Table of contents
1. Algorithm simulation effect
2. Summary of theoretical knowledge involved in algorithms
2.2. Long short-term memory network
4. Obtain the complete algorithm code file
1. Algorithm simulation effect
The matlab2022a simulation results are as follows:
2. Summary of theoretical knowledge involved in algorithms
The power load data prediction algorithm based on GA-LSTM genetic optimization long short-term memory network is a hybrid model that combines genetic algorithm (GA) and long short-term memory network (LSTM) for predicting power load data. This algorithm optimizes the hyperparameters of the LSTM model through a genetic algorithm to improve the prediction performance of the model. The principle, formula and implementation process of this algorithm will be introduced in detail below.
2.1. Genetic algorithm
Genetic algorithm is an optimization algorithm based on biological evolution theory, which is used to solve optimization problems. It simulates the evolutionary process of nature and uses operations such as selection, crossover and mutation to find the optimal solution. In genetic algorithms, each solution is called an individual, and each individual is represented by a chromosome. Chromosomes are made up of genes, and each gene has a value called an allele.
The basic process of genetic algorithm is as follows:
- Initialization population: Randomly generate a group of individuals as the initial population.
- Calculate fitness: For each individual, calculate the value of its fitness function. The fitness function is used to measure the quality of an individual. The larger the value, the better the individual.
- Selection operation: According to the value of the fitness function, select the individual with higher fitness as the parent generation. There are many ways to select operations, such as roulette selection, tournament selection, etc.
- Crossover operation: perform crossover operation on the selected parents to generate children. Crossover operation is the process of exchanging genes between the chromosomes of two parents according to a certain probability.
- Mutation operation: perform mutation operation on the offspring to change the value of the gene on the chromosome. The probability of mutation operation is low and generally takes a smaller value.
- Iterative update: Repeat selection, crossover and mutation operations until the termination condition is met and the optimal solution or approximately optimal solution is obtained.
2.2. Long short-term memory network
Long short-term memory network is a variant of recurrent neural network (RNN) used to process sequence data. It solves the vanishing gradient problem that occurs when traditional RNN processes long sequences by introducing memory units to save previous information. The basic structure of the LSTM model is as follows:
The LSTM model consists of an input gate, a forget gate, an output gate and a memory unit. The input gate is responsible for sending input data into the memory unit, the forgetting gate is responsible for forgetting the output of the previous moment from the memory unit, and the output gate is responsible for taking the content of the memory unit as output. The memory unit stores the output of the previous moment and the input of the current moment and is used to calculate the output of the current moment.
The calculation process of the LSTM model is as follows:
- Input gate: Combine the input xt at the current moment and the output ht−1 at the previous moment into a vector it through a fully connected layer, and then map the vector it into the input gate output gt and through a nonlinear function Candidate output ct.
- Forgetting gate: Combine the input xt at the current moment and the output ht−1 at the previous moment into a vector ft through a fully connected layer, and then map the vector ft into the forgetting gate output rt through a nonlinear function. Used to decide what information needs to be forgotten.
- Memory unit: The memory unit stores the output ht−1 of the previous moment and the input xt of the current moment, and is used to calculate the output of the current moment. Specifically, the output ht−1 of the previous moment and the input xt of the current moment are combined into a vector zt through the fully connected layer, and then the vector zt is mapped into the candidate output ct through a nonlinear function. At the same time, the vector rt output by the forgetting gate is multiplied by the memory unit state ct−1 at the previous moment to obtain the forgotten information dt, and then the candidate output ct is added to the forgotten information dt to obtain the current The memory unit state ct at the moment.
- Output gate: Combine the memory unit state ct and the output ht−1 of the previous moment into a vector yt through the fully connected layer, and then map the vector to the output ht of the current moment through a nonlinear function.
2.3. GA-LSTM hybrid model
The power load data prediction algorithm based on the GA-LSTM genetic optimization long and short memory network combines the genetic algorithm and LSTM, and optimizes the hyperparameters of the LSTM model through the genetic algorithm to improve the prediction performance of the model. The implementation process of this algorithm is as follows:
- Data preprocessing: Preprocess the original power load data, including normalization and other processing to ensure the quality and consistency of the data. At the same time, the data is divided into a training set and a test set for training and testing the model.
- LSTM model parameter setting: Set the parameters of the LSTM model based on the extracted features. These parameters will serve as optimization variables for the genetic algorithm. The optimization parameter selected for this project is the hidden layer size.
- Build a GA-LSTM hybrid model: Combine the genetic algorithm and the LSTM model to build a GA-LSTM hybrid model. The specific method is to use the parameters of the LSTM model as optimization variables of the genetic algorithm, and use the genetic algorithm to search for the optimal parameters. During the search process, operations such as crossover and mutation are used to generate new parameter combinations, and their merits and demerits are evaluated through the fitness function, and finally a set of optimal parameters is obtained.
- Training the GA-LSTM hybrid model: Use the training set to train the GA-LSTM hybrid model so that it can learn the characteristics and patterns of the data. During the training process, the backpropagation algorithm is used to calculate gradients and update parameters.
- Forecasting power load data: Use the trained GA-LSTM hybrid model to predict the test set and output the prediction results. In order to obtain better prediction results, sliding window technology can be used to divide the test set into blocks and predict each block.
The advantage of this algorithm is that it combines genetic algorithm and LSTM to find the optimal solution through continuous iteration and cross-mutation. It overcomes the problems of large LSTM model parameters and complex training process, and improves the learning ability and generalization performance of the model. At the same time, the algorithm also has good versatility and can be applied to data prediction problems in other fields.
3.MATLAB core program
...............................................................
while gen < MAXGEN
gen
Pe0 = 0.999;
pe1 = 0.001;
FitnV=ranking(Objv);
Selch=select('sus',Chrom,FitnV);
Selch=recombin('xovsp', Selch,Pe0);
Selch=mut( Selch,pe1);
phen1=bs2rv(Selch,FieldD);
for a=1:1:NIND
X = phen1(a);
%计算对应的目标值
[epls] = func_obj(X);
E = epls;
JJ(a,1) = E;
end
Objvsel=(JJ);
[Chrom,Objv]=reins(Chrom,Selch,1,1,Objv,Objvsel);
gen=gen+1;
Error2(gen) = mean(JJ);
end
figure
plot(smooth(Error2,MAXGEN),'linewidth',2);
grid on
xlabel('迭代次数');
ylabel('遗传算法优化过程');
legend('Average fitness');
[V,I] = min(JJ);
X = phen1(I);
numFeatures = 2;
numResponses = 1;
numHiddenUnits = round(X);% 定义隐藏层中LSTM单元的数量
layers = [ ...% 定义网络层结构
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits)
...............................................................
net = trainNetwork(P,T,layers,options);
ypred = predict(net,[P],'MiniBatchSize',1);
figure;
subplot(211);
plot(T)
hold on
plot(ypred)
xlabel('days');
ylabel('负荷');
legend('实际负荷','LSTM预测负荷');
subplot(212);
plot(T-ypred)
xlabel('days');
ylabel('LSTM误差');
save R2.mat T ypred
0X_030m4. Obtain the complete algorithm code file
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