Memory ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Renewable energy has recently gained considerable attention. In particular, interest in wind energy is rapidly increasing globally. However, the characteristics of instability and volatility in wind energy systems also have a significant on power systems. To address these issues, numerous studies have been carried out to predict wind speed and power. Methods used to forecast wind energy are divided into three categories: physical, data-driven (statistical and artificial intelligence methods), and hybrid methods. In this study, among artificial intelligence methods, we compare short-term wind power using a support vector machine (SVM) and long short-term memory (LSTM). The method using an SVM is a short-term wind power forecast that considers the wind speed and direction on Jeju Island, whereas the method using LSTM does not consider the wind speed and direction. As the experiment results indicate, the SVM method achieves an excellent performance when considering the wind speed and direction.

scholarly journals Improvements to the operation of wind power generators in Vietnam

2017 ◽  
Vol 8 (3) ◽  
pp. 173-178
Author(s):  
Nhu Y Do ◽  
Xuan Thanh Le
Keyword(s):  
Wind Power ◽  
Viet Nam ◽  
Power Station ◽  
Power Generators ◽  
Earth Fault ◽  
Wind Power Station ◽  
The Stability ◽  
National Networks ◽  
Wind Power Generators ◽  
Generation Technology

In Vietnam, the number of wind power generators has been increased yearly. Because of geophysical characters, the generation of these generators has different properties. It depends much on wind’s characteristics as well as the generation technology. Based on simulation implemented on Matlab, the paper analyzes the stability of national networks at PCC nods containing the connection of wind power station when there is a change of wind’s velocity or when there is an earth fault in grid. The analyzing results are used to suggest solutions to improve the stability and effectiveness of the whole system. Các nhà máy điện gió đang ngày càng gia tăng ở Việt Nam, do cấu tạo địa lý nên đặc điểm của nguồn điện này khá khác biệt so với các nguồn điện truyền thống khác. Nguồn điện gió phụ thuộc nhiều vào đặc điểm của gió và công nghệ sử dụng để phát điện gió. Nội dung chính của bài báo là khảo sát mức độ ổn định của lưới điện kết nối các nguồn điện gió tại nút kết nối chung khi có sự thay đổi về tốc độ gió hoặc khi trong mạng xảy ra sự cố chạm đất thoáng qua, từ đó đề xuất giải pháp nâng cao ổn định, tin cậy và nâng cao hiệu quả làm việc của hệ thống.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

scholarly journals Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 445-451
Author(s):  
Yifei Sun ◽  
Navid Rashedi ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real World ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Jun Meng ◽  
Qiang Kang ◽  
Zheng Chang ◽  
Yushi Luan
Keyword(s):  
Deep Learning ◽  
Noncoding Rna ◽  
Nearest Neighbor ◽  
Short Term Memory ◽  
Biological Activities ◽  
Support Vector ◽  
Multiple Perspectives ◽  
K Nearest Neighbor ◽  
Rna Sequences ◽  
Deep Learning Model

Abstract Background Long noncoding RNAs (lncRNAs) play an important role in regulating biological activities and their prediction is significant for exploring biological processes. Long short-term memory (LSTM) and convolutional neural network (CNN) can automatically extract and learn the abstract information from the encoded RNA sequences to avoid complex feature engineering. An ensemble model learns the information from multiple perspectives and shows better performance than a single model. It is feasible and interesting that the RNA sequence is considered as sentence and image to train LSTM and CNN respectively, and then the trained models are hybridized to predict lncRNAs. Up to present, there are various predictors for lncRNAs, but few of them are proposed for plant. A reliable and powerful predictor for plant lncRNAs is necessary. Results To boost the performance of predicting lncRNAs, this paper proposes a hybrid deep learning model based on two encoding styles (PlncRNA-HDeep), which does not require prior knowledge and only uses RNA sequences to train the models for predicting plant lncRNAs. It not only learns the diversified information from RNA sequences encoded by p-nucleotide and one-hot encodings, but also takes advantages of lncRNA-LSTM proposed in our previous study and CNN. The parameters are adjusted and three hybrid strategies are tested to maximize its performance. Experiment results show that PlncRNA-HDeep is more effective than lncRNA-LSTM and CNN and obtains 97.9% sensitivity, 95.1% precision, 96.5% accuracy and 96.5% F1 score on Zea mays dataset which are better than those of several shallow machine learning methods (support vector machine, random forest, k-nearest neighbor, decision tree, naive Bayes and logistic regression) and some existing tools (CNCI, PLEK, CPC2, LncADeep and lncRNAnet). Conclusions PlncRNA-HDeep is feasible and obtains the credible predictive results. It may also provide valuable references for other related research.

scholarly journals An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images

2020 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
Jingtao Li ◽  
Yonglin Shen ◽  
Chao Yang
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Short Term Memory ◽  
Complete Classification ◽  
Support Vector ◽  
Classification Models ◽  
Training Samples ◽  
Agricultural Applications ◽  
Crop Classification ◽  
Increasing Demand

Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN’s discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.

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