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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system
Signal processing Image processing Path planning Cellular automaton Drone
Content introduction
With the continuous development of the power industry and the continuous increase of power load, the accuracy and accuracy requirements for power load forecasting are becoming higher and higher. Therefore, how to effectively predict power load has become a popular research direction. Temporal convolutional neural network (TCN) is a new type of neural network model that can effectively process time series data and therefore has broad application prospects in power load forecasting. This paper optimizes the time convolutional neural network DBO-TCN based on the dung beetle algorithm to implement the power load forecasting algorithm process, aiming to improve the accuracy and accuracy of power load forecasting.
1. Dung beetle algorithm
The dung beetle algorithm is an optimization algorithm based on the ant colony algorithm. It searches for the optimal solution by simulating the search behavior of dung beetles between food and nests. The dung beetle algorithm has global optimization capabilities and local optimization capabilities, and can effectively avoid falling into local optimal solutions. Therefore, the dung beetle algorithm has wide applications in optimization problems.
2. Temporal convolutional neural network
Temporal convolutional neural network is a new type of neural network model that uses a new convolution method to effectively process time series data. The temporal convolutional neural network has a multi-layer convolution layer and residual network structure, which can effectively extract features in time series data, thereby achieving accurate prediction of time series data.
3. Electric power load forecasting algorithm process
The power load forecasting algorithm process is mainly divided into four steps: data preprocessing, model construction, model training and model prediction.
(1) Data preprocessing
Data preprocessing is the first step in power load forecasting, which includes three steps: data cleaning, data normalization and data segmentation. Data cleaning refers to processing original data to remove unreasonable data such as outliers and missing values. Data normalization refers to normalizing the original data so that the data are in the same dimension. Data segmentation refers to dividing the data set into two parts: a training set and a test set. The training set is used for model training, and the test set is used for model testing and evaluation.
(2) Model construction
Model construction is the second step in power load forecasting, which includes model selection and model parameter setting. Model selection refers to selecting a model suitable for power load forecasting. This article selects the temporal convolutional neural network model. Model parameter setting refers to setting the hyperparameters of the model, including convolution kernel size, number of convolution layers, residual network structure, etc.
(3) Model training
Model training is the third step in power load forecasting, which includes three steps: model initialization, loss function setting and optimization algorithm selection. Model initialization refers to initializing the parameters of the model, and loss function setting refers to setting the loss function of the model. This article chooses the mean square error as the loss function. Optimization algorithm selection refers to selecting an optimization algorithm to train the model. This article chooses the dung beetle algorithm as the optimization algorithm.
(4) Model prediction
Model prediction is the last step in power load prediction, which includes two steps: model testing and prediction result display. Model testing refers to using the test set to test and evaluate the model, and prediction result display refers to visually displaying the model prediction results to facilitate user analysis and decision-making.
4. Summary
This paper optimizes the time convolutional neural network DBO-TCN based on the dung beetle algorithm to implement the power load prediction algorithm process. Through the four steps of data preprocessing, model construction, model training and model prediction, accurate prediction of power load is achieved. This algorithm has global optimization capabilities and local optimization capabilities, and can effectively improve the accuracy and accuracy of power load forecasting.
Part of the code
%% Clear environment variables</code><code>warning off % Close alarm information</code><code>close all % Close open figure window</code><code>clear % Clear variables</code><code>clc % clear command line</code><code>?</code><code>%% import data</code><code>res = xlsread('dataset.xlsx');</code><code>?</code><code>%% divide the training set and test set</code><code>temp = randperm(357);</code><code>?</code><code>P_train = res(temp(1: 240), 1: 12)';</code><code>T_train = res(temp(1: 240), 13)';</code><code>M = size(P_train , 2);</code><code>?</code><code>P_test = res(temp(241: end), 1: 12)';</code><code>T_test = res(temp(241 : end), 13)';</code><code>N = size(P_test, 2);</code><code>?</code><code>%% data normalization</code><code>[p_train, ps_input] = mapminmax(P_train, 0, 1);</code><code>p_test = mapminmax('apply', P_test, ps_input);</code><code>t_train = ind2vec(T_train) ;</code><code>t_test = ind2vec(T_test );
Operation results
