Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models

In the context of smart grids, the need for forecasts of the power output of small-scale photovoltaic (PV) arrays increases as control processes such as the management of flexibilities in the distribution grid gain importance. However, there is often only very little knowledge about the PV systems installed: even fundamental system parameters such as panel orientation, the number of panels and their type, or time series data of past PV system performance are usually unknown to the grid operator. In the past, only forecasting models that attempted to account for cause-and-effect chains existed; nowadays, also data-driven methods that attempt to recognize patterns in past behavior are available. Choosing between physics-based or data-driven forecast methods requires knowledge about the typical forecast quality as well as the requirements that each approach entails. In this contribution, the achieved forecast quality for a typical scenario (day-ahead, based on numerical weather predictions [NWP]) is evaluated for one physics-based as well as five different data-driven forecast methods for a year at the same site in south-western Germany. Namely, feed-forward neural networks (FFNN), long short-term memory (LSTM) networks, random forest, bagging and boosting are investigated. Additionally, the forecast quality of the weather forecast is analyzed for key quantities. All evaluated PV forecast methods showed comparable performance; based on concise descriptions of the forecast approaches, advantages and disadvantages of each are discussed. The approaches are viable even though the forecasts regularly differ significantly from the observed behavior; the residual analysis performed offers a qualitative insight into the achievable forecast quality in a typical real-world scenario.

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Improvement of Variant Adaptable LSTM Trained With Metaheuristic Algorithms for Healthcare Analysis

Research Anthology on Artificial Intelligence Applications in Security ◽

10.4018/978-1-7998-7705-9.ch048 ◽

2021 ◽

pp. 1031-1051

Author(s):

Tarik A. Rashid ◽

Mohammad K. Hassan ◽

Mokhtar Mohammadi ◽

Kym Fraser

Keyword(s):

Diabetes Mellitus ◽

Neural Networks ◽

Time Series Data ◽

Short Term Memory ◽

Learning Algorithm ◽

Back Propagation ◽

Machine Learning Techniques ◽

Series Data ◽

Data Sets ◽

Patients With Diabetes

Recently, the population of the world has increased along with health problems. Diabetes mellitus disease as an example causes issues to the health of many patients globally. The task of this chapter is to develop a dynamic and intelligent decision support system for patients with different diseases, and it aims at examining machine-learning techniques supported by optimization techniques. Artificial neural networks have been used in healthcare for several decades. Most research works utilize multilayer layer perceptron (MLP) trained with back propagation (BP) learning algorithm to achieve diabetes mellitus classification. Nonetheless, MLP has some drawbacks, such as, convergence, which can be slow; local minima can affect the training process. It is hard to scale and cannot be used with time series data sets. To overcome these drawbacks, long short-term memory (LSTM) is suggested, which is a more advanced form of recurrent neural networks. In this chapter, adaptable LSTM trained with two optimizing algorithms instead of the back propagation learning algorithm is presented. The optimization algorithms are biogeography-based optimization (BBO) and genetic algorithm (GA). Dataset is collected locally and another benchmark dataset is used as well. Finally, the datasets fed into adaptable models; LSTM with BBO (LSTMBBO) and LSTM with GA (LSTMGA) for classification purposes. The experimental and testing results are compared and they are promising. This system helps physicians and doctors to provide proper health treatment for patients with diabetes mellitus. Details of source code and implementation of our system can be obtained in the following link “https://github.com/hamakamal/LSTM.”

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A Data-Driven Long Time-Series Electrical Line Trip Fault Prediction Method Using an Improved Stacked-Informer Network

Sensors ◽

10.3390/s21134466 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4466

Author(s):

Li Guo ◽

Runze Li ◽

Bin Jiang

Keyword(s):

Time Series ◽

Prediction Accuracy ◽

Time Series Data ◽

Short Term Memory ◽

Fault Prediction ◽

Data Driven ◽

Series Data ◽

Sequence Prediction ◽

Long Time Series ◽

Long Time

The monitoring of electrical equipment and power grid systems is very essential and important for power transmission and distribution. It has great significances for predicting faults based on monitoring a long sequence in advance, so as to ensure the safe operation of the power system. Many studies such as recurrent neural network (RNN) and long short-term memory (LSTM) network have shown an outstanding ability in increasing the prediction accuracy. However, there still exist some limitations preventing those methods from predicting long time-series sequences in real-world applications. To address these issues, a data-driven method using an improved stacked-Informer network is proposed, and it is used for electrical line trip faults sequence prediction in this paper. This method constructs a stacked-Informer network to extract underlying features of long sequence time-series data well, and combines the gradient centralized (GC) technology with the optimizer to replace the previously used Adam optimizer in the original Informer network. It has a superior generalization ability and faster training efficiency. Data sequences used for the experimental validation are collected from the wind and solar hybrid substation located in Zhangjiakou city, China. The experimental results and concrete analysis prove that the presented method can improve fault sequence prediction accuracy and achieve fast training in real scenarios.

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Deep Learning-Based GNSS Network-Based Real-Time Kinematic Improvement for Autonomous Ground Vehicle Navigation

Journal of Sensors ◽

10.1155/2019/3737265 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 6

Author(s):

Hee-Un Kim ◽

Tae-Suk Bae

Keyword(s):

Deep Learning ◽

Real Time ◽

Time Series Data ◽

Short Term Memory ◽

Advanced Technology ◽

Series Data ◽

Measurement Unit ◽

Vehicle Navigation ◽

Real Time Kinematic ◽

Autonomous Vehicle Navigation

Much navigation over the last several decades has been aided by the global navigation satellite system (GNSS). In addition, with the advent of the multi-GNSS era, more and more satellites are available for navigation purposes. However, the navigation is generally carried out by point positioning based on the pseudoranges. The real-time kinematic (RTK) and the advanced technology, namely, the network RTK (NRTK), were introduced for better positioning and navigation. Further improved navigation was also investigated by combining other sensors such as the inertial measurement unit (IMU). On the other hand, a deep learning technique has been recently evolving in many fields, including automatic navigation of the vehicles. This is because deep learning combines various sensors without complicated analytical modeling of each individual sensor. In this study, we structured the multilayer recurrent neural networks (RNN) to improve the accuracy and the stability of the GNSS absolute solutions for the autonomous vehicle navigation. Specifically, the long short-term memory (LSTM) is an especially useful algorithm for time series data such as navigation with moderate speed of platforms. From an experiment conducted in a testing area, the LSTM algorithm developed the positioning accuracy by about 40% compared to GNSS-only navigation without any external bias information. Once the bias is taken care of, the accuracy will significantly be improved up to 8 times better than the GNSS absolute positioning results. The bias terms of the solution need to be estimated within the model by optimizing the layers as well as the nodes each layer, which should be done in further research.

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Different Techniques used in Stock Market Prediction

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e9275.069520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 60-62

Keyword(s):

Machine Learning ◽

Time Series ◽

Stock Market ◽

Time Series Data ◽

Short Term Memory ◽

Moving Average ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Series Data ◽

Autoregressive Integrated Moving Average

The stock market has been one of the primary revenue streams for many for years. The stock market is often incalculable and uncertain; therefore predicting the ups and downs of the stock market is an uphill task even for the financial experts, which they been trying to tackle without any little success. But it is now possible to predict stock markets due to rapid improvement in technology which led to better processing speed and more accurate algorithms. It is necessary to forswear the misconception that prediction of stock market is only meant for people who have expertise in finance; hence an application can be developed to guide the user about the tempo of the stock market and risk associated with it.The prediction of prices in stock market is a complicated task, and there are various techniques that are used to solve the problem, this paper investigates some of these techniques and compares the accuracy of each of the methods. Forecasting the time series data is important topic in many economics, statistics, finance and business. Of the many techniques in forecasting time series data such as the Autoregressive, Moving Average, and the Autoregressive Integrated Moving Average, it is the Autoregressive Integrated Moving Average that has higher accuracy and higher precision than other methods. And with recent advancement in computational power of processors and advancement in knowledge of machine learning techniques and deep learning, new algorithms could be made to tackle the problem of predicting the stock market. This paper investigates one of such machine learning algorithms to forecast time series data such as Long Short Term Memory. It is compared with traditional algorithms such as the ARIMA method, to determine how superior the LSTM is compared to the traditional methods for predicting the stock market.

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Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17020453 ◽

2020 ◽

Vol 17 (2) ◽

pp. 453 ◽

Cited By ~ 1

Author(s):

Jiucheng Xu ◽

Keqiang Xu ◽

Zhichao Li ◽

Fengxia Meng ◽

Taotian Tu ◽

...

Keyword(s):

Time Series Data ◽

Short Term Memory ◽

Mean Squared Error ◽

Control Measures ◽

Superior Performance ◽

Effective Control ◽

Series Data ◽

Learning Method ◽

Outbreak Period

Dengue fever (DF) is one of the most rapidly spreading diseases in the world, and accurate forecasts of dengue in a timely manner might help local government implement effective control measures. To obtain the accurate forecasting of DF cases, it is crucial to model the long-term dependency in time series data, which is difficult for a typical machine learning method. This study aimed to develop a timely accurate forecasting model of dengue based on long short-term memory (LSTM) recurrent neural networks while only considering monthly dengue cases and climate factors. The performance of LSTM models was compared with the other previously published models when predicting DF cases one month into the future. Our results showed that the LSTM model reduced the average the root mean squared error (RMSE) of the predictions by 12.99% to 24.91% and reduced the average RMSE of the predictions in the outbreak period by 15.09% to 26.82% as compared with other candidate models. The LSTM model achieved superior performance in predicting dengue cases as compared with other previously published forecasting models. Moreover, transfer learning (TL) can improve the generalization ability of the model in areas with fewer dengue incidences. The findings provide a more precise forecasting dengue model and could be used for other dengue-like infectious diseases.

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Improvement of Variant Adaptable LSTM Trained With Metaheuristic Algorithms for Healthcare Analysis

Advances in Medical Technologies and Clinical Practice - Advanced Classification Techniques for Healthcare Analysis ◽

10.4018/978-1-5225-7796-6.ch006 ◽

2019 ◽

pp. 111-131 ◽

Cited By ~ 1

Author(s):

Tarik A. Rashid ◽

Mohammad K. Hassan ◽

Mokhtar Mohammadi ◽

Kym Fraser

Keyword(s):

Diabetes Mellitus ◽

Neural Networks ◽

Time Series Data ◽

Short Term Memory ◽

Learning Algorithm ◽

Back Propagation ◽

Machine Learning Techniques ◽

Series Data ◽

Data Sets ◽

Patients With Diabetes

Recently, the population of the world has increased along with health problems. Diabetes mellitus disease as an example causes issues to the health of many patients globally. The task of this chapter is to develop a dynamic and intelligent decision support system for patients with different diseases, and it aims at examining machine-learning techniques supported by optimization techniques. Artificial neural networks have been used in healthcare for several decades. Most research works utilize multilayer layer perceptron (MLP) trained with back propagation (BP) learning algorithm to achieve diabetes mellitus classification. Nonetheless, MLP has some drawbacks, such as, convergence, which can be slow; local minima can affect the training process. It is hard to scale and cannot be used with time series data sets. To overcome these drawbacks, long short-term memory (LSTM) is suggested, which is a more advanced form of recurrent neural networks. In this chapter, adaptable LSTM trained with two optimizing algorithms instead of the back propagation learning algorithm is presented. The optimization algorithms are biogeography-based optimization (BBO) and genetic algorithm (GA). Dataset is collected locally and another benchmark dataset is used as well. Finally, the datasets fed into adaptable models; LSTM with BBO (LSTMBBO) and LSTM with GA (LSTMGA) for classification purposes. The experimental and testing results are compared and they are promising. This system helps physicians and doctors to provide proper health treatment for patients with diabetes mellitus. Details of source code and implementation of our system can be obtained in the following link “https://github.com/hamakamal/LSTM.”

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Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology

Journal of Soft Computing Paradigm - September 2019 ◽

10.36548/jscp.2021.4.001 ◽

2021 ◽

Vol 3 (4) ◽

pp. 249-259

Author(s):

Joy Iong-Zong Chen ◽

Lu-Tsou Yeh

Keyword(s):

Power Systems ◽

Time Series Data ◽

Short Term Memory ◽

Imbalanced Data ◽

Series Data ◽

Support Vector ◽

Financial Loss ◽

Operating Characteristics ◽

Electricity Theft ◽

Under Sampling

In power systems, electrical losses can be categorized into two types, namely, Technical Losses (TLs) and Non-Technical Losses (NTLs). It has been identified that NTL is more hazardous when compared to TL, primarily due to the factors such as billing errors, faulty meters, electricity theft etc. This proves to be crucial in the power system and will result in heavy financial loss for the utility companies. To identify theft, both academia and industry, use a mechanism known as Electricity Theft Detection (ETD). However, ETD is not used efficiently because of handling high-dimensional data, overfitting issues and imbalanced data. Hence, in this paper, a means of addressing this issue using Random Under-Sampling Boosting (RUSBoost) technique and Long Short-Term Memory (LSTM) technique is proposed. Here, parameter optimization is performed using RUSBoost and abnormal electricity patterns are detected by LSTM technique. Electricity data are pre-processed in the proposed methodology, using interpolation and normalization methods. The data thus obtained are then sent to the LSTM module where feature extraction takes place. These features are then classified using RUSBoost algorithm. Based on the output simulated, it is identified that this methodology addresses several issues such as handling and overfitting of massive time series data and data imbalancing. Moreover, this technique also proves to be more efficient than several other methodologies such as Logistic Regression (LR), Convolutional Neural Network (CNN) and Support Vector Machine (SVM). A comparison is also drawn, taking into consideration the parameters such as Receiver operating characteristics, recall, precision and F1-score.

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A Study on the Application of LSTM to Judge Bike Accidents for Inflating Wearable Airbags

Sensors ◽

10.3390/s21196541 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6541

Author(s):

So-Hyeon Jo ◽

Joo Woo ◽

Gi-Sig Byun ◽

Baek-Soon Kwon ◽

Jae-Hoon Jeong

Keyword(s):

Neural Network ◽

Time Series Data ◽

Short Term Memory ◽

Occurrence Rate ◽

Series Data ◽

Measurement Unit ◽

Judgment Accuracy ◽

Personal Mobility ◽

Long Short Term Memory ◽

Mobility Device

The traffic accident occurrence rate is increasing relative to the increase in the number of people using personal mobility device (PM). This paper proposes an airbag system with a more efficient algorithm to decide the deployment of a wearable bike airbag in case of an accident. The existing wearable airbags are operated by judging the accident situations using the thresholds of sensors. However, in this case, the judgment accuracy can drop against various motions. This study used the long short-term memory (LSTM) model using the sensor values of the inertial measurement unit (IMU) as input values to judge accident occurrences, which obtains data in real time from the three acceleration-axis and three angular velocity-axis sensors on the driver motion states and judges whether or not an accident has occurred using the obtained data. The existing neural network (NN) or convolutional neural network (CNN) model judges only the input data. This study confirmed that this model has a higher judgment accuracy than the existing NN or CNN by giving strong points even in “past information” through LSTM by regarding the driver motion as time-series data.

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LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection

Applied Sciences ◽

10.3390/app10124378 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4378 ◽

Cited By ~ 8

Author(s):

Muhammad Adil ◽

Nadeem Javaid ◽

Umar Qasim ◽

Ibrar Ullah ◽

Muhammad Shafiq ◽

...

Keyword(s):

Power Systems ◽

Time Series Data ◽

Short Term Memory ◽

Imbalanced Data ◽

Electricity Consumption ◽

Series Data ◽

Support Vector ◽

Operating Characteristics ◽

Electricity Theft ◽

Under Sampling

The electrical losses in power systems are divided into non-technical losses (NTLs) and technical losses (TLs). NTL is more harmful than TL because it includes electricity theft, faulty meters and billing errors. It is one of the major concerns in the power system worldwide and incurs a huge revenue loss for utility companies. Electricity theft detection (ETD) is the mechanism used by industry and academia to detect electricity theft. However, due to imbalanced data, overfitting issues and the handling of high-dimensional data, the ETD cannot be applied efficiently. Therefore, this paper proposes a solution to address the above limitations. A long short-term memory (LSTM) technique is applied to detect abnormal patterns in electricity consumption data along with the bat-based random under-sampling boosting (RUSBoost) technique for parameter optimization. Our proposed system model uses the normalization and interpolation methods to pre-process the electricity data. Afterwards, the pre-processed data are fed into the LSTM module for feature extraction. Finally, the selected features are passed to the RUSBoost module for classification. The simulation results show that the proposed solution resolves the issues of data imbalancing, overfitting and the handling of massive time series data. Additionally, the proposed method outperforms the state-of-the-art techniques; i.e., support vector machine (SVM), convolutional neural network (CNN) and logistic regression (LR). Moreover, the F1-score, precision, recall and receiver operating characteristics (ROC) curve metrics are used for the comparative analysis.

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