scholarly journals Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2307
Author(s):  
Mohanad S. Al-Musaylh ◽  
Ravinesh C. Deo ◽  
Yan Li
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Electricity Markets ◽  
Energy Demand ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Model Development ◽  
Series Data ◽  
Percentage Error ◽  
Discrete Wavelet

To support regional electricity markets, accurate and reliable energy demand (G) forecast models are vital stratagems for stakeholders in this sector. An online sequential extreme learning machine (OS-ELM) model integrated with a maximum overlap discrete wavelet transform (MODWT) algorithm was developed using daily G data obtained from three regional campuses (i.e., Toowoomba, Ipswich, and Springfield) at the University of Southern Queensland, Australia. In training the objective and benchmark models, the partial autocorrelation function (PACF) was first employed to select the most significant lagged input variables that captured historical fluctuations in the G time-series data. To address the challenges of non-stationarities associated with the model development datasets, a MODWT technique was adopted to decompose the potential model inputs into their wavelet and scaling coefficients before executing the OS-ELM model. The MODWT-PACF-OS-ELM (MPOE) performance was tested and compared with the non-wavelet equivalent based on the PACF-OS-ELM (POE) model using a range of statistical metrics, including, but not limited to, the mean absolute percentage error (MAPE%). For all of the three datasets, a significantly greater accuracy was achieved with the MPOE model relative to the POE model resulting in an MAPE = 4.31% vs. MAPE = 11.31%, respectively, for the case of the Toowoomba dataset, and a similarly high performance for the other two campuses. Therefore, considering the high efficacy of the proposed methodology, the study claims that the OS-ELM model performance can be improved quite significantly by integrating the model with the MODWT algorithm.

scholarly journals Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Suspended Sediment ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Original Time Series ◽  
Inference System ◽  
Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

scholarly journals Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

2022 ◽  
Vol 24 (3) ◽  
pp. 1-26
Author(s):  
Nagaraj V. Dharwadkar ◽  
Anagha R. Pakhare ◽  
Vinothkumar Veeramani ◽  
Wen-Ren Yang ◽  
Rajinder Kumar Mallayya Math
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Production Line ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Multiple Production ◽  
Wavelet Regression ◽  
The Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

Robust and sparse Dual Tree Complex Wavelet Transform-based Twin Support Vector regression for dense 5G InH  Communications

2021 ◽  
Author(s):  
anis charrada ◽  
Abdelaziz Samet
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Support Vector Regression ◽  
Path Loss ◽  
Series Data ◽  
Support Vector ◽  
Sensitivity Threshold ◽  
Discrete Wavelet ◽  
Twin Support Vector Regression ◽  
Receiver Sensitivity

Abstract A robust and sparse Twin Support Vector Regression based on Dual Tree Discrete Wavelet Transform algorithm is conceived in this paper and applied to 28, 38, 60 and 73-GHz LOS (Line-of-Sight) wireless multipath transmission system in 5G Indoor Hotspot (InH) settings (simple, semi-complex and complex conference rooms) under small receiver sensitivity threshold. The algorithm establishes a denoising process in the learning phase based on Dual Tree Discrete Wavelet Transform (DT-CWT) which is suitable for time-series data. Additionally, the Close-In (CI) free space reference distance path loss model is analyzed and the large-scale propagation and probability distribution functions are investigated by determining the PLE (Path Loss Exponent) and the standard deviation of Shadow Factor (SF) for each InH scenario under consideration. Performance are evaluated under twelve (12) configuration scenarios, according to three criteria: mobility (0/3mps), receiver sensitivity threshold (-80/-120 dBm) and type of the InH area (simple, semi-complex and complex conference room). Experimental results confirm the effectiveness of the proposed approach compared to other standard techniques.

Textile Production Line Monitoring System Using Wavelet-Regression Neural Network

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Production Line ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Multiple Production ◽  
Wavelet Regression ◽  
The Neural Network

This paper presents design and experiments for a production line monitoring system. The system is designed based on an existing production line which mapping to the smart grid standards. The Discrete wavelet transform (DWT) and regression neural network (RNN) are applied to the operation modes data analysis. DWT used to preprocess the signals to remove noise from the raw signals. The output of DWT energy distribution has given as an input to the GRNN model. The neural network GRNN architecture involves multi-layer structures. Mean Absolute Percentage Error (MAPE) loss has used in the GRNN model, which is used to forecast the time-series data. Current research results can only apply to the single production line but in future, it will used for multiple production lines.

scholarly journals Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

2019 ◽  
Vol 11 (3) ◽  
pp. 793 ◽  
Author(s):  
Rashad Aliyev ◽  
Sara Salehi ◽  
Rafig Aliyev
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Forecast Accuracy ◽  
Fuzzy Rule ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error

Receiving appropriate forecast accuracy is important in many countries’ economic activities, and developing effective and precise time series model is critical issue in tourism demand forecasting. In this paper, fuzzy rule-based system model for hotel occupancy forecasting is developed by analyzing 40 months’ time series data and applying fuzzy c-means clustering algorithm. Based on the values of root mean square error and mean absolute percentage error which are metrics for measuring forecast accuracy, it is defined that the model with 7 clusters and 4 inputs is the optimal forecasting model for hotel occupancy.

Pre-Processing Influence on Identification of Optimal Path Length for NIR Analysis of Tea Polyphenols

2013 ◽  
Vol 791-793 ◽  
pp. 265-268
Author(s):  
Xiao Li Yang ◽  
Qiong He ◽  
Li Liu ◽  
Tong Yang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Path Length ◽  
Near Infrared ◽  
Optimal Path ◽  
Tea Polyphenols ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Least Squares Regression ◽  
Path Lengths

We investigated the optical path length to tea polyphenols (TP) determination in Puer tea by near infrared (NIR) spectroscopy. The NIR spectra samples include three path lengths (1mm, 2mm and 5mm). Firstly, spectra were pre-processed to eliminate useless information. Then, determination model was constructed by partial least squares regression. To study the influence of pre-processing on identification of optimal path for NIR analysis of tea polyphenols, we applied five techniques to pre-process spectra, including normalization, standardization, centralization, derivative and discrete wavelet transform. Comparison of the mean absolute percentage error (MAPE) of the models with different path lengths show that the models constructed with spectra collected in 2mm path length gave the best results. 1mm path length gained the uncorrected determination results. Normalization, centralization and derivative are better than standardization or discrete wavelet transform for pre-processing.

scholarly journals Interval Grey Prediction Models with Forecast Combination for Energy Demand Forecasting

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 960
Author(s):  
Peng Jiang ◽  
Yi-Chung Hu ◽  
Wenbao Wang ◽  
Hang Jiang ◽  
Geng Wu
Keyword(s):  
Regression Analysis ◽  
Energy Demand ◽  
Prediction Models ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Series Data ◽  
Grey Prediction ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Energy Demand Forecasting

Time series data for decision problems such as energy demand forecasting are often derived from uncertain assessments, and do not meet any statistical assumptions. The interval grey number becomes an appropriate representation for an uncertain and imprecise observation. In order to obtain nonlinear interval grey numbers with better forecasting accuracy, this study proposes a combined model by fusing interval grey numbers estimated by neural networks (NNs) and the grey prediction models. The proposed model first uses interval regression analysis using NNs to estimate interval grey numbers for a real valued sequence; and then a grey residual modification model is constructed using the upper and lower wrapping sequences obtained by NNs. It turns out that two different kinds of interval grey numbers can be estimated by nonlinear interval regression analysis. Forecasting accuracy on real data sequences was then examined by the best non-fuzzy performance values of the combined model. The proposed combined model performed well compared with the other interval grey prediction models considered.

scholarly journals Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

2019 ◽  
Vol 9 (6) ◽  
pp. 1108 ◽  
Author(s):  
Yao Liu ◽  
Lin Guan ◽  
Chen Hou ◽  
Hua Han ◽  
Zhangjie Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Wind Power ◽  
Short Term Memory ◽  
Wind Farms ◽  
Absolute Error ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Short Term

A wind power short-term forecasting method based on discrete wavelet transform and long short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to effectively exhibit the dynamic behavior of the wind power time series. The discrete wavelet transform is introduced to decompose the non-stationary wind power time series into several components which have more stationarity and are easier to predict. Each component is dug by an independent LSTM. The forecasting results of the wind power are obtained by synthesizing the prediction values of all components. The prediction accuracy has been improved by the proposed method, which is validated by the MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error) of experimental results of three wind farms as the benchmarks. Wind power forecasting based on the proposed method provides an alternative way to improve the security and stability of the electric power network with the high penetration of wind power.

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