### 基于气象因素的电力负荷预测模型 Python 实现 为了构建基于气象因素的电力负荷预测模型，可以采用多种机器学习算法和技术栈。这里展示一种利用 LSTM（长短期记忆网络）的方法，该方法特别适合处理具有时间依赖性的序列数据。 #### 导入必要的库 首先导入所需的库： ```python import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM, Dropout import matplotlib.pyplot as plt ``` #### 加载并预处理数据 假设有一个 CSV 文件 `electricity_load.csv` 和另一个文件 `weather_data.csv` 分别存储了历史电能消耗量以及对应的天气状况信息。这些数据需要被加载进来，并做适当清洗和转换以便后续建模使用。 ```python # Load datasets load_df = pd.read_csv('electricity_load.csv') weather_df = pd.read_csv('weather_data.csv') # Merge two dataframes based on date column merged_df = pd.merge(load_df, weather_df, how='inner', on=['date']) # Convert 'date' to datetime format and set it as index merged_df['date'] = pd.to_datetime(merged_df['date']) merged_df.set_index('date', inplace=True) # Select relevant columns for modeling selected_columns = ['temperature', 'humidity', 'wind_speed', 'power_consumption'] data = merged_df[selected_columns] # Normalize the dataset using MinMax Scaler scaler = MinMaxScaler(feature_range=(0, 1)) scaled_data = scaler.fit_transform(data) ``` #### 创建训练集与测试集 将原始数据划分为训练样本和验证样本两部分，通常会保留最近一段时间的数据作为测试集以评估模型性能。 ```python train_size = int(len(scaled_data) * 0.8) test_size = len(scaled_data) - train_size train, test = scaled_data[:train_size], scaled_data[train_size:] def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), :] X.append(a) Y.append(dataset[i + look_back, -1]) return np.array(X), np.array(Y) look_back = 24 # Use previous day's data points X_train, y_train = create_dataset(train, look_back) X_test, y_test = create_dataset(test, look_back) # Reshape input to be [samples, time steps, features] X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], X_train.shape[2])) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], X_test.shape[2])) ``` #### 构建 LSTM 模型架构 定义一个简单的三层 LSTM 网络结构来进行回归分析任务。 ```python model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(look_back, X_train.shape[-1]))) model.add(Dropout(0.2)) model.add(LSTM(units=50, return_sequences=False)) model.add(Dropout(0.2)) model.add(Dense(units=1)) # Output layer with single neuron since we're predicting one value per sequence. model.compile(optimizer='adam', loss='mean_squared_error') history = model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.1) ``` #### 预测未来值并与真实情况对比 完成上述步骤之后就可以用训练好的模型对未来几天内的用电情况进行预测，并绘制图表直观比较实际测量结果同预期之间的差异程度。 ```python predicted_values = model.predict(X_test) plt.figure(figsize=(16,7)) plt.plot(y_test, color="blue", label="Actual Power Consumption") plt.plot(predicted_values, color="red", label="Predicted Power Consumption") plt.title("Power Consumption Prediction vs Actual Values") plt.xlabel("Time Index") plt.ylabel("Normalized Power Consumption Value") plt.legend(loc="upper left") plt.show() ``` 通过这种方式，不仅可以有效地捕捉到由温度、湿度等因素引起的负载变化趋势，而且还能进一步优化参数设置提高准确性[^1]。