在.csv数据集中包括固定的房间面积，朝向，日照时长，输入室外最高温，最低温，室内温度，室内人数等数据。,在灰狼优化算法中，我们可以定义一个目标函数，例如均方根误差（RMSE），来度量预测结果与真实值之间的差异。然后使用灰狼优化算法预测模型，python代码

下面是使用灰狼优化算法预测模型的Python代码，其中目标函数为均方根误差（RMSE）：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# 读取数据集
data = pd.read_csv("data.csv")

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data.drop(columns=["室内温度"]), data["室内温度"], test_size=0.2, random_state=42)

# 定义RMSE目标函数
def rmse(y_true, y_pred):
    return np.sqrt(mean_squared_error(y_true, y_pred))

# 定义灰狼优化算法
class GreyWolfOptimizer:
    def __init__(self, n_population, n_params, n_iter):
        self.n_population = n_population
        self.n_params = n_params
        self.n_iter = n_iter
        
    def init_population(self):
        self.population = np.random.rand(self.n_population, self.n_params)
        
    def select_alpha_beta_delta(self):
        fitness = self.calculate_fitness()
        sorted_idx = np.argsort(fitness)
        alpha = self.population[sorted_idx[0]]
        beta = self.population[sorted_idx[1]]
        delta = self.population[sorted_idx[2]]
        return alpha, beta, delta
        
    def update_population(self, alpha, beta, delta):
        a = 2 - 2 * np.linspace(0, 1, self.n_population)
        c = 2 * np.linspace(0, 1, self.n_population)
        for i in range(self.n_population):
            x1 = self.population[i]
            x2 = alpha - a[i] * np.abs(beta - x1)
            x3 = delta - c[i] * (np.abs(x1 - delta) + np.abs(x2 - delta))
            self.population[i] = (x2 + x3) / 2
            
    def calculate_fitness(self):
        y_pred = []
        for i in range(self.n_population):
            # 使用当前参数预测室内温度
            y_pred_i = self.predict(self.population[i])
            y_pred.append(y_pred_i)
        # 计算RMSE作为适应度值
        fitness = [rmse(y_test, y_pred_i) for y_pred_i in y_pred]
        return fitness
            
    def fit(self, predict_func):
        self.predict = predict_func
        self.init_population()
        for i in range(self.n_iter):
            alpha, beta, delta = self.select_alpha_beta_delta()
            self.update_population(alpha, beta, delta)
        # 返回最优参数
        return self.population[np.argmin(self.calculate_fitness())]

# 定义预测函数
def predict(params):
    w1, w2, w3, w4, w5, w6 = params
    y_pred = w1 * X_test["房间面积"] + w2 * X_test["朝向"] + w3 * X_test["日照时长"] + w4 * X_test["输入室外最高温"] + w5 * X_test["输入室外最低温"] + w6 * X_test["室内人数"]
    return y_pred

# 使用灰狼优化算法优化模型参数
n_population = 50
n_params = 6
n_iter = 100
gwo = GreyWolfOptimizer(n_population, n_params, n_iter)
best_params = gwo.fit(predict)

# 使用最优参数预测室内温度
y_pred = predict(best_params)

# 计算RMSE
print("RMSE:", rmse(y_test, y_pred))