scholarly journals Day-Ahead Solar Irradiance Forecasting Using Hybrid Recurrent Neural Network with Weather Classification for Power System Scheduling

2021 ◽  
Vol 11 (15) ◽  
pp. 6738
Author(s):  
Rehman Zafar ◽  
Ba Hau Vu ◽  
Munir Husein ◽  
Il-Yop Chung
Keyword(s):  
Neural Network ◽  
Renewable Energy ◽  
Power System ◽  
Recurrent Neural Network ◽  
Solar Irradiance ◽  
Short Term Memory ◽  
Dew Point ◽  
Support Vector ◽  
Renewable Energy Resources ◽  
Feed Forward Neural Network

At the present time, power-system planning and management is facing the major challenge of integrating renewable energy resources (RESs) due to their intermittent nature. To address this problem, a highly accurate renewable energy generation forecasting system is needed for day-ahead power generation scheduling. Day-ahead solar irradiance (SI) forecasting has various applications for system operators and market agents such as unit commitment, reserve management, and biding in the day-ahead market. To this end, a hybrid recurrent neural network is presented herein that uses the long short-term memory recurrent neural network (LSTM-RNN) approach to forecast day-ahead SI. In this approach, k-means clustering is first used to classify each day as either sunny or cloudy. Then, LSTM-RNN is used to learn the uncertainty and variability for each type of cluster separately to predict the SI with better accuracy. The exogenous features such as the dry-bulb temperature, dew point temperature, and relative humidity are used to train the models. Results show that the proposed hybrid model has performed better than a feed-forward neural network (FFNN), a support vector machine (SVM), a conventional LSTM-RNN, and a persistence model.

scholarly journals Ekstraksi Relasi Antar Entitas di Bahasa Indonesia Menggunakan Neural Network

2021 ◽  
Vol 3 (02) ◽  
pp. 49-54
Author(s):  
Ananta Tio Putra ◽  
Eunike Kardinata ◽  
Hartarto Junaedi ◽  
Francisca Chandra ◽  
Joan Santoso
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Relation Extraction ◽  
Support Vector ◽  
Short Term ◽  
Tree Kernel ◽  
Kernel Support Vector Machine ◽  
Long Short Term Memory ◽  
Bahasa Indonesia

Dengan perkembangan zaman yang begitu pesat, berdampak pada perkembangan data pula. Salah satu bentuk data yang paling banyak saat ini berupa data tekstual seperti artikel sederhana maupun dokumen lain yang terdapat di internet. Agar data tekstual tersebut dapat dimengerti dan dimanfaatkan dengan baik oleh manusia, maka perlu di proses dan disederhanakan agar menjadi informasi yang ringkas dan jelas. Oleh karena itu, semakin berkembang pula penelitian dalam bidang Information Extraction (IE) dan salah satu contoh penelitian di IE adalah Relation Extraction (RE). Penelitian RE sudah banyak dilakukan terutama pada Bahasa Inggris dimana resourcenya sudah termasuk banyak. Metode yang digunakan pun bermacam-macam seperti kernel, tree kernel, support vector machine, long short-term memory, convulution recurrent neural network, dan lain sebagainya. Pada penelitian kali ini adalah penelitian RE pada Bahasa Indonesia dengan menggunakan metode convulution recurrent neural network yang sudah dipergunakan untuk RE Bahasa Inggris. Dataset yang digunakan pada penelitian ini adalah dataset Bahasa Indonesia yang berasal dari file xml wikipedia. File xml wikipedia ini kemudian diproses sehingga menghasilkan dataset seperti yang digunakan pada CRNN dalam Bahasa inggris yaitu dalam format SemEval-2 Task 8. Uji coba dilakukan dengan berbagai macam perbandingan data training dan testing yaitu 80:20, 70:30, dan 60:40. Selain itu, parameter pooling untuk CRNN yang digunakan ada dua macam yaitu ‘att’ dan ‘max’. Dari uji coba yang dilakukan, hasil yang didapatkan adalah bervariasi mulai dari mendekati maupun lebih baik bila dibandingkan dengan CRNN dengan menggunakan dataset Bahasa inggris sehingga dapat disimpulkan bahwa dengan CRNN ini bisa digunakan untuk proses RE pada Bahasa Indonesia apabila dataset yang digunakan sesuai dengan penelitian sebelumnya.

Attention mechanism for developing wind speed and solar irradiance forecasting models

2020 ◽  
pp. 0309524X2098188
Author(s):  
Banalaxmi Brahma ◽  
Rajesh Wadhvani ◽  
Sanyam Shukla
Keyword(s):  
Neural Network ◽  
Renewable Energy ◽  
Wind Speed ◽  
Recurrent Neural Network ◽  
Solar Irradiance ◽  
Power Plants ◽  
Energy Resource ◽  
Attention Mechanism ◽  
Wind Speed Prediction ◽  
Forecasting Models

This article presents the Recurrent Neural Network (RNN) and its Attention mechanism to develop forecasting models for renewable energy applications. In this study, wind speed and solar irradiance forecasting models have been developed as these two factors play a significant role in renewable energy production. The irregular nature of wind poses the challenge of accurate wind speed prediction, while solar irradiance forecasting can aid in the planning and deployment of solar power plants. In this paper, six RNN techniques, namely RNN, GRU, LSTM, Content-based Attention, Luong Attention, and Self-Attention based RNN are considered for forecasting the future values of wind speed and solar irradiance in particular geographical locations. The aim is the identification of the advantages, comparison, and importance of different recurrent neural network methods for forecasting models. All models are developed on the datasets of the National Renewable Energy Laboratory (NREL) and NASA’s Prediction of Worldwide Energy Resource (POWER).

scholarly journals Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 235
Author(s):  
Shuo-Yan Chou ◽  
Anindhita Dewabharata ◽  
Ferani Eva Zulvia
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Support Vector ◽  
Time Data ◽  
Short Term ◽  
Term Memory ◽  
Parking Lots ◽  
Parking System ◽  
Long Short Term Memory

The size of cities has been continuously increasing because of urbanization. The number of public and private transportation vehicles is rapidly increasing, thus resulting in traffic congestion, traffic accidents, and environmental pollution. Although major cities have undergone considerable development in terms of transportation infrastructure, problems caused by a high number of moving vehicles cannot be completely resolved through the expansion of streets and facilities. This paper proposes a solution for the parking problem in cities that entails a shared parking system. The primary concept of the proposed shared parking system is to release parking lots that are open to specific groups for public usage without overriding personal usage. Open-to-specific-groups parking lots consist of parking spaces provided for particular people, such as parking buildings at universities for teachers, staff, and students. The proposed shared parking system comprises four primary steps: collecting and preprocessing data by using an Internet of Things system, predicting internal demand by using a recurrent neural network algorithm, releasing several unoccupied parking lots based on prediction results, and continuously updating the real-time data to improve future internal usage prediction. Data collection and data forecasting are performed to ensure that the system does not override personal usage. This study applied several forecasting algorithms, including seasonal ARIMA, support vector regression, multilayer perceptron, convolutional neural network, long short-term memory recurrent neural network with a many-to-one structure, and long short-term memory recurrent neural network with a many-to-many structure. The proposed system was evaluated using artificial and real datasets. Results show that the recurrent neural network with the many-to-many structure generates the most accurate prediction. Furthermore, the proposed shared parking system was evaluated for some scenarios in which different numbers of parking spaces were released. Simulation results show that the proposed shared parking system can provide parking spaces for public usage without overriding personal usage. Moreover, this system can generate new income for parking management and/or parking lot owners.

scholarly journals Power System Load Forecasting Method Based on Recurrent Neural Network

2020 ◽  
Vol 182 ◽  
pp. 02007
Author(s):  
Chuanjun Pang ◽  
Tie Bao ◽  
Lei He
Keyword(s):  
Neural Network ◽  
Power System ◽  
Recurrent Neural Network ◽  
Electricity Market ◽  
Short Term Memory ◽  
Sequence Data ◽  
Load Forecasting ◽  
System Load ◽  
Forecasting Method ◽  
Deep Recurrent Neural Network

Power system load forecasting plays an important role in the power dispatching operation. The development of the electricity market and the increasing integration of distributed generators have increased the complexity of power consumption model and put forward higher requirements for the accuracy and stability of load forecasting. A load forecasting method based on long-short term memory (LSTM) is proposed. This method uses deep recurrent neural network from the artificial intelligence field to establish a load forecasting model. Using the LSTM network to memorize the long-term dependence of the sequence data, the intrinsic variation of the load itself is identified from both the horizontal and vertical dimensions within a longer historical time period, while considering various influencing factors. Actual load data is used to verify the forecasting performance of different historical date windows and different network architectures.

Using a recurrent neural network with S2 characteristics, efficient identification of localised cortical dysplasia

2021 ◽  
pp. 1-14
Author(s):  
A. Karthika ◽  
R. Subramanian ◽  
S. Karthik
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Focal Cortical Dysplasia ◽  
Cortical Dysplasia ◽  
Support Vector ◽  
Sensitivity Rate ◽  
Long Short Term Memory ◽  
Artificial Neural Network Ann ◽  
The Brain

Focal cortical dysplasia (FCD) is an inborn anomaly in brain growth and morphological deformation in lesions of the brain which induces focal seizures. Neurosurgical therapies were performed for the detection of FCD. Furthermore, it can be overcome through the presurgical evaluation of epilepsy. The surgical result is attained basically through the output of the presurgical output. In preprocessing the process of increasing true positives with the decrease in false negatives occurs which results in an effective outcome. MRI (Magnetic Resonance Imaging) outputs are efficient to predict the FCD lesions through T1- MPRAGE and T2- FLAIR efficient output can be obtained. In our proposed work we extract the S2 features through the testing of T1, T2 images. Using RNN-LSTM (Recurrent neural network-Long short-term memory) test images were trained and the FCD lesions were segmented. The output of our work is compared with the proposed work yields better results compared to the existing system such as artificial neural network (ANN), support vector machine (SVM), and convolution neural network (CNN). This approach obtained an accuracy rate of 0.195% (ANN), 0.20% (SVM), 0.14% (CNN), specificity rate of 0.23% (ANN), 0.15% (SVM), 0.13% (CNN) and sensitivity rate of 0.22% (ANN), 0.14% (SVM), 0.08% (CNN) respectively in comparison with RNN-LSTM.

scholarly journals Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1856 ◽  
Author(s):  
Munir Husein ◽  
Il-Yop Chung
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Power Output ◽  
Solar Irradiance ◽  
Short Term Memory ◽  
Forecasting Model ◽  
Short Term ◽  
Term Memory ◽  
Irradiance Data ◽  
Long Short Term Memory

In microgrids, forecasting solar power output is crucial for optimizing operation and reducing the impact of uncertainty. To forecast solar power output, it is essential to forecast solar irradiance, which typically requires historical solar irradiance data. These data are often unavailable for residential and commercial microgrids that incorporate solar photovoltaic. In this study, we propose an hourly day-ahead solar irradiance forecasting model that does not depend on the historical solar irradiance data; it uses only widely available weather data, namely, dry-bulb temperature, dew-point temperature, and relative humidity. The model was developed using a deep, long short-term memory recurrent neural network (LSTM-RNN). We compare this approach with a feedforward neural network (FFNN), which is a method with a proven record of accomplishment in solar irradiance forecasting. To provide a comprehensive evaluation of this approach, we performed six experiments using measurement data from weather stations in Germany, U.S.A, Switzerland, and South Korea, which all have distinct climate types. Experiment results show that the proposed approach is more accurate than FFNN, and achieves the accuracy of up to 60.31 W/m2 in terms of root-mean-square error (RMSE). Moreover, compared with the persistence model, the proposed model achieves average forecast skill of 50.90% and up to 68.89% in some datasets. In addition, to demonstrate the effect of using a particular forecasting model on the microgrid operation optimization, we simulate a one-year operation of a commercial building microgrid. Results show that the proposed approach is more accurate, and leads to a 2% rise in annual energy savings compared with FFNN.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

scholarly journals Recurrent neural network-based acute concussion classifier using raw resting state EEG data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Karun Thanjavur ◽  
Arif Babul ◽  
Brandon Foran ◽  
Maya Bielecki ◽  
Adam Gilchrist ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Resting State ◽  
Short Term Memory ◽  
Characteristic Curve ◽  
Health Concern ◽  
Adolescent Athletes ◽  
Individual Level ◽  
Diffuse Brain Injury ◽  
Eeg Data

AbstractConcussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, the most significant challenge in concussion management is the lack of objective, clinically- accepted, brain-based approaches for determining whether an athlete has suffered a concussion. Here, we report on our efforts to address this challenge. Specifically, we introduce a deep learning long short-term memory (LSTM)-based recurrent neural network that is able to distinguish between non-concussed and acute post-concussed adolescent athletes using only short (i.e. 90 s long) samples of resting state EEG data as input. The athletes were neither required to perform a specific task nor expected to respond to a stimulus during data collection. The acquired EEG data were neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and validated using data from 27 male, adolescent athletes with sports related concussion, benchmarked against 35 non-concussed adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of > 90% and achieved an ensemble median Area Under the Receiver Operating Characteristic Curve (ROC/AUC) equal to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state, raw EEG data. Our concussion classifier represents a promising first step towards the development of an easy-to-use, objective, brain-based, automatic classification of concussion at an individual level.

Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network

2021 ◽  
pp. 1-11
Author(s):  
Sang-Ki Jeong ◽  
Dea-Hyeong Ji ◽  
Ji-Youn Oh ◽  
Jung-Min Seo ◽  
Hyeung-Sik Choi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Flow Velocity ◽  
Recurrent Neural Network ◽  
Pid Controller ◽  
Short Term Memory ◽  
Ocean Currents ◽  
Ocean Current ◽  
Movement Speed ◽  
Marine Research

In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9].

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