基于TensorFlow2.0利用LSTM神经网络对明天的电价进行预测, 使用前 24个时刻的电价和3个影响因素作为输入, 该时刻电价作为输出。 网络由 200 节点 LSTM 层和 1 层全连接 层构成, 采用 ReLu 激活函数和 adam 优化器, 迭代 100 次

好的，首先需要导入相关的库和数据集：

import tensorflow as tf
import numpy as np

# 加载数据集
data = # 加载数据集的代码

接下来，你需要对数据进行预处理，包括划分训练集和测试集，以及归一化处理：

# 划分训练集和测试集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 归一化处理
mean = train_data.mean(axis=0)
std = train_data.std(axis=0)
train_data = (train_data - mean) / std
test_data = (test_data - mean) / std

然后，你需要编写一个生成器函数，用于生成训练数据和测试数据：

def generate_data(data, lookback, delay, min_index, max_index, shuffle=False, batch_size=128, step=6):
    if max_index is None:
        max_index = len(data) - delay - 1
    i = min_index + lookback
    while True:
        if shuffle:
            rows = np.random.randint(min_index + lookback, max_index, size=batch_size)
        else:
            if i + batch_size >= max_index:
                i = min_index + lookback
            rows = np.arange(i, min(i + batch_size, max_index))
            i += len(rows)
        samples = np.zeros((len(rows), lookback // step, data.shape[-1]))
        targets = np.zeros((len(rows),))
        for j, row in enumerate(rows):
            indices = range(rows[j] - lookback, rows[j], step)
            samples[j] = data[indices]
            targets[j] = data[rows[j] + delay][0]
        yield samples, targets

接下来，你可以定义模型：

lookback = 24
step = 1
delay = 0
batch_size = 128
train_gen = generate_data(train_data, lookback=lookback, delay=delay, min_index=0,
                           max_index=train_size, shuffle=True, step=step, batch_size=batch_size)
test_gen = generate_data(test_data, lookback=lookback, delay=delay, min_index=train_size,
                          max_index=None, step=step, batch_size=batch_size)
val_steps = (len(test_data) - lookback) // batch_size
test_steps = (len(test_data) - lookback) // batch_size

model = tf.keras.models.Sequential([
    tf.keras.layers.LSTM(200, input_shape=(None, train_data.shape[-1])),
    tf.keras.layers.Dense(1)
])
model.compile(optimizer=tf.keras.optimizers.Adam(), loss='mae')

最后，你可以训练模型并进行预测：

history = model.fit(train_gen, steps_per_epoch=500, epochs=20, validation_data=test_gen, validation_steps=val_steps)

# 进行预测
preds = model.predict(test_data)

在训练完模型后，你可以使用均方根误差 (RMSE) 或平均绝对误差 (MAE) 来评估模型的预测准确性：

# 计算 RMSE 或 MAE
from sklearn.metrics import mean_squared_error, mean_absolute_error
rmse = np.sqrt(mean_squared_error(test_data[:, 0], preds[:, 0]))
mae = mean_absolute_error(test_data[:, 0], preds[:, 0])