Data Regression Prediction Using CNN with MATLAB Code

Data Regression Prediction Using CNN with MATLAB Code

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1 Content Introduction

The safe and stable operation of the power system is closely related to the development of the national economy and the safety of personal property. Accurate short-term load forecasting results are important for the power grid to guide the power system in formulating generation plans, coordinating unit operations, scheduling load distribution, and developing maintenance plans. With the development of smart grids and the increasing access of distributed power sources, the factors affecting short-term load forecasting results have become more complex, making the load exhibit stronger nonlinearity. This severe environment also places higher demands on the accuracy and adaptability of short-term load forecasting. The load of the power system has characteristics such as randomness, nonlinearity, and temporal sequence due to the large amount of data and the influence of many factors. Convolutional Neural Networks (CNN) can deeply explore the multiple characteristics of information and obtain useful feature descriptions, making it an accurate and efficient feature extraction method. This article uses CNN to predict the short-term load of the power system, considering the characteristics of the load sequence.

Data Regression Prediction Using CNN with MATLAB Code

Data Regression Prediction Using CNN with MATLAB Code

2 Simulation Code

function X_aug = image_augmentation(Xdata,aug_params)%Randomly augment images slice-by-slice between given ranges, optionally add Gaussian noise:
X_aug = zeros(size(Xdata));
imageAugmenter = imageDataAugmenter( ...    'RandRotation',aug_params.rot, ...    'RandXTranslation',aug_params.trans_x, ...    'RandYTranslation',aug_params.trans_y, ...    'RandXReflection', aug_params.refl_x, ...    'RandYReflection', aug_params.refl_y, ...    'RandScale', aug_params.scale, ...    'RandXShear', aug_params.shear_x, ...    'RandYShear', aug_params.shear_y);
for i = 1:size(Xdata,4)Xtrain_single = Xdata(:,:,:,i);Xtrain_single_aug = augment(imageAugmenter,Xtrain_single);
if aug_params.add_gauss_noise == 1Xtrain_single_aug = imnoise(Xtrain_single_aug,'gaussian',0,aug_params.gauss_noise_var);end
X_aug(:,:,:,i) = Xtrain_single_aug;end

3 Running Results

Data Regression Prediction Using CNN with MATLAB Code

Data Regression Prediction Using CNN with MATLAB Code

4 References

[1] Yan Kang. Research on Bearing Health Assessment and Prediction Based on Convolutional Neural Networks and Grey Models.

Author Introduction: Skilled in intelligent optimization algorithms, neural network prediction, signal processing, cellular automata, image processing, path planning, drones, and various fields of MATLAB simulation. Related MATLAB code issues can be discussed privately.

Some theoretical references are from online literature. If there is any infringement, please contact the author for removal.

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