1. 传统统计预测方法

时间序列分析

# ARIMA模型示例
from statsmodels.tsa.arima.model import ARIMA
import pandas as pd

# 能源负荷预测
def arima_forecast(data, order=(1,1,1)):
    model = ARIMA(data, order=order)
    fitted_model = model.fit()
    forecast = fitted_model.forecast(steps=24)  # 预测未来24小时
    return forecast

# 季节性分解
from statsmodels.tsa.seasonal import seasonal_decompose
decomposition = seasonal_decompose(energy_data, model='additive', period=24)

回归分析

# 多元线性回归
from sklearn.linear_model import LinearRegression
import numpy as np

# 特征：温度、湿度、风速、历史负荷
X = np.array([[temp, humidity, wind_speed, lag_load] for ...])
y = energy_demand

model = LinearRegression()
model.fit(X, y)
predictions = model.predict(X_test)

2. 机器学习预测方法

支持向量机（SVM）

from sklearn.svm import SVR
from sklearn.preprocessing import StandardScaler

# 数据标准化
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# SVM回归
svr_model = SVR(kernel='rbf', C=100, gamma=0.1)
svr_model.fit(X_scaled, y)
predictions = svr_model.predict(X_test_scaled)

随机森林

from sklearn.ensemble import RandomForestRegressor

# 随机森林预测
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X, y)

# 特征重要性分析
feature_importance = rf_model.feature_importances_
predictions = rf_model.predict(X_test)

3. 深度学习预测方法

LSTM循环神经网络

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout

def create_lstm_model(input_shape):
    model = Sequential([
        LSTM(50, return_sequences=True, input_shape=input_shape),
        Dropout(0.2),
        LSTM(50, return_sequences=True),
        Dropout(0.2),
        LSTM(50),
        Dropout(0.2),
        Dense(25),
        Dense(1)
    ])
    
    model.compile(optimizer='adam', loss='mean_squared_error')
    return model

# 训练模型
model = create_lstm_model((60, 1))  # 60个时间步
model.fit(X_train, y_train, epochs=100, batch_size=32)

CNN-LSTM混合模型

from tensorflow.keras.layers import Conv1D, MaxPooling1D

def create_cnn_lstm_model(input_shape):
    model = Sequential([
        Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=input_shape),
        MaxPooling1D(pool_size=2),
        LSTM(50, return_sequences=True),
        LSTM(50),
        Dense(50),
        Dense(1)
    ])
    
    model.compile(optimizer='adam', loss='mse')
    return model

4. Transformer注意力机制

能源需求预测的Transformer

import torch
import torch.nn as nn
import torch.nn.functional as F

class EnergyTransformer(nn.Module):
    def __init__(self, input_dim, d_model, nhead, num_layers):
        super(EnergyTransformer, self).__init__()
        self.input_projection = nn.Linear(input_dim, d_model)
        self.positional_encoding = PositionalEncoding(d_model)
        
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model, 
            nhead=nhead,
            dim_feedforward=512,
            dropout=0.1
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
        self.output_layer = nn.Linear(d_model, 1)
    
    def forward(self, x):
        x = self.input_projection(x)
        x = self.positional_encoding(x)
        x = self.transformer(x)
        x = self.output_layer(x[-1])  # 取最后一个时间步
        return x

# 训练示例
model = EnergyTransformer(input_dim=5, d_model=128, nhead=8, num_layers=6)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

5. 大模型预测方法

基于LLM的时间序列预测

# 使用预训练语言模型进行时间序列预测
from transformers import GPT2LMHeadModel, GPT2Tokenizer

class LLMEnergyPredictor:
    def __init__(self, model_name='gpt2'):
        self.tokenizer = GPT2Tokenizer.from_pretrained(model_name)
        self.model = GPT2LMHeadModel.from_pretrained(model_name)
    
    def time_series_to_text(self, ts_data):
        # 将时间序列转换为文本格式
        text = "Energy consumption pattern: "
        for i, value in enumerate(ts_data):
            text += f"Hour {i}: {value:.2f}kWh, "
        text += "Next hour prediction:"
        return text
    
    def predict(self, historical_data):
        input_text = self.time_series_to_text(historical_data)
        inputs = self.tokenizer.encode(input_text, return_tensors='pt')
        
        with torch.no_grad():
            outputs = self.model.generate(
                inputs, 
                max_length=inputs.shape[1] + 10,
                temperature=0.7,
                do_sample=True
            )
        
        prediction_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return self.extract_prediction(prediction_text)

6. 多模态预测方法

融合多种数据源的预测

import torch.nn as nn
from torchvision import models

class MultimodalEnergyPredictor(nn.Module):
    def __init__(self):
        super(MultimodalEnergyPredictor, self).__init__()
        
        # 时间序列分支
        self.ts_lstm = nn.LSTM(input_size=1, hidden_size=64, num_layers=2, batch_first=True)
        
        # 天气图像分支（卫星图像）
        self.image_cnn = models.resnet18(pretrained=True)
        self.image_cnn.fc = nn.Linear(512, 64)
        
        # 文本特征分支（天气描述、新闻等）
        self.text_embedding = nn.Embedding(vocab_size=10000, embedding_dim=64)
        self.text_lstm = nn.LSTM(64, 64, batch_first=True)
        
        # 融合层
        self.fusion = nn.Linear(64 * 3, 128)
        self.predictor = nn.Linear(128, 24)  # 预测未来24小时
    
    def forward(self, ts_data, image_data, text_data):
        # 时间序列特征
        ts_out, _ = self.ts_lstm(ts_data)
        ts_features = ts_out[:, -1, :]  # 取最后时间步
        
        # 图像特征
        image_features = self.image_cnn(image_data)
        
        # 文本特征
        text_embedded = self.text_embedding(text_data)
        text_out, _ = self.text_lstm(text_embedded)
        text_features = text_out[:, -1, :]
        
        # 特征融合
        combined = torch.cat([ts_features, image_features, text_features], dim=1)
        fused = F.relu(self.fusion(combined))
        prediction = self.predictor(fused)
        
        return prediction

7. 实时预测系统架构

流式数据处理

import asyncio
from kafka import KafkaConsumer
import json

class RealTimePredictor:
    def __init__(self, model, update_interval=300):  # 5分钟更新
        self.model = model
        self.update_interval = update_interval
        self.data_buffer = []
        
    async def consume_data(self):
        consumer = KafkaConsumer(
            'energy_data',
            bootstrap_servers=['localhost:9092'],
            value_deserializer=lambda x: json.loads(x.decode('utf-8'))
        )
        
        for message in consumer:
            data = message.value
            self.data_buffer.append(data)
            
            if len(self.data_buffer) >= 60:  # 60个数据点
                prediction = await self.predict()
                await self.publish_prediction(prediction)
                
    async def predict(self):
        # 使用缓冲区数据进行预测
        input_data = torch.tensor(self.data_buffer[-60:]).unsqueeze(0)
        with torch.no_grad():
            prediction = self.model(input_data)
        return prediction.item()
    
    async def publish_prediction(self, prediction):
        # 发布预测结果
        print(f"Predicted energy demand: {prediction:.2f} kWh")

8. 预测模型评估

性能指标计算

from sklearn.metrics import mean_absolute_error, mean_squared_error
import numpy as np

def evaluate_predictions(y_true, y_pred):
    """评估预测性能"""
    mae = mean_absolute_error(y_true, y_pred)
    mse = mean_squared_error(y_true, y_pred)
    rmse = np.sqrt(mse)
    
    # 平均绝对百分比误差
    mape = np.mean(np.abs((y_true - y_pred) / y_true)) * 100
    
    # 对称平均绝对百分比误差
    smape = np.mean(2 * np.abs(y_true - y_pred) / (np.abs(y_true) + np.abs(y_pred))) * 100
    
    return {
        'MAE': mae,
        'MSE': mse,
        'RMSE': rmse,
        'MAPE': mape,
        'SMAPE': smape
    }

# 可视化预测结果
import matplotlib.pyplot as plt

def plot_predictions(y_true, y_pred, title="Energy Demand Prediction"):
    plt.figure(figsize=(12, 6))
    plt.plot(y_true, label='Actual', color='blue')
    plt.plot(y_pred, label='Predicted', color='red', alpha=0.7)
    plt.title(title)
    plt.xlabel('Time (hours)')
    plt.ylabel('Energy Demand (kWh)')
    plt.legend()
    plt.grid(True)
    plt.show()

 预测方法选择指南

短期预测（1-24小时）

• 推荐：LSTM、Transformer、CNN-LSTM
• 特点：高精度，适合实时调度

中期预测（1-7天）

• 推荐：Random Forest、SVR、多模态融合
• 特点：平衡精度和计算复杂度

长期预测（1周-1年）

• 推荐：ARIMA、Prophet、大模型
• 特点：捕捉季节性和趋势

不确定性量化

# 贝叶斯神经网络示例
import torch.distributions as dist

class BayesianPredictor(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size * 2)  # 均值和方差
    
    def forward(self, x):
        x = F.relu(self.fc1(x))
        params = self.fc2(x)
        
        mean = params[:, :output_size]
        log_var = params[:, output_size:]
        std = torch.exp(0.5 * log_var)
        
        return mean, std
    
    def sample_prediction(self, x, n_samples=100):
        mean, std = self.forward(x)
        distribution = dist.Normal(mean, std)
        samples = distribution.sample((n_samples,))
        return samples