目录摘要一、能耗监控概述1.1 能耗监控架构1.2 能耗类型1.3 监控指标二、能耗数据采集2.1 能耗数据表2.2 分布式存储2.3 数据采集接口三、实时统计3.1 实时功率统计3.2 累计能耗统计3.3 单位能耗计算四、能耗分析4.1 能耗分布分析4.2 能耗趋势分析4.3 能耗对比分析五、能耗预测5.1 简单预测5.2 趋势预测六、可视化展示6.1 能耗大屏数据6.2 能耗排名6.3 能耗曲线七、节能优化7.1 能耗异常检测7.2 节能建议八、实战案例7.1 完整能耗监控系统八、总结参考资料摘要本文深入讲解DolphinDB能耗实时监控技术。从能耗数据采集到实时统计从能耗分析到可视化展示从能耗预测到节能优化全面介绍能耗监控的核心方法。通过丰富的代码示例帮助读者掌握能耗数据可视化的核心技能。一、能耗监控概述1.1 能耗监控架构能耗监控架构电表/气表数据采集DolphinDB实时统计可视化展示1.2 能耗类型类型单位说明电力kWh设备用电燃气m³燃气消耗蒸汽t蒸汽消耗水m³用水量1.3 监控指标指标说明实时功率当前功率累计能耗累计消耗单位能耗单位产品能耗能耗成本能耗费用二、能耗数据采集2.1 能耗数据表//能耗数据表 share streamTable(100000:0,meter_iddevice_idtimestamppowervoltagecurrentenergy,[SYMBOL,SYMBOL,TIMESTAMP,DOUBLE,DOUBLE,DOUBLE,DOUBLE])asenergy_stream//启用持久化 enableTablePersistence(energy_stream,true,true,1000000)2.2 分布式存储//创建分布式表 dbdatabase(dfs://energy_db,VALUE,1..100)schematable(1:0,meter_iddevice_idtimestamppowervoltagecurrentenergy,[SYMBOL,SYMBOL,TIMESTAMP,DOUBLE,DOUBLE,DOUBLE,DOUBLE])db.createPartitionedTable(schema,energy_data,device_id)//订阅写入 subscribeTable(,energy_stream,persist,-1,def(msg){loadTable(dfs://energy_db,energy_data).append!(msg)},10000,5000)2.3 数据采集接口//能耗数据上报接口defreportEnergy(meterId,deviceId,power,voltage,current,energy){insert into energy_stream values(meterId,deviceId,now(),power,voltage,current,energy)}三、实时统计3.1 实时功率统计//实时功率聚合 share table(1:0,time_windowdevice_idavg_powermax_powermin_power,[TIMESTAMP,SYMBOL,DOUBLE,DOUBLE,DOUBLE])aspower_agg//功率聚合引擎 powerEnginecreateTimeSeriesEngine(power_engine,60000,[avg(power)asavg_power,max(power)asmax_power,min(power)asmin_power],power_agg,timestamp,device_id)subscribeTable(,energy_stream,power_agg,-1,powerEngine,true)3.2 累计能耗统计//累计能耗表 share table(1:0,device_idtotal_energyupdate_time,[SYMBOL,DOUBLE,TIMESTAMP])asenergy_total//累计计算defcalculateTotalEnergy(deviceId){dataselect last(energy)aslast_energyfromenergy_stream where device_iddeviceIdif(data.rows()0){update energy_totalsettotal_energydata.last_energy[0],update_timenow()where device_iddeviceId}}3.3 单位能耗计算//单位能耗计算defcalculateUnitEnergy(deviceId,startTime,endTime){//获取能耗 energyselectsum(energy)astotal_energyfromenergy_stream where device_iddeviceIdandtimestamp between startTimeandendTime//获取产量 productionselect count(*)astotal_productionfromproduction_stream where device_iddeviceIdandtimestamp between startTimeandendTimeif(production.total_production[0]0){return0.0}returnenergy.total_energy[0]/production.total_production[0]}四、能耗分析4.1 能耗分布分析//能耗分布defgetEnergyDistribution(startTime,endTime){returnselect device_id,sum(energy)astotal_energy,sum(energy)*100.0/(selectsum(energy)fromenergy_stream where timestamp between startTimeandendTime)aspercentagefromenergy_stream where timestamp between startTimeandendTime group by device_id order by total_energy desc}4.2 能耗趋势分析//能耗趋势defgetEnergyTrend(deviceId,startTime,endTime,interval3600000){returnselect bar(timestamp,interval)astime_window,sum(energy)astotal_energy,avg(power)asavg_powerfromenergy_stream where device_iddeviceIdandtimestamp between startTimeandendTime group by bar(timestamp,interval)}4.3 能耗对比分析//同比对比defcompareYoY(deviceId){nownow()currentgetEnergyTotal(deviceId,now-86400000,now)lastYeargetEnergyTotal(deviceId,now-365*86400000,now-364*86400000)returndict(STRING,ANY,[[current,current],[lastYear,lastYear],[change,(current-lastYear)*100.0/lastYear]])}//环比对比defcompareMoM(deviceId){nownow()currentgetEnergyTotal(deviceId,now-86400000,now)lastMonthgetEnergyTotal(deviceId,now-60*86400000,now-59*86400000)returndict(STRING,ANY,[[current,current],[lastMonth,lastMonth],[change,(current-lastMonth)*100.0/lastMonth]])}五、能耗预测5.1 简单预测//移动平均预测defpredictEnergy(deviceId,periods7){dataselectsum(energy)asdaily_energyfromenergy_stream where device_iddeviceId group by date(timestamp)order by date(timestamp)desc limit periodsif(data.rows()0){return0.0}returnavg(data.daily_energy)}5.2 趋势预测//线性趋势预测defpredictEnergyTrend(deviceId,futureDays7){dataselectsum(energy)asdaily_energyfromenergy_stream where device_iddeviceId group by date(timestamp)order by date(timestamp)if(data.rows()2){return0.0}//简单线性回归 ndata.rows()x1..n ydata.daily_energy sumXsum(x)sumYsum(y)sumXYsum(x*y)sumX2sum(x*x)slope(n*sumXY-sumX*sumY)/(n*sumX2-sumX*sumX)intercept(sumY-slope*sumX)/nreturninterceptslope*(nfutureDays)}六、可视化展示6.1 能耗大屏数据//能耗大屏defgetEnergyDashboard(){nownow()returndict(STRING,ANY,[[totalEnergy,getTotalEnergy(now-86400000,now)],[avgPower,getAvgPower(now-3600000,now)],[peakPower,getPeakPower(now-86400000,now)],[unitEnergy,getUnitEnergy(now-86400000,now)],[energyCost,getEnergyCost(now-86400000,now)]])}defgetTotalEnergy(startTime,endTime){returnexecsum(energy)fromenergy_stream where timestamp between startTimeandendTime}defgetAvgPower(startTime,endTime){returnexecavg(power)fromenergy_stream where timestamp between startTimeandendTime}defgetPeakPower(startTime,endTime){returnexecmax(power)fromenergy_stream where timestamp between startTimeandendTime}6.2 能耗排名//能耗排名defgetEnergyRanking(limit10){nownow()returnselect device_id,sum(energy)astotal_energy,rank()over order bysum(energy)descasrankfromenergy_stream where timestampnow-86400000group by device_id limit limit}6.3 能耗曲线//能耗曲线数据defgetEnergyCurve(deviceId,startTime,endTime){returnselect timestampastime,power,energyfromenergy_stream where device_iddeviceIdandtimestamp between startTimeandendTime order by timestamp}七、节能优化7.1 能耗异常检测//能耗异常检测defdetectEnergyAnomaly(deviceId){dataselect avg(power)asavg_powerfromenergy_stream where device_iddeviceIdandtimestampnow()-86400000avgPowerdata.avg_power[0]stdPowerexecstd(power)fromenergy_stream where device_iddeviceIdandtimestampnow()-86400000currentPowerexeclast(power)fromenergy_stream where device_iddeviceIdif(abs(currentPower-avgPower)3*stdPower){returntrue}returnfalse}7.2 节能建议//节能建议defgenerateEnergyAdvice(deviceId){advicearray(STRING,0)//检查峰谷用电 peakUsagegetPeakUsage(deviceId)valleyUsagegetValleyUsage(deviceId)if(peakUsagevalleyUsage*1.5){advice.append!(建议将部分生产转移到谷电时段)}//检查设备效率 unitEnergycalculateUnitEnergy(deviceId,now()-86400000,now())targetEnergygetTargetEnergy(deviceId)if(unitEnergytargetEnergy*1.2){advice.append!(设备能耗偏高建议检查设备运行状态)}returnadvice}八、实战案例7.1 完整能耗监控系统//能耗实时监控系统//1.创建数据表 share streamTable(100000:0,meter_iddevice_idtimestamppowervoltagecurrentenergy,[SYMBOL,SYMBOL,TIMESTAMP,DOUBLE,DOUBLE,DOUBLE,DOUBLE])asenergy_stream enableTablePersistence(energy_stream,true,true,1000000)//2.创建分布式表 dbdatabase(dfs://energy_db,VALUE,1..100)schematable(1:0,meter_iddevice_idtimestamppowervoltagecurrentenergy,[SYMBOL,SYMBOL,TIMESTAMP,DOUBLE,DOUBLE,DOUBLE,DOUBLE])db.createPartitionedTable(schema,energy_data,device_id)//3.订阅写入 subscribeTable(,energy_stream,persist,-1,def(msg){loadTable(dfs://energy_db,energy_data).append!(msg)},10000,5000)//4.功率聚合 share table(1:0,time_windowdevice_idavg_powermax_powermin_power,[TIMESTAMP,SYMBOL,DOUBLE,DOUBLE,DOUBLE])aspower_agg powerEnginecreateTimeSeriesEngine(power_engine,60000,[avg(power)asavg_power,max(power)asmax_power,min(power)asmin_power],power_agg,timestamp,device_id)subscribeTable(,energy_stream,power_agg,-1,powerEngine,true)//5.模拟数据defgenerateMockEnergy(){while(true){datatable(Mstring(rand(100,10))asmeter_id,take(1..10,10)asdevice_id,take(now(),10)astimestamp,rand(50.0..100.0,10)aspower,rand(220.0..240.0,10)asvoltage,rand(10.0..50.0,10)ascurrent,rand(1000.0..2000.0,10)asenergy)energy_stream.append!(data)sleep(5000)}}submitJob(mock_energy,模拟能耗数据,generateMockEnergy)//6.能耗看板接口defgetEnergyDashboard(){nownow()returnselectsum(energy)astotal_energy,avg(power)asavg_power,max(power)asmax_powerfromenergy_stream where timestampnow-86400000}addFunctionView(getEnergyDashboard)print(能耗实时监控系统启动完成)八、总结本文详细介绍了DolphinDB能耗实时监控数据采集能耗数据表、分布式存储实时统计功率统计、累计能耗、单位能耗能耗分析分布分析、趋势分析、对比分析能耗预测简单预测、趋势预测可视化展示能耗大屏、能耗排名、能耗曲线节能优化异常检测、节能建议思考题如何提高能耗预测的准确性如何设计有效的节能策略如何实现能耗成本优化参考资料DolphinDB时序数据处理DolphinDB聚合计算