International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
AI-POWERED ENERGY MANAGEMENT SYSTEM FOR RESIDENTIAL AREA
Prof. J. Kalidass 1, S. Karthika2, S. Kiruthika 3, L.S. Shanu4
1 Assistant Professor, Department of the Computer Science and Engineering, Government College of Engineering Srirangam, Tamilnadu, India 2,3,4 Final year UG Student, Department of the Computer Science and Engineering, Government College of Engineering, Srirangam, Tamilnadu, India ***
ABSTRACT
Inthelastyears,thereisaneedforintelligentsystemstobe developed to make the electricity consumed in the homes optimized and rate wastage reduced. In this paper, we propose an AI-Powered Energy Management System (AIEMS), a novel system, which is computer vision and a deeplearning-basedsystem,designedforresidentialareas. The suggested system combines Convolutional Neural Networks (CNNs), real-time image processing, Internet of Things (IoT), and smart grid ideas to recognize a human presenceandappliancestatusbyclassificationfromimages. The system utilizes object detection frameworks such as YOLO and face recognition libraries to track the active appliancesand the presence ofoccupants,hence ensuring theappliancesareoffwhennotinuse.Italsocomeswithfire and smoke detection mechanism that provides real time alerts using email, sms or mobile notifications. Saving ApplianceSolutionisaninterfaceforvisualizingappliance activities, alerts, and recommendations for energy saving. Basedontheexperimentalresults,theproposedsolutionhas proven its effectiveness in minimizing the unnecessary energyconsumptionandtheimprovedmonitoring,safety, anddecisionprocessinthesmarthomes.[1]
KEYWORDS
Artificial Intelligence (AI), Energy Management System (EMS), Smart Home, Computer Vision, YOLO, Deep Learning, Human Detection, Appliance Monitoring, IoT, Fire Detection, Face Recognition, Real-Time Alerting, Energy Efficiency, Smart Grid, Dashboard Visualization.
1. INTRODUCTION
Thepressuretowardsgenerationofmore powertogether with the growth of smart devices as well as home automationhasledtotheneedforformulationofintelligent power management systems. Residential energy consumption plays a significant role in the total energy demandconsumptionandinefficientconsumptionpatterns end up wasting energy, increased carbon footprints and increasedbills.Traditionalformsofenergymonitoringhave alwaysusedmanualinputsorsensorsfordetectingenergyif thisisnotproperlyreceived,itmightnotbeabletocapture
the real-time human presence or appliance status hence leadingtopoorenergymanagement.
Smart homes stand to benefit greatly from Artificial Intelligence(AI)andComputerVisionthathavethepotential to change energy monitoring and control forever. These technology allows systems to make informed decisions basedontheanalysisofrealtimevisualandcontextualdata thus ensuring effective energy expenditure without compromisingthecomfortorsecurityoftheuser.Theroleof AIintoenergy.
managementsystemsenablesreal-timedetectionofhuman activities and appliance usage, as well as environmental change/conditions based on real-time video feeds and trained models. This paper introduces an AI-Powered Energy Management System for residential areas that are builtontheuseofimageclassification,real-timeobjectand facedetection,andIoT-basedalerts.Advanceddeeplearning models like YOLO for object detection and Media Pipe or Haar cascades for pose and face recognition are used for human presence and appliance status monitoring by the system.IncaseanapplianceisdeterminedON,butthereis nohumanpresence,thesystemgivesthealertsandhasrealtimenotificationsusingvariouschannelssuchasemail,SMS and mobile notifications. Apart from optimizing the consumptionofenergy,thesystemincludesfireandsmoke detection modules for enhanced home safety. Monitoring data,alerts,andanysystemdecisionareoutputthrougha web dashboard, which provides transparency as well as actionableinsightstousers.Inthispaper,thearchitecture, themethodology,algorithms,techniques,implementation, andprogrammingaswellastheperformancesareexplained, showingustheefficiencyofAItomanageresidentialenergy consumptionefficientlyandintelligently.[2][3]
2. LITERATURE SURVEY
Lotsofresearchworkshave focused on the integration of Artificial Intelligence and IoT in the energy management systemsinordertoenhanceefficiencyandautomation.The belowstudiesprovideafundamentalcomprehensionofthe existingmethodologiesandtheirdeficit,thatourproposed systemintendstoovercome.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
Kekre et al. (2017) designed a solar photovoltaic remote monitoringsystembasedonIoTwhichenablesitsusersto beabletomonitorandcontrolenergygenerationremotely. Although the work is centred on the potential of IoT for energysystems,itcentresonenergygenerationandnotthe dynamic,real-timeconsumptioncontrolwithinhomes.[1][3]
Tamilarasan et al. (2021) developed a special sensor for intracranial pressure monitoring, showing the usage of highly-precisesensingandcommunicationtechnologiesfor life-crictial applications. Even though they target medical systems, the principles of real-time monitoring and smart decisionarerelevantforenergymanagementsystems.[2]
Inotheroftherecentworks,thecomputervisionmethods have been applied to the occupancy detection in smart buildings.Suchsystemsgenerallyemploymotionsensorsor basic image processing techniques that are frequent false positivecausesandinsufficientinrespecttoadaptabilityto variouslightingconditionsorocclusions.Incontrast,deep learning-based methods such as the Convolutional Neural Networks(CNNs)provideeffectiveclassificationcapabilities, eventincomplicatedsettings.
Also, the YOLO (You Only Look Once) framework has becomepopularwiththeuseofreal-timeobjectdetection processesbecauseofitshighspeedandaccuracy.Theuseof thisisanimportantenhancementoverconventionalsensorbasedmodelsinsmarthomesystemstodetectappliances andhumans. Therearealsoseveralstudiesthathavelooked atalertmechanismsinhomeautomation.Systems,suchas email-basedalerts,SMS-basedalerts,etc.havealreadybeen implementedusingsuchservicesasSMTPorTwiliobutthe combinationwithintelligentdecisionlogichasnotbeenas explored.
Despitethesedevelopments,thereisstillalackofaholistic system that integrates the real time object detection and human-recognitiontechnologies,safetycontrol(forexample, firedetection),aswellasintelligentenergy-savingdecisionmakinginmanyexistingworks.OurproposedAI-Powered EnergyManagementSystem fillsthisgapbyincludingthe full stack solution, from detection to classification, alert notificationandvisualizationonthedashboard.[2][3]
3. METHODOLOGY
TheAI-PoweredEnergyManagementSystem(AI-EMS)that isproposedforaddressingtheenergymanagementissuein residentialsettingsisaimedtomonitorandcontrolenergy consumption intelligently through image detection, deep learning, and real time alerts. The system implements a modulararchitectureoffourmajorparts:Thenumberoneis ImageClassificationandModelTraining,thenthenumber twoisRealTimeImageDetection,thenumberthreeisAlert andNotificationSystemandthenumberfourisMonitoring andDashboardInterface.Thesemodulescooperativelymeet
theintelligentautomation,humanpresencedetection,and efficientappliancesmonitory.
A. Image Classification and Model Training Module
Thismodulestartswiththeformingofalabeleddatasetof different appliance images with different operation and occupancy scenarios (e.g., appliances ON with human presence, appliances ON without human presence, appliances OFF). The images are subjected to such preprocessing tasks as resizing, normalization, and augmentationtoenhancethegeneralizationofthemodel.
Then, a Convolutional Neural Network-based model is developed using such frameworks as TensorFlow or PyTorch. State-of-the-art architectures of Convolutional Neural Networks like MobileNet or ResNet are applied becauseoftheirefficiencyandaccuracy.Themodeltrainis toclassifyappliancesandidentifypresenceofhumanbased ontheimagefeature.AtrainedmodelinHierarchicalData format version 5 file format is generated for deployment intothereal-timedetectionmodule.
B. Real-Time Image Detection Module
Attheoperationalstage,anactivevideostreamisobtained withUSBorIPcamerasandprocessedframe-by-framewith thehelpofOpenCV.TheCNNmodeltrainedisthenusedon eachframetoidentifyactiveappliances.Atthesametime, thepresenceofhumansisdetectedwiththehelpofobject detection (e.g., YOLOv8) algorithms and face or pose recognitionones(e.g.,MediaPipeorHaarCascades).
Thedetectionprocessinvolves:
-Extractionoffeaturesfromthecurrentframe.
• Classifyingobjectsandpersonspresent.
• Deciding whether appliances are active and unattended.
Discrepanciesaredetectedinthesystem(e.g.anapplianceis ONwithnodetectedhumanpresence),thesystemismade readytomakeadecisionandnotify.
C. Alert and Notification System
Ifthesystemfindsaviolation(e.g.,energywastage)itsends amulti-channelalertmechanism.
• An alarm will generate locally through a GeneralpurposeInput/Outputbuzzersorspeaker.
• Notificationsaresentvia:
- EmailusingSMTPprotocol
- SMSusingTwilioAPI
- PushnotificationsviaPushbullet
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
MongoDB is applied in storing Alert logs, time stamped recordsandhistoryforappliancesactivity.Shorttimerdelay (grace period) is used to prevent false alarms, sustaining detectionintimebeforeraisingalerts.
Inaddition,themoduleisabletosupportintruderalertsby comparingdetectedfacesto a databaseofknownpersons using`face_recognition`.Unknownpeopleactivateanother categoryofwarningsandarerecordedassecuritythreats.
D. Monitoring and Dashboard Interface
A web application based on Flask gives a user-friendly interfacefor:
• Show real-timevideo framesinwhichobjectsare detectedandpersonsareidentified.
• Displayhowtheappliancesarefunctioningandthe tipsonhowtosaveenergy.
• Providealertlogsandthehistoricalinformationthat arestoredinMongoDB. Thedashboardinterfaceiswritten inHTML,CSS,andJavaScriptwithFlaskforliveupdatessuch thattheuserscanenjoyuninterrupteddatavisualization.It increases the transparency and enables users will be in a position to evaluate household energy consumption and systemactionsatanytime.
Thismodularandscalableapproachenablesthedeployment ofthesystemindifferentresidentialsettings,andachieves great flexibility, low intervention by human, and strong energymanagement.[3][4][7]
4. SYSTEM ARCHITECTURE
Proposed AI-Powered Energy Management System is structured in two primary and basic phases; the phase of trainingandthephaseoftesting(operational).Eachonehas severalinterconnectedmodulesthatoperateasateamtodo intelligent energy monitoring, detection, and alerting. The systemisorganizedasamodularsystemforscalabilityand maintainabilityaswellasreal-timeperformance.
System Architecture Diagram
A. Training Phase
Thetrainingphasereferstothepreparationandtrainingof deeplearningmodelsforobject,aswellashumanpresence detection.Thisisthephasewhichconsistsofthefollowing stages:
1. DataCollection:Aimagelabelleddatasetiscreated comprisingcommonhouseholdappliancesinvariousstates (ON/OFF) and the level of human engagement (presence/absence).
2. Preprocessing:Imagesareresized,normalized,and augmented to increase models’ robustness. Data augmentation techniques of like, rotation, flipping, and brightness adjustment enable the model to generalize on differentlightinganddifferentorientationscenarios.
3. SegmentationandFeatureExtraction:Keyfeatures areextractedoutoftheimagesfromconvolutional layers, and the feature maps are put through pooling and fully connectedlayersforclassification.
4. ModelTraining:MobileNet,ResNet,orYOLOCNN model is being trained with TensorFlow or PyTorch. The modeliscapableofrecognizingthepresenceofappliances andhumanpresencewithhighaccuracy.
5. ModelSaving:Thetrainedmodelisthenexportedto the file Hierarchical Data Format Version 5 for the deploymentinthereal-timeengineofinference.
B. Testing (Operational) Phase
Afterthevalidationprocessofthemodel,itispushedtothe real time detection pipeline. Comprising the components mentionedbelow,thisphaseincludes.
1. InputImageCapture:Videoframesareobtainedina continuouswaythroughcameras(USB/IP/webcam).These framesactasreal-timeinputforthetasksofdetection.
2. Preprocessing:Eachcapturedframeispreprocessed (resizedandnormalized)soastosuitthetrainedmodel.
3. Segmentation and Feature Extraction: System passes the frames through the deployed CNN to identify objects,faces,andpostures.
4. RecognitionandDecisionLogic:Thesystemcarries outseveraltasksinaparallelmanner.
• Identifies appliances with the help of object detectionmodels(YOLO).
• Identifies human presence with the help of pose/facedetection(MediaPipe,Haarcascades).
• Determinesappliancestatus interms ofoccupant status.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
Identifiesintruder-basedonknownfaceencodings.
5. Post-ProcessingandAlerts:Fromthedecisionlogic, if an appliance is on and only with the absence of an individual,thesystemwill:
• Activateslocalalarms.
• Sendsremotenotifications.•Storestheeventintoa NoSQLdatabase(MongoDB).
6. Fire/SmokeDetectionModule:AYOLO-basedmodel isusedtodetectvisualmanifestationoffireorsmoke.Ifitis identified,thentheframeissavedwithboundingboxes,and anemergencyalertissentthroughtheFlaskserver.
7. Dashboard Interface: A web dashboard raises the sourceofreal-timedataandvisualizationsthroughFlask.It displays:
• Livecamerafeedthatsupportsdetectionoverlays
• Appliancestatusandrecommendations
• Alertshistoryandsystemlogs
Concurrent task execution in the modular system design, achieved by usage of Python threading and `Thread Pool Executor`, provides continuous monitoring and instant responsiveness.[7][9][11]
5. ALOGRITHM
AI-PoweredEnergyManagementSystemutilizesanumberof algorithms that as a whole make accurate object identification, human detection, anomaly detection, and intelligentdecisionmakingpossible.Thesealgorithmsrange from deep learning models, image handling algorithms to real-time event processing logic. The main algorithms employedinthesystemaregivenbelow:
A. YOLOv8 – Real-Time Object Detection
YouOnlyLookOnce(YOLO) isa stateoftheart,real-time object detection algorithm. The most recent version, YOLOv8,providesbetteraccuracyofdetectionandinference speed. It keeps to processing an entire image in just once forward pass through the network and outputs bounding boxesandclasslabels.
• Application: For the detection of household appliances, identification of fire or smoke in the frames, YOLOv8isused.
• Advantages: o High frames-per-second (FPS) performance.
o Excellentcombinationofspeedandaccuracy.
o Canprocess various objectclassesatonego.
Steps:
1. Load YOLOv8 weights pre-trained using COCO dataset.
2. ApplyonlivevideoframeswithOpenCV.
3. Gettheboundingboxes,classnames,andconfidence scores.
4. Determine appliances, fire, and smoke as determinesbythedetectionresults.
B. CNN-Based Face Recognition
Thesystemreliesonthelibrary`face_recognition`,which worksonthebasesofdeepconvolutionalneuralnetworks (CNNs)tocodeandcomparefacialfeatures.Itisusedto:
• Identify familiar members of the family from picturesstored.
• Detectunknownfaces(possibleintruders).
• Empower location-specific energy decisions dependingonthepersonsinvolved.
Steps:
1. Loadknownfaceimages,calculateencodings.
2. Match encodings of faces occurring in live video againststoredencodingsoffaces.
3. Intruder alert should be set off if a foreign face is detected.
C.Human Presence Detection – MediaPipe & Haar Cascade
Aspartofimprovinghumanpresencedetection,the systemhas:
Media Pipe:Lightweightframeworkforhumanpose andlandmarksdetection.
HaarCascade:ClassicalmethodwiththeuseofViola Jonesmethodforfacedetection.
These tools can be used in complimenting YOLObasedobjectdetectionandaddingupaccuracywith variouslightingsandcameraangles.
Fire and Smoke Detection Algorithm
The fire detection algorithm employs YOLO with a custom trained model for the detection of flames patternsandsmokestructures.Itdetects: Brightreddish-orangemarkingsnormallyrelatedto fire.
Smokygrayareasthatarediffuseandirregular.
ResponseFlow:
Ifaframeisseentocontainfire/smoke,theimageis savedwithboundingboxes.
Fire alert is sent to the Flask backend and to be forwardedthroughpush/SMS/email.
Event Decision Logic & Notification Algorithm
The decision logic checks for conflict conditions:
WhenAppliance=ONANDHumanPresence= FALSE
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
→TriggerAlert
Toavoidfalsepositives:
Beforetriggeringthealert,timerbaseddelay isemployed.
Confirmations are compiled on sequential frames.
NotificationLogic:
Sendemailusing`smtplib`
SendSMSusing`Twilio`
Pushtomobileusing`Pushbullet`
Storein`MongoDB`soitcanbeaccessedin thefuturedashboards
WebSocket/AJAX Real-Time Updates
The dashboard interface applies asynchronous use of WebSocketorAJAXpollingtoupdate:
• Real-timeappliancestatus.
• Faceandobjectdetectionresults.
• Energy-savingsuggestions.
Thisarchitectureprovidesthelow-latencyfeedbackofthe userandcontinuousvisibilityofthesystem.
Thesecombinedalgorithmsenablethissystemtowork in real time to make intelligent decisions and provide many optionsforhomeautomationandenergysavingcapabilities. [12][14]
6. RESULT ANALYSIS
To test how the AI-Powered Energy Management System performed in real-time energy monitoring and human presence detection, and the generation of the alerts, the system was installed and executed in a residential environment. The system was therefore tested under differentconditionssuchasdayandnightconditionwithout light,withlittlelightandnormallightsaswellasscenarios with different activity of appliances. To test how the AIPowered Energy Management System performed in realtimeenergymonitoringandhumanpresencedetection,and the generation of the alerts, the system was installed and executed in a residential environment. The system was thereforetestedunderdifferentconditionssuchasdayand night condition without light, with little light and normal lights as well as scenarios with different activity of appliances.
Experimental Setup
• Camera: USB webcam/IP camera with 720p resolution.
• ProcessingUnit:NVIDIAGPU(CUDAenabled),16GB RAM.
• Software Stack: Python, OpenCV, TensorFlow, YOLOv8,facerecognition,Flask,MongoDB.
• Environment: Simulation of home set up with severalappliances (fan,light,airconditioner,etc.).
Model Accuracy
1. YOLOv8 Appliance Detection:
• Precision:94.6%
• Recall:91.2%
• F1Score:92.9%
• Latencyperframe:~0.025seconds(40FPS)
2. Human Presence Detection:
• FaceDetection(HaarCascade):~90.5%accuracy
• PoseDetection(MediaPipe):~92.8%accuracy
• Falsepositiverate(nohumanpresentbutdetection triggered):3.4%
3. Fire/Smoke Detection:
• Custom YOLO model was trained with fire and smokeimages.
• DetectionAccuracy:93.1%
• Detection Time: 1–1.2 seconds (including alert dispatch)
4. Face Recognition (Known vs. Unknown):
• RecognitionAccuracy:95.7%
• IntruderAlertTriggerRate(Correct):96%
C. Alert and Notification System
• Alertsweretestedacross:
• Email(SMTP)→Averagedelay:1.5–2.5sec
• SMS(Twilio)→Averagedelay:2.0–3.5sec
• PushNotifications(Pushbullet)→Instant(<1sec)
• MongoDBhandledconcurrentinsertionssmoothly, maintaininglogswithoutfailureover1,000+events.
D. Dashboard Performance
• BuiltusingFlask+JavaScriptwith AJAX/WebSocket integration.
• Real-timeupdatesdemonstratedsmoothtransitions, withlessthan500mslatency.
• User testing showed 95% satisfaction for responsivenessandusability.
E.Energy Efficiency and Behavioral Insights
Overaweek-longobservationperiod:
• A monitoring and alerting automation captured estimated18–22%reductionintheusageofenergy.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025 www.irjet.net p-ISSN: 2395-0072
• Thecommonformsofenergywastepatternswere recorded,andtheyinclude:
• Lights/fans kept ON when there are no human beings.
• Longuseofapplianceinroomsthatarenotinuse.
Case Study Scenarios
1. ApplianceON+NoHuman→Alertsuccessfullyfired up–After5s-7s.
2. Fire Detection → System was able to capture ire frameandsaveitwithboundingbox.dashboardupdated immediately.
3. Intruder Detection → An unknown face was detected and logged with timestamped record of notificationandstorageofimage.
Thesefindingsprovethatthesystemisworkablenotonly in real time conditions but also largely contribute to smarter,saferandmoreenergy-efficientresidentialliving. [8][10]
7. DISCUSSION AND CONCLUSION
The creation and implementation of AI-Powered Energy Management System for residential areas show how it is possibletointegrateAI,ComputerVision,andIoTtoincrease energy efficiency, and home safety. By combining YOLObasedobjectdetection,CNN-poweredfacerecognition,and real-time alert mechanisms, the system itself can be proactiveaboutmonitoringanddecision-makingpurposes.
From the findings found, it can been established that this system is capable of detecting appliance usage patterns, establishmentwhetherornotthereareresidentswithinthe house and eliminating unnecessary energy use. The successfuldeploymentoffireandintruderalarmextendsits usetosafetyandsecurityapplications.Withauserfriendly dashboardinterface,homeresidentsareempoweredwith realtimeunderstandingoftheenergyconsumptionaswell as behavioral patterns in their home. One of the system’s strengths is its modular architecture that enables its componentstooperateinisolationoraspartofafullstack solution.Theutilizationofasynchronoustaskexecutionand multi-threadingguaranteesrealtimeresponses,whereas,an expansive MongoDB backend is used for past logging and futureanalytics.
Althoughtheexistingimplementationdoeswellundermost residentialscenarios,somechallengesremain:
- Ambientlightingvariationssometimesmayaffect imagedetectionaccuracy.
–Falsepositivesinfacedetectionmaybeexperienced withthehiddenorpartially-exposedfaces.
- Large computing resources are needed for a continued real-time performance although this can be improved towards through edge computing strategies or evenmodelquantization.[18][19]
FUTURE WORK
In order to make the system even more effective, the followingdirectionscanbeused:
• Powermeteringsensorsintegrationtobeusedfor directenergymeasurement.
• Voice-command support with the use of NLP for manualoverrideandcustomization
• Deploymenttoedgedevices suchasRaspberryPi withoptimizedmodelversions(forexample,TensorRTor TFLiteimplementation).
• Scalingtomulti-homeorapartmentbuildingwith concentrateddashboardsandanalytics.[19][20]
CONCLUSION
This paper has introduced an AI-powered energy managementsystemthatisabletomonitoranappliancein real-time,detecthumanstoavoidfalsepositives,andalert where necessary in an intelligent way. The results are a confirmationofpossibilitiesofcombinationofAIwithIoTto produce sustainable and smart living environments. In additiontoassistinginwasteeliminationofelectricity,the systemaswellprovidesmoresafetyandempowermentto the user. With residential spaces being transformed to smarter ecosystems, these AI-powered solutions will take thecenterstageinthepursuitofenergysustainabilityand intelligentautomation.
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