Simulink开发项1000例实战专栏--实例196：Simulink实例--基于LSTM的电动汽车充电需求预测仿真

 %% 数据清洗与归一化

    
 function [X_train, y_train, scaler] = preprocess_data(raw_data)
    
     % 缺失值处理
    
     X_train = fillmissing(raw_data(:,1:5), 'linear');
    
     
    
     % 归一化处理
    
     scaler = minmax scaler(X_train(:,2:6));
    
     X_train(:,2:6) = scaler.transform(X_train(:,2:6));
    
     
    
     % 时间序列重塑
    
     X_train = reshape(X_train, [], 7, 1); % [samples, time_steps, features]
    
 end
    
    
    
    

 %% Simulink LSTM模型

    
 function [y_pred] = lstm_model(X, hidden_size, num_layers)
    
     % 参数初始化
    
     layers = [
    
     sequenceInputLayer(7) 
    
     lstmLayer(hidden_size, 'Dropout', 0.2)
    
     fullyConnectedLayer(1)
    
     regressionLayer
    
     ];
    
     
    
     % 训练配置
    
     options = trainingOptions('adam', ...
    
     'MaxEpochs', 200, ...
    
     'Plots', 'training-progress');
    
     
    
     % 模型训练
    
     net = trainNetwork(X, y, layers, options);
    
     
    
     % 预测
    
     y_pred = predict(net, X);
    
 end
    
    
    
    

 %% Python代码实现

    
 from fbprophet import Prophet
    
 import pandas as pd
    
  
    
 def hybrid_model(train_df):
    
     # Prophet模型
    
     model = Prophet(daily_seasonality=True)
    
     model.fit(train_df.rename(columns={'date':'ds', 'demand':'y'}))
    
     
    
     # LSTM模型
    
     from tensorflow.keras.models import Sequential
    
     model_lstm = Sequential([
    
     LSTM(64, input_shape=(7,1)),
    
     Dense(32, activation='relu'),
    
     Dense(1)
    
     ])
    
     model_lstm.compile(optimizer='adam', loss='mse')
    
     model_lstm.fit(train_df[['timestep', 'temp', 'humidity', 'price', 'soc']], 
    
                 train_df['demand'], 
    
                 epochs=50, 
    
                 batch_size=32)
    
     
    
     return model, model_lstm
    
    
    
    

 %% 充电需求生成

    
 function [P_demand, T_env] = demand_profile()
    
     % 时间(s) | 温度(℃) | 车辆密度（辆/km²） | 电价($/kWh)
    
     data = [0-300, 25, 50, 0.12; 300-600, 30, 200, 0.15; 600-900, 15, 80, 0.18];
    
     t = cumsum(data(:,3));
    
     P_demand = interp1(t, data(:,2), linspace(0,900,1000))';
    
     T_env = interp1(t, data(:,1), linspace(0,900,1000))';
    
 end
    
    
    
    

 %% 贝叶斯优化实现

    
 function [best_params] = hyperparameter_tuning()
    
     % 定义搜索空间
    
     param_space = {
    
     'hidden_size'        , [32 64 128]
    
     'num_layers'         , [1 2 3]
    
     'learning_rate'      , logspace(-4, -2, 10)
    
     'dropout_rate'       , [0.1 0.2 0.3]
    
     };
    
     
    
     % 目标函数
    
     objective = @(params) train_lstm(params);
    
     
    
     % 贝叶斯优化器
    
     opt = bayesianOptimization(objective, param_space, ...
    
     'AcquisitionFunction', @expectedimprovement, ...
    
     'MaxIterations', 50);
    
     
    
     best_params = opt.X_opt;
    
 end
    
    
    
    

 // C++代码移植示例（TensorFlow Lite）

    
 #include <tensorflow/lite/interpreter.h>
    
 #include <tensorflow/lite/kernels/register.h>
    
  
    
 int main() {
    
     // 加载模型
    
     TfLiteInterpreter* interpreter = new TfLiteInterpreter();
    
     interpreter->AllocateTensors();
    
     
    
     // 数据预处理
    
     float input[7] = { /* 时间戳, 温度, 湿度, 电价, SOC, 车辆密度, 历史需求 */ };
    
     interpreter->SetTensor("input_1", input, 7);
    
     
    
     // 运行推理
    
     interpreter->Invoke();
    
     float* output = interpreter->GetTensor("output_1");
    
     
    
     // 后处理
    
     printf("Predicted demand: %.2f MW\n", *output);
    
     return 0;
    
 }