.csv包含室外最高温，室外最低温，室内温度，室内人数，室外最高温，室外最低温，室内温度，室内人数为输入，室内适宜温度为输出，定义目标函数 = abs(室内温度 - 室内适宜温度) + (室内人数 - 人员适宜人数) + abs(室外温度 - 室内温度) + K * abs(室外温度 - 上一时刻室外温度)，采用灰狼优化算法输出最优的室内适宜温度，将室内适宜温度与实际室内温度的差值做比较确定电采暖的开启python

以下是用 Python 代码实现灰狼优化算法来求解最优的室内适宜温度：

import numpy as np
import pandas as pd

# 读取数据
data = pd.read_csv('data.csv')

# 定义目标函数
def objective(indoor_temp, comfortable_temp, indoor_people, comfortable_people, outdoor_temp, last_outdoor_temp, k):
    return abs(indoor_temp - comfortable_temp) + abs(indoor_people - comfortable_people) + abs(outdoor_temp - indoor_temp) + k * abs(outdoor_temp - last_outdoor_temp)

# 灰狼优化算法
def GWO(obj_func, lb, ub, dim, SearchAgents_no, Max_iter, indoor_temp, indoor_people, outdoor_temp, last_outdoor_temp, k):
    # 初始化搜索代理狼的位置
    Positions = np.zeros((SearchAgents_no, dim))
    for i in range(dim):
        Positions[:, i] = np.random.uniform(lb[i], ub[i], SearchAgents_no)
    
    # 初始化alpha、beta、delta狼的位置
    Alpha_pos = np.zeros(dim)
    Alpha_score = float("inf")
    
    Beta_pos = np.zeros(dim)
    Beta_score = float("inf")
    
    Delta_pos = np.zeros(dim)
    Delta_score = float("inf")
    
    # 迭代优化过程
    for l in range(0, Max_iter):
        for i in range(0, SearchAgents_no):
            # 更新每个狼的位置
            for j in range(dim):
                r1 = np.random.rand()
                r2 = np.random.rand()
                
                A1 = 2 * r1 - 1
                C1 = 2 * r2
                
                D_alpha = abs(C1 * Alpha_pos[j] - Positions[i, j])
                X1 = Alpha_pos[j] - A1 * D_alpha
                
                r1 = np.random.rand()
                r2 = np.random.rand()
                
                A2 = 2 * r1 - 1
                C2 = 2 * r2
                
                D_beta = abs(C2 * Beta_pos[j] - Positions[i, j])
                X2 = Beta_pos[j] - A2 * D_beta
                
                r1 = np.random.rand()
                r2 = np.random.rand()
                
                A3 = 2 * r1 - 1
                C3 = 2 * r2
                
                D_delta = abs(C3 * Delta_pos[j] - Positions[i, j])
                X3 = Delta_pos[j] - A3 * D_delta
                
                Positions[i, j] = (X1 + X2 + X3) / 3
        
        # 计算每个狼的适应度值
        for i in range(0, SearchAgents_no):
            indoor_temp = Positions[i, 0]
            comfortable_temp = Positions[i, 1]
            indoor_people = Positions[i, 2]
            comfortable_people = Positions[i, 3]
            
            # 计算室内适宜温度对应的目标函数值
            fitness = obj_func(indoor_temp, comfortable_temp, indoor_people, comfortable_people, outdoor_temp, last_outdoor_temp, k)
            
            # 更新alpha、beta、delta狼的位置
            if fitness < Alpha_score:
                Delta_score = Beta_score
                Delta_pos = Beta_pos.copy()
                Beta_score = Alpha_score
                Beta_pos = Alpha_pos.copy()
                Alpha_score = fitness
                Alpha_pos = Positions[i, :].copy()
            
            if fitness > Alpha_score and fitness < Beta_score:
                Delta_score = Beta_score
                Delta_pos = Beta_pos.copy()
                Beta_score = fitness
                Beta_pos = Positions[i, :].copy()
            
            if fitness > Alpha_score and fitness > Beta_score and fitness < Delta_score:
                Delta_score = fitness
                Delta_pos = Positions[i, :].copy()
        
        # 输出每次迭代的最优值
        print("Iteration:", l, "Best fitness:", Alpha_score, "Best position:", Alpha_pos)
    
    return Alpha_pos

# 设置灰狼优化算法的参数
SearchAgents_no = 30  # 搜索代理狼的数量
Max_iter = 100  # 最大迭代次数
dim = 2  # 变量维度
lb = [data['indoor_temp'].min(), data['indoor_people'].min()]  # 变量下界
ub = [data['indoor_temp'].max(), data['indoor_people'].max()]  # 变量上界
indoor_temp = data['indoor_temp'].values  # 室内温度
indoor_people = data['indoor_people'].values  # 室内人数
comfortable_people = 20  # 人员适宜人数
outdoor_temp = data['outdoor_temp'].values  # 室外温度
last_outdoor_temp = data['outdoor_temp'].shift(1).fillna(data['outdoor_temp'].iloc[0]).values  # 上一时刻室外温度
k = 0.1  # 自定义参数

# 调用灰狼优化算法求解最优的室内适宜温度
comfortable_temp = GWO(objective, lb, ub, dim, SearchAgents_no, Max_iter, indoor_temp, indoor_people, outdoor_temp, last_outdoor_temp, k)[1]

# 计算室内适宜温度与实际室内温度的差值
temp_diff = comfortable_temp - data['indoor_temp'].mean()

# 根据差值确定电采暖的开启
if temp_diff > 0:
    print("需要开启电采暖，提高室内温度。")
else:
    print("不需要开启电采暖，室内温度已经适宜。")

其中，我们使用 pandas 库读取包含室内最高温、室内最低温、室内温度、室内人数、室外最高温、室外最低温、室内温度、室内人数的 csv 文件，并使用灰狼优化算法求解最优的室内适宜温度。最后，根据室内适宜温度与实际室内温度的差值确定电采暖的开启。