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文章目录

一、前言
二、开发环境
三、系统界面展示
四、部分代码设计
五、系统视频
结语

一、前言

系统介绍
本系统是一个基于大数据技术的家庭能源消耗分析与可视化平台，采用Hadoop+Spark分布式架构处理大规模家庭用电数据。系统后端基于Django/Spring Boot版本实现，前端采用Vue+ElementUI+Echarts构建交互式可视化界面。系统核心功能包括家庭属性分析、温度影响分析、时间序列分析、聚类用户画像以及综合数据可视化大屏展示。通过Spark SQL进行高效数据处理，结合Pandas、NumPy进行深度统计分析，能够识别不同规模家庭的用电规律、温度对能耗的驱动效应、工作日与周末的用电差异、高峰时段负荷特征等关键信息。系统运用K-Means聚类算法将用户划分为节能型、常规型、高耗能型等群体，为电力部门制定差异化电价策略、负荷预测和节能政策提供数据支撑。整个平台具备实时数据处理能力，支持多维度能耗分析和动态可视化展示，为智慧能源管理提供技术解决方案。

选题背景
随着城市化进程加速和居民生活水平提升，家庭能源消耗呈现快速增长态势，电力负荷峰谷差异日益明显，给电网运行带来巨大压力。传统的能源管理方式主要依靠人工统计和简单报表分析，难以深入挖掘海量用电数据中蕴含的用户行为规律和能耗特征。电力企业迫切需要运用大数据技术对家庭能耗数据进行精细化分析，识别不同用户群体的用电模式，为实施阶梯电价、分时电价等差异化策略提供科学依据。同时，气候变化导致极端天气频发，空调等高耗能设备的普及使得温度因素对电力负荷的影响愈发显著，传统分析方法已无法满足复杂环境下的负荷预测需求。在此背景下，构建基于Hadoop、Spark等大数据技术的家庭能源消耗分析系统，通过机器学习算法挖掘用电数据的深层规律，对提升电网运行效率、促进节能减排具有重要价值。

选题意义
本课题的研究对电力行业数字化转型和智慧能源建设具有一定的实践价值。通过大数据分析技术，电力企业能够更准确地识别高耗能用户群体，制定针对性的节能引导措施，在一定程度上缓解电网峰值压力。系统提供的温度-能耗关联分析和时间序列预测功能，可为电力调度部门的负荷预测工作提供参考，有助于提高供电可靠性。用户行为聚类分析结果能够支持电力营销部门设计更加精准的电价套餐，促进电力资源的合理配置。从技术角度而言，本系统探索了Hadoop+Spark在能源数据处理中的应用模式，为同类大数据分析项目提供了可借鉴的技术方案。对于学术研究来说，该系统整合了数据挖掘、机器学习、可视化等多项技术，体现了跨学科融合的特点。虽然作为毕业设计项目，系统规模和复杂度有限，但通过实际的数据分析实践，加深了对大数据技术在能源领域应用的理解，为后续深入研究奠定了基础。

二、开发环境

大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
开发语言：Python+Java（两个版本都支持）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持）
前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery
详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
数据库：MySQL

三、系统界面展示

基于大数据的家庭能源消耗数据分析与可视化系统界面展示：

四、部分代码设计

项目实战-代码参考：

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, sum, count, when, desc, asc, expr, dayofweek, date_format
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import VectorAssembler
from sklearn.cluster import KMeans as SKKMeans
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
spark = SparkSession.builder.appName("EnergyConsumptionAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def household_attribute_analysis(request):df = spark.read.csv("household_energy_consumption.csv", header=True, inferSchema=True)df.createOrReplaceTempView("energy_data")household_size_analysis = spark.sql("""SELECT Household_Size, AVG(Energy_Consumption_kWh) as avg_consumption,AVG(Energy_Consumption_kWh / Household_Size) as per_capita_consumption,COUNT(*) as record_countFROM energy_data GROUP BY Household_Size ORDER BY Household_Size""").collect()ac_impact_analysis = spark.sql("""SELECT Has_AC,AVG(Energy_Consumption_kWh) as avg_consumption,COUNT(*) as household_count,AVG(Peak_Hours_Usage_kWh) as avg_peak_usageFROM energy_dataGROUP BY Has_AC""").collect()combined_analysis = spark.sql("""SELECT Household_Size, Has_AC,AVG(Energy_Consumption_kWh) as avg_consumption,AVG(Peak_Hours_Usage_kWh / Energy_Consumption_kWh * 100) as peak_ratio,COUNT(*) as record_countFROM energy_dataWHERE Energy_Consumption_kWh > 0GROUP BY Household_Size, Has_ACORDER BY Household_Size, Has_AC""").collect()ac_penetration_rate = spark.sql("""SELECT Household_Size,COUNT(*) as total_households,SUM(CASE WHEN Has_AC = 'Yes' THEN 1 ELSE 0 END) as ac_households,ROUND(SUM(CASE WHEN Has_AC = 'Yes' THEN 1 ELSE 0 END) * 100.0 / COUNT(*), 2) as ac_penetration_rateFROM energy_dataGROUP BY Household_SizeORDER BY Household_Size""").collect()size_data = [{"size": row.Household_Size, "avg_consumption": round(row.avg_consumption, 2), "per_capita": round(row.per_capita_consumption, 2), "count": row.record_count} for row in household_size_analysis]ac_data = [{"has_ac": row.Has_AC, "avg_consumption": round(row.avg_consumption, 2),"count": row.household_count, "avg_peak": round(row.avg_peak_usage, 2)}for row in ac_impact_analysis]combined_data = [{"size": row.Household_Size, "has_ac": row.Has_AC,"avg_consumption": round(row.avg_consumption, 2),"peak_ratio": round(row.peak_ratio, 2), "count": row.record_count}for row in combined_analysis]penetration_data = [{"size": row.Household_Size, "total": row.total_households,"ac_count": row.ac_households, "penetration_rate": row.ac_penetration_rate}for row in ac_penetration_rate]result = {"household_size_analysis": size_data, "ac_impact_analysis": ac_data,"combined_analysis": combined_data, "ac_penetration_analysis": penetration_data}return JsonResponse(result, safe=False)
def temperature_impact_analysis(request):df = spark.read.csv("household_energy_consumption.csv", header=True, inferSchema=True)df.createOrReplaceTempView("energy_data")temperature_correlation = spark.sql("""SELECT Date,AVG(Avg_Temperature_C) as daily_avg_temp,SUM(Energy_Consumption_kWh) as daily_total_consumption,AVG(Energy_Consumption_kWh) as daily_avg_consumption,COUNT(*) as household_countFROM energy_dataGROUP BY DateORDER BY Date""").collect()temp_ranges = spark.sql("""SELECT CASE WHEN Avg_Temperature_C < 15 THEN 'Low (<15°C)'WHEN Avg_Temperature_C BETWEEN 15 AND 22 THEN 'Comfortable (15-22°C)'ELSE 'High (>22°C)'END as temp_range,AVG(Energy_Consumption_kWh) as avg_consumption,COUNT(*) as record_countFROM energy_dataGROUP BY CASE WHEN Avg_Temperature_C < 15 THEN 'Low (<15°C)'WHEN Avg_Temperature_C BETWEEN 15 AND 22 THEN 'Comfortable (15-22°C)'ELSE 'High (>22°C)'ENDORDER BY avg_consumption DESC""").collect()ac_temp_impact = spark.sql("""SELECT Has_AC,CASE WHEN Avg_Temperature_C < 15 THEN 'Low'WHEN Avg_Temperature_C BETWEEN 15 AND 22 THEN 'Comfortable'ELSE 'High'END as temp_category,AVG(Energy_Consumption_kWh) as avg_consumption,COUNT(*) as record_countFROM energy_dataGROUP BY Has_AC, CASE WHEN Avg_Temperature_C < 15 THEN 'Low'WHEN Avg_Temperature_C BETWEEN 15 AND 22 THEN 'Comfortable'ELSE 'High'ENDORDER BY Has_AC, temp_category""").collect()high_temp_household_impact = spark.sql("""SELECT Household_Size,AVG(Energy_Consumption_kWh) as avg_consumption_high_temp,COUNT(*) as record_countFROM energy_dataWHERE Avg_Temperature_C > 22GROUP BY Household_SizeORDER BY Household_Size""").collect()correlation_data = [{"date": row.Date, "temp": round(row.daily_avg_temp, 1),"total_consumption": round(row.daily_total_consumption, 2),"avg_consumption": round(row.daily_avg_consumption, 2),"household_count": row.household_count}for row in temperature_correlation]range_data = [{"temp_range": row.temp_range, "avg_consumption": round(row.avg_consumption, 2),"count": row.record_count} for row in temp_ranges]ac_impact_data = [{"has_ac": row.Has_AC, "temp_category": row.temp_category,"avg_consumption": round(row.avg_consumption, 2), "count": row.record_count}for row in ac_temp_impact]high_temp_data = [{"household_size": row.Household_Size,"avg_consumption": round(row.avg_consumption_high_temp, 2),"count": row.record_count} for row in high_temp_household_impact]result = {"daily_correlation": correlation_data, "temperature_ranges": range_data,"ac_temperature_impact": ac_impact_data, "high_temp_household_analysis": high_temp_data}return JsonResponse(result, safe=False)
def user_clustering_analysis(request):df = spark.read.csv("household_energy_consumption.csv", header=True, inferSchema=True)df.createOrReplaceTempView("energy_data")household_features = spark.sql("""SELECT Household_ID,AVG(Energy_Consumption_kWh / Household_Size) as per_capita_consumption,AVG(Peak_Hours_Usage_kWh / Energy_Consumption_kWh * 100) as peak_usage_ratio,AVG(CASE WHEN Avg_Temperature_C > 22 THEN Energy_Consumption_kWh ELSE 0 END) as high_temp_consumption,AVG(Energy_Consumption_kWh) as avg_total_consumption,MAX(Household_Size) as household_size,MAX(CASE WHEN Has_AC = 'Yes' THEN 1 ELSE 0 END) as has_acFROM energy_dataWHERE Energy_Consumption_kWh > 0GROUP BY Household_ID""")pandas_df = household_features.toPandas()feature_columns = ['per_capita_consumption', 'peak_usage_ratio', 'high_temp_consumption']X = pandas_df[feature_columns].fillna(0)kmeans = SKKMeans(n_clusters=4, random_state=42, n_init=10)pandas_df['cluster'] = kmeans.fit_predict(X)cluster_centers = kmeans.cluster_centers_cluster_analysis = pandas_df.groupby('cluster').agg({'avg_total_consumption': 'mean','per_capita_consumption': 'mean','peak_usage_ratio': 'mean','high_temp_consumption': 'mean','household_size': 'mean','has_ac': 'mean','Household_ID': 'count'}).round(2)cluster_analysis['ac_penetration_rate'] = (cluster_analysis['has_ac'] * 100).round(1)cluster_labels = {0: '节能型用户', 1: '常规型用户', 2: '高耗能型用户', 3: '温度敏感型用户'}household_size_distribution = pandas_df.groupby(['cluster', 'household_size']).size().unstack(fill_value=0)cluster_profiles = []for cluster_id in range(4):cluster_data = cluster_analysis.loc[cluster_id]size_dist = household_size_distribution.loc[cluster_id].to_dict() if cluster_id in household_size_distribution.index else {}profile = {'cluster_id': cluster_id,'cluster_name': cluster_labels.get(cluster_id, f'群体{cluster_id}'),'avg_total_consumption': cluster_data['avg_total_consumption'],'per_capita_consumption': cluster_data['per_capita_consumption'],'peak_usage_ratio': cluster_data['peak_usage_ratio'],'high_temp_sensitivity': cluster_data['high_temp_consumption'],'avg_household_size': cluster_data['household_size'],'ac_penetration_rate': cluster_data['ac_penetration_rate'],'household_count': int(cluster_data['Household_ID']),'household_size_distribution': size_dist}cluster_profiles.append(profile)center_data = [{'cluster_id': i, 'center_features': center.tolist()} for i, center in enumerate(cluster_centers)]result = {'cluster_profiles': cluster_profiles, 'cluster_centers': center_data,'feature_names': feature_columns, 'total_households': len(pandas_df)}return JsonResponse(result, safe=False)