Electricity Peak Load Demand using De-noising Wavelet Transform integrated with Neural Network Methods

2016 ◽  
Vol 6 (1) ◽  
pp. 12
Author(s):  
Pituk Bunnoon
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Power Systems ◽  
Peak Load ◽  
Electric Power System ◽  
Load Demand ◽  
The Neural Network ◽  
Forecasting Method ◽  
Neural Network Algorithm ◽  
The One

One of most important elements in electric power system planning is load forecasts. So, in this paper proposes the load demand forecasts using de-noising wavelet transform (DNWT) integrated with neural network (NN) methods. This research, the case study uses peak load demand of Thailand (Electricity Generating Authority of Thailand: EGAT). The data of demand will be analyzed with many influencing variables for selecting and classifying factors. In the research, the de-noising wavelet transform uses for decomposing the peak load signal into 2 components these are detail and trend components. The forecasting method using the neural network algorithm is used. The work results are shown a good performance of the model proposed. The result may be taken to the one of decision in the power systems operation.

scholarly journals Electricity Peak Load Demand using De-noising Wavelet Transform integrated with Neural Network Methods

2016 ◽  
Vol 6 (1) ◽  
pp. 12
Author(s):  
Pituk Bunnoon
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Power Systems ◽  
Peak Load ◽  
Electric Power System ◽  
Load Demand ◽  
The Neural Network ◽  
Forecasting Method ◽  
Neural Network Algorithm ◽  
The One

One of most important elements in electric power system planning is load forecasts. So, in this paper proposes the load demand forecasts using de-noising wavelet transform (DNWT) integrated with neural network (NN) methods. This research, the case study uses peak load demand of Thailand (Electricity Generating Authority of Thailand: EGAT). The data of demand will be analyzed with many influencing variables for selecting and classifying factors. In the research, the de-noising wavelet transform uses for decomposing the peak load signal into 2 components these are detail and trend components. The forecasting method using the neural network algorithm is used. The work results are shown a good performance of the model proposed. The result may be taken to the one of decision in the power systems operation.

Power System Fault Diagnosis Based on Wavelet Transform and Neural Networks

2014 ◽  
Vol 705 ◽  
pp. 255-258 ◽  
Author(s):  
Jun Liu ◽  
Lin Li ◽  
Qing Tao Long
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Power System ◽  
Electric Power ◽  
Electric Power System ◽  
Singularity Detection ◽  
Fault Recognition ◽  
The Neural Network ◽  
Simulation Results

Using the principle of wavelet transform in the aspect of signal singularity detection analyzes and detects the electric power system fault signal. Then we extract signal feature near the fault moment and sent the feature vectors into the neural network. The simulation results fully prove the effectiveness and superiority of combining wavelet transform and neural network in electric power system fault recognition.

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 723-728 ◽  
Author(s):  
Chenhao Niu ◽  
Xiaomin Xu ◽  
Yan Lu ◽  
Mian Xing
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Load Forecasting ◽  
Optimal Solution ◽  
Electric Power System ◽  
Local Optimum ◽  
Good Prediction ◽  
Ant Colony Optimization Algorithm ◽  
The Neural Network ◽  
Power Load

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

The Design of a Wavelet-Based Neural Network Adaptive Filter

2013 ◽  
Vol 860-863 ◽  
pp. 2791-2795
Author(s):  
Qian Xiao ◽  
Yu Shan Jiang ◽  
Ru Zheng Cui
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Real Time ◽  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Lms Algorithm ◽  
The Neural Network ◽  
Simulation Results

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

scholarly journals Adaptive Solar Power Forecasting based on Machine Learning Methods

2018 ◽  
Vol 8 (11) ◽  
pp. 2224 ◽  
Author(s):  
Yu Wang ◽  
Hualei Zou ◽  
Xin Chen ◽  
Fanghua Zhang ◽  
Jie Chen
Keyword(s):  
Power Systems ◽  
Solar Power ◽  
Machine Learning Methods ◽  
Load Demand ◽  
Forecasting Method ◽  
Combining Data ◽  
Data Updating ◽  
Solar Power Forecasting ◽  
Forecasting Errors ◽  
Power Forecasting

Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.

Research of Fabric Weave Pattern’s Recognition Using Texture Orientation Features

2011 ◽  
Vol 328-330 ◽  
pp. 1763-1767
Author(s):  
Jian Qiang Shen ◽  
Xuan Zou
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Radon Transform ◽  
Image Decomposition ◽  
Automatic Recognition ◽  
Line Detection ◽  
Original Image ◽  
Novel Approach ◽  
The Neural Network ◽  
Texture Orientation

A novel approach is proposed for measuring fabric texture orientations and recognizing weave patterns. Wavelet transform is suited for fabric image decomposition and Radon Transform is fit for line detection in fabric texture. Since different weave patterns have their own regular orientations in original image and sub-band images decomposed by Wavelet transform, these orientations features are extracted and used as SOM and LVQ inputs to achieve automatic recognition of fabric weave. The experimental results show that the neural network of LVQ is more effective than SOM. The contribution of this study is that it not only can identify fundamental fabric weaves but also can classify double layer and some derivative twill weaves such as angular twill and pointed twill.

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