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- 저자 : Tien Minh Cao (검색결과 2 건)
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A Statistical Data-Filtering Method Proposed for Short-Term Load Forecasting Models
Duong Minh Bui,Phuc Duy Le,Tien Minh Cao,Hung Nguyen,Trang Thi Pham,Duy Anh Pham 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.5
Reliability assessment of the SCADA-system based load data is necessary for improving accuracy of short-term load forecasting (STLF) methods in a distribution network (DN). Specifi cally, the reliability evaluation of the load data is to properly eliminate noise/outliers caused by random power consumption behaviors or the sudden change in load demand from industrial and residential customers in the DN. Thus, this paper proposes a novel statistical data-fi ltering method, working at an input data pre-processing stage, which will evaluate the reliability of input load data by analyzing all possible data confi dence levels in order to fi lter-out the noise/outliers for accuracy improvement of diff erent short-term load forecasting models. The proposed statistical data-fi ltering method is also compared to other existing data-fi ltering methods (such as Kalman Filter, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Discrete Wavelet Transform (DWT) and Singular Spectrum Analysis (SSA)). Moreover, several case studies of short-term load forecasting for a typical 22 kV distribution network in Vietnam are conducted with an Artifi cial Neural Network (ANN) model, a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model, a combined model of Long Short-Term Memory Network and Convolutional Neural Network (LSTM-CNN), and a conventional Autoregressive Integrated Moving Average (ARIMA) model to validate the statistical data-fi ltering method proposed. The achieved results demonstrate which the STLF using ANN, LSTM-RNN, LSTMCNN, and ARIMA models with the statistical data-fi ltering method can all outperform those with the existing data-fi ltering methods. Additionally, the numerical results also indicate that in case the SCADA-based load data is normally distributed, time-series forecasting models should be more preferred than neural network models; otherwise, when the SCADA-based load data contains multiple normally distributed sub-datasets, neural network-based prediction models are highly recommended.
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Improving photocatalytic oxidation of semiconductor (TiO2, SnO2, ZnO)/CNTs for NOx removal
Hoang Phuong Nguyen,Thi Minh Cao,Tien-Thanh Nguyen,Viet Van Pham 한국공업화학회 2023 Journal of Industrial and Engineering Chemistry Vol.127 No.-
Traditional semiconductors, i.e., SnO2, ZnO, and TiO2 have a good photocatalytic activity but still manydisadvantages to applying in industry. In this study, these semiconductors are combined by commercialCNTs to form heterojunctions by a ball-milling route. The physical–chemical analyses include X-raydiffraction pattern (XRD), diffused reflectance spectra (UV–Vis DRS), Fourier transform infrared spectrum(FTIR), Mott-Schottky analysis, high-resolution transmission electron microscopy (HRTEM), and selectedarea electron diffraction (SAED) are run to characterize the as-prepared materials. The total photocatalyticNOx efficiency of SnO2/CNTs, ZnO/CNTs, and TiO2/CNTs composites generate green products, i.e.,NO3 – , HNO3, etc. with 10%, 10.1%, and 41.25% after 30 min under visible light illumination, respectively. We also found that ZnO/CNTs easily inactivated the photocatalytic ability and converted NO gas to a moretoxic product. Meanwhile, the highest selectivity of green products conversion belongs to SnO2/CNTsnanocomposite. Our findings will design and select an excellent photocatalytic oxidation system forNOx removal towards a flue gas treatment established.
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