Shakirah Mohd Taib

Short term residential load forecasting using long short-term memory recurrent neural network

Publisher: Institute of Advanced Engineering and Science

Date: 10-2022

DOI: 10.11591/IJECE.V12I5.PP5589-5599

Abstract: span Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions. /span

A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool

Publisher: MDPI AG

Date: 15-04-2021

DOI: 10.3390/SYM13040686

Abstract: High blood pressure (BP) may lead to further health complications if not monitored and controlled, especially for critically ill patients. Particularly, there are two types of blood pressure monitoring, invasive measurement, whereby a central line is inserted into the patient’s body, which is associated with infection risks. The second measurement is cuff-based that monitors BP by detecting the blood volume change at the skin surface using a pulse oximeter or wearable devices such as a smartwatch. This paper aims to estimate the blood pressure using machine learning from photoplethysmogram (PPG) signals, which is obtained from cuff-based monitoring. To avoid the issues associated with machine learning such as improperly choosing the classifiers and/or not selecting the best features, this paper utilized the tree-based pipeline optimization tool (TPOT) to automate the machine learning pipeline to select the best regression models for estimating both systolic BP (SBP) and diastolic BP (DBP) separately. As a pre-processing stage, notch filter, band-pass filter, and zero phase filtering were applied by TPOT to eliminate any potential noise inherent in the signal. Then, the automated feature selection was performed to select the best features to estimate the BP, including SBP and DBP features, which are extracted using random forest (RF) and k-nearest neighbors (KNN), respectively. To train and test the model, the PhysioNet global dataset was used, which contains 32.061 million s les for 1000 subjects. Finally, the proposed approach was evaluated and validated using the mean absolute error (MAE). The results obtained were 6.52 mmHg for SBS and 4.19 mmHg for DBP, which show the superiority of the proposed model over the related works.

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

Publisher: MDPI AG

Date: 09-10-2021

DOI: 10.3390/ELECTRONICS10202453

Abstract: Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great significance for improving the reliability and safety of the engine system. Due to the high dimension and complex features of sensor data in RUL prediction, this paper proposes four data-driven prognostic models based on deep neural networks (DNNs) with an attention mechanism. To improve DNN feature extraction, data are prepared using a sliding time window technique. The raw data collected after normalizing is simply fed into the suggested network, requiring no prior knowledge of prognostics or signal processing and simplifying the proposed method’s applicability. In order to verify the RUL prediction ability of the proposed DNN techniques, the C-MAPSS benchmark dataset of the turbofan engine system is validated. The experimental results showed that the developed long short-term memory (LSTM) model with attention mechanism achieved accurate RUL prediction in both scenarios with a high degree of robustness and generalization ability. Furthermore, the proposed model performance outperforms several state-of-the-art prognosis methods, where the LSTM-based model with attention mechanism achieved an RMSE of 12.87 and 11.23 for FD002 and FD003 subset of data, respectively.

Deep-Learning Based Prognosis Approach for Remaining Useful Life Prediction of Turbofan Engine

Publisher: MDPI AG

Date: 03-10-2021

DOI: 10.3390/SYM13101861

Abstract: The entire life cycle of a turbofan engine is a type of asymmetrical process in which each engine part has different characteristics. Extracting and modeling the engine symmetry characteristics is significant in improving remaining useful life (RUL) predictions for aircraft components, and it is critical for an effective and reliable maintenance strategy. Such predictions can improve the maximum operating availability and reduce maintenance costs. Due to the high nonlinearity and complexity of mechanical systems, conventional methods are unable to satisfy the needs of medium- and long-term prediction problems and frequently overlook the effect of temporal information on prediction performance. To address this issue, this study presents a new attention-based deep convolutional neural network (DCNN) architecture to predict the RUL of turbofan engines. The prognosability metric was used for feature ranking and selection, whereas a time window method was employed for s le preparation to take advantage of multivariate temporal information for better feature extraction by means of an attention-based DCNN model. The validation of the proposed model was conducted using a well-known benchmark dataset and evaluation measures such as root mean square error (RMSE) and asymmetric scoring function (score) were used to validate the proposed approach. The experimental results show the superiority of the proposed approach to predict the RUL of a turbofan engine. The attention-based DCNN model achieved the best scores on the FD001 independent testing dataset, with an RMSE of 11.81 and a score of 223.

Ontology development for programming related materials

Publisher: Springer Berlin Heidelberg

Date: 2011

DOI: 10.1007/978-3-642-22203-0_26

Internet of Things (IoT) Security Challenges and Solutions: A Systematic Literature Review

Publisher: Springer Singapore

Date: 2021

DOI: 10.1007/978-981-16-8059-5_9

University Of Tasmania

Location: No location found

View Organisation

Universiti Utara Malaysia

Location: Malaysia

View Organisation

Universiti Teknologi PETRONAS

Location: Malaysia

View Organisation

University Of Tasmania

Location: Australia

View Organisation