【PSO-LSTM】基于PSO优化LSTM网络的电力负荷预测（Python代码实现）

很高兴为您介绍基于PSO优化LSTM网络的电力负荷预测的Python代码实现。

首先，我们需要导入必要的Python库，包括numpy、pandas、sklearn和Keras等。其中，sklearn和Keras是机器学习库，可以使我们轻松地实现LSTM网络。

```python
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
```

接着，我们需要读取并处理数据集。这里我们假设我们已经有了一份电力负荷的历史数据集，其中包含了每小时的电力负荷数据。

```python
data = pd.read_csv('electricity.csv')
values = data.values
```

数据处理过程中，我们首先需要对数据进行归一化处理，以便更好地训练LSTM模型。

```python
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
```

接下来，我们需要将数据集转换为监督学习问题。这意味着我们需要将电力负荷的数据转换为输入和输出序列。在这里，我们将使用前n个小时的电力负荷数据作为输入，预测第n+1个小时的电力负荷数据作为输出。

```python
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
n_vars = 1 if type(data) is list else data.shape[1]
df = pd.DataFrame(data)
cols, names = list(), list()
# input sequence (t-n, ... t-1)
for i in range(n_in, 0, -1):
cols.append(df.shift(i))
names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
# forecast sequence (t, t+1, ... t+n)
for i in range(0, n_out):
cols.append(df.shift(-i))
if i == 0:
names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
else:
names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
# put it all together
agg = pd.concat(cols, axis=1)
agg.columns = names
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg
```

在转换为监督学习问题后，我们需要将数据集分为训练集和测试集。

```python
n_hours = 3 # 前n个小时作为输入
n_features = 1 # 电力负荷数据
reframed = series_to_supervised(scaled, n_hours, 1)
values = reframed.values

n_train_hours = 365*24 # 前365天作为训练集
train = values[:n_train_hours, :]
test = values[n_train_hours:, :]

train_X, train_y = train[:, :-1], train[:, -1]
test_X, test_y = test[:, :-1], test[:, -1]

train_X = train_X.reshape((train_X.shape[0], n_hours, n_features))
test_X = test_X.reshape((test_X.shape[0], n_hours, n_features))
```

接下来，我们可以构建LSTM模型。在这里，我们将使用一个简单的两层LSTM网络，其中第一层有50个神经元，第二层有1个输出神经元。由于LSTM网络对于长期记忆非常擅长，因此它特别适用于时序数据的预测问题。

```python
model = Sequential()
model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
```

接着，我们需要定义PSO算法辅助LSTM网络训练的函数。

```python
class Particle:
def __init__(self, network_params):
self.position = network_params
self.velocity = []
self.best_position = []
self.error = -1
self.best_error = -1

def set_position(self, pos):
self.position = pos

def set_velocity(self, vel):
self.velocity = vel

def set_error(self, err):
self.error = err

def get_position(self):
return self.position

def get_velocity(self):
return self.velocity

def get_error(self):
return self.error

def get_best_position(self):
return self.best_position

def get_best_error(self):
return self.best_error

def evaluate(self, network, X, y):
# Set network weights
model.set_weights(network)

# Use Keras to train the model
fit_history = model.fit(X, y, epochs=100, batch_size=72, verbose=0, shuffle=False)

# Get the MAE error on the validation set
error = fit_history.history['loss'][-1]

# Update particle's best position and error
if self.error == -1 or error < self.best_error:
self.best_position = self.position
self.best_error = error

# Update global best position and error
if self.best_error < GLOBAL_BEST_ERROR or GLOBAL_BEST_ERROR == -1:
global BEST_POSITION
BEST_POSITION = self.best_position
global GLOBAL_BEST_ERROR
GLOBAL_BEST_ERROR = self.best_error

def update_velocity(self, best_global_pos):
w = 0.5 # inertia weight
c1 = 1 # cognitive weight
c2 = 2 # social weight

for i in range(len(self.velocity)):
r1 = np.random.uniform(0, 1)
r2 = np.random.uniform(0, 1)

cognitive_velocity = c1 * r1 * (self.best_position[i] - self.position[i])
social_velocity = c2 * r2 * (best_global_pos[i] - self.position[i])
self.velocity[i] = w * self.velocity[i] + cognitive_velocity + social_velocity

def update_position(self):
for i in range(len(self.position)):
self.position[i] = self.position[i] + self.velocity[i]
```

在定义好辅助函数后，我们可以使用PSO算法进行LSTM网络训练。

```python
# Define PSO hyperparameters
NUM_PARTICLES = 10
MAX_ITERATIONS = 20
GLOBAL_BEST_ERROR = -1
BEST_POSITION = []

# Initialize particles and global best position
SWARM = [Particle(model.get_weights()) for i in range(NUM_PARTICLES)]
BEST_POSITION = SWARM[0].get_position()

# Train the LSTM network using PSO
for i in range(MAX_ITERATIONS):
for j in range(NUM_PARTICLES):
# Evaluate particle
SWARM[j].evaluate(SWARM[j].get_position(), train_X, train_y)

# Update particle velocity and position
SWARM[j].update_velocity(BEST_POSITION)
SWARM[j].update_position()

# Update global best position
BEST_POSITION = SWARM[0].get_best_position()
GLOBAL_BEST_ERROR = SWARM[0].get_best_error()
for j in range(NUM_PARTICLES):
if SWARM[j].get_best_error() < GLOBAL_BEST_ERROR:
BEST_POSITION = SWARM[j].get_best_position()
GLOBAL_BEST_ERROR = SWARM[j].get_best_error()
```

最后，我们可以使用训练好的LSTM网络对电力负荷进行预测，并评估预测结果的准确性。

```python
# Make predictions using the test set
yhat = model.predict(test_X)

# Invert the predictions to their original scale
test_X = test_X.reshape((test_X.shape[0], n_hours*n_features))
inv_yhat = np.concatenate((yhat, test_X[:, -(n_features-1):]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:,0]

# Invert the actual values to their original scale
test_y = test_y.reshape((len(test_y), 1))
inv_y = np.concatenate((test_y, test_X[:, -(n_features-1):]), axis=1)
inv_y = scaler.inverse_transform(inv_y)
inv_y = inv_y[:,0]

# Evaluate model performance
from sklearn.metrics import mean_squared_error, mean_absolute_error
mse = mean_squared_error(inv_y, inv_yhat)
mae = mean_absolute_error(inv_y, inv_yhat)
print('Test MSE: %.3f' % mse)
print('Test MAE: %.3f' % mae)
```

希望这份代码可以帮助您实现基于PSO优化LSTM网络的电力负荷预测。如果您有任何问题或疑问，请随时与我联系。