An Intelligent Energy Consumption and Optimization Dashboard Using Machine Learning Techniques

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072

An Intelligent Energy Consumption and Optimization Dashboard Using Machine Learning Techniques

Abhishek Kashid¹, Abhilesh Shriniwas², Tulsi Chavle³, Kalyani Jagnale⁴, Shraddha Kashid⁵

¹²³⁴ Student Authors, Department of Computer Science

MIT ADT University, Pune, India

⁵ Faculty Advisor, Department of Engineering

MIT ADT University, Pune, India

Abstract - The increasing demand for energy and the need for sustainable resource utilization have made intelligent energymanagementsystemsessential.Traditionalmonitoring systemslackpredictivecapabilitiesandreal-timeadaptability.

This paper presents an intelligent energy consumption and optimization dashboard that uses machine learning techniques forforecastingandanomalydetection.Thesystem integrates data preprocessing, predictive modeling, and anomaly detection to provide meaningful insights.

Machinelearningmodelsimproveforecastingaccuracy,while anomaly detection helps identify unusual energy usage patterns. The system also includes an interactive dashboard for real-time visualization, cost estimation, and smart scheduling.

The results show improved decision-making, reduced energy wastage,and better efficiency,supportingthedevelopment of smart energy systems.

Key Words: Energy Management, Machine Learning, Forecasting,AnomalyDetection,SmartDashboard

1.INTRODUCTION

Therapidgrowthinenergyconsumptionhasincreasedthe needforefficientenergymanagementsystems.Traditional systemsprovidelimitedinsightsandcannotpredictfuture usage.

With advancements in artificial intelligence and smart technologies,modernsystemscananalyzeenergydataand forecastdemand.Machinelearningtechniquesareusefulfor identifyingpatternsanddetectinganomalies.

Thispaperproposesanintelligentdashboardthatcombines machine learning models with visualization tools for realtimemonitoringandoptimization

2. RELATED WORK

Severalresearchershavecontributedtothedevelopment ofenergymanagementsystemsusingmoderntechnologies. Studies on artificial intelligence-based anomaly detection haveshownthatsuchtechniquescansignificantlyimprove

system efficiency by identifying irregular energy usage patterns.Machinelearningalgorithmshavealsobeenwidely usedtodetectanomaliesandforecastenergyconsumptionin variousenvironments.

Inaddition,IoT-basedenergymonitoringsystemshave been developed to enable real-time data collection from sensorsanddevices.Thesesystemsprovidecontinuousdata streams, which improve the accuracy of analysis and predictions.Forecastingmodels,particularlythosebasedon machinelearning,havedemonstratedstrongperformancein predictingfutureenergyconsumptionusinghistoricaldata.

However,mostexistingsolutionsfocusonasingleaspect suchasforecastingoranomalydetection.Veryfewsystems combineallfunctionalitiesintooneplatform.Theproposed system addresses this gap by integrating data processing, forecasting, anomaly detection, and visualization into a unifiedandefficientframework.

3. PROPOSED SYSTEM

Theproposedsystemisdesignedasamodularandscalable frameworkthatperformsintelligentenergymanagement.It begins with data acquisition, where energy consumption dataiscollectedfromdifferentsources.Thisisfollowedby datapreprocessing,whichincludescleaning,normalization, andfeatureselectiontopreparethedataforanalysis. Thesystemthenappliesmachinelearningmodelstoforecast futureenergyconsumptionbasedonhistoricalpatterns.At the same time, anomaly detection algorithms are used to identifyunusualorabnormalenergyusagethatmayindicate faultsorinefficiencies.

Finally, all results are presented through an interactive dashboard that allows users to visualize energy trends, monitorsystemperformance,andmakeinformeddecisions. Thisintegratedapproachensuresimprovedefficiencyand reducedenergywastage.

4. SYSTEM ARCHITECTURE

The architecture of the system consists of multiple interconnected components that work together in a structured manner. Initially, raw energy data is collected fromsensorsordatasets.Thisdataisthenpassedthrougha

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072

preprocessingstage,whereitiscleanedandtransformedinto asuitableformatforanalysis.

Afterpreprocessing,machinelearningmodelsareappliedto performforecastingandanomalydetection.Theforecasting model predicts future energy consumption, while the anomalydetectionmodelidentifiesirregularpatternsinthe data.

Theoutputofthesemodelsisthensenttothevisualization layer,whereaninteractivedashboarddisplaystheresultsin theformofgraphs,charts,andindicators.Thisarchitecture enables real-time monitoring, predictive analysis, and efficientenergyoptimization.

5. METHODOLOGY

Themethodologyoftheproposedsysteminvolvesthreemain stages: data preprocessing, forecasting, and anomaly detection.Inthedatapreprocessingstage,thecollecteddata is cleaned to remove noise and missing values. It is then normalized and relevant features are selected to improve modelperformance.

In the forecasting stage, machine learning algorithms are trained on historical data to predict future energy consumption.Thesemodelslearnpatternsandtrends,which helpinmakingaccuratepredictions.

In the anomaly detection stage, specialized algorithms are usedtoidentifyunusualpatternsorsuddenspikesinenergy usage.Theseanomaliesmayindicatefaults,inefficiencies,or abnormal behavior. Together, these steps ensure accurate analysisandeffectiveenergyoptimization.

Forvisualization,theDashframeworkisusedtocreatean interactive dashboard that displays energy consumption trends,predictions,andanomalies.Thesystemisdesigned to be user-friendly and efficient, allowing users to easily monitorandanalyzeenergydata.

7. RESULTS AND DISCUSSION

The results of the system demonstrate its effectiveness in analyzingandoptimizingenergyconsumption.Theenergy consumption analysis shows clear patterns and identifies peak usage periods, which helps in better planning and management.

The forecasting model produces predictions that closely match actual energy consumption values, indicating high accuracy.Thishelpsinplanningfutureenergyrequirements andavoidingwastage.

The anomaly detection system successfully identifies abnormal spikes and irregular patterns in energy usage. These detections help in identifying potential issues and improvingsystemreliability.Overall,theresultsconfirmthat the proposed system enhances efficiency and supports betterdecision-making.

8. CONCLUSION

Thispaperpresentsanintelligentenergyconsumptionand optimization dashboard that uses machine learning techniques for forecasting and anomaly detection. The systemprovidesacomprehensivesolutionformonitoring, analyzing,andoptimizingenergyusage.

6. IMPLEMENTATION

ThesystemisimplementedusingPythonduetoitspowerful libraries and ease of use. Libraries such as Pandas and NumPyareusedfordatapreprocessingandmanipulation. Scikit-learnisusedtoimplementmachinelearningmodels forforecastingandanomalydetection.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 13 Issue: 04 | Apr 2026 www.irjet.net p-ISSN: 2395-0072

Fig.- 3.Actualvspredictedenergyconsumption

It improves efficiency, reduces energy wastage, and enhances decision-making capabilities. The integration of multiple functionalities into a single platform makes it a powerfultoolformodernenergymanagementsystems.

4.Detectedanomaliesinenergyconsumption

9. FUTURE WORK

Inthefuture,thesystemcanbeenhancedbyintegratingIoT devicesforreal-timedatacollectionandmonitoring.Amobile applicationcanalsobedevelopedtoprovideeasyaccessto thedashboard.Additionally,advanceddeeplearningmodels canbeusedtoimprovepredictionaccuracyandhandlemore complex data patterns. These improvements will further increasethesystem’seffectivenessandusability.

REFERENCES

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[2] A.Zontaetal., “AI-basedanomalydetectioninenergy systems,” AppliedEnergy,2021.

[3] M.Mittaletal., “Machinelearningforenergyanomaly detection,” Energies,2021.

[4] M. T. Quasim et al., “IoT-based energy monitoring system,” JournalofCloudComputing,2023.

[5] “Predictive analysis of energy consumption,” IEEE Conference,2023.

[6] “Anomaly detection using deep learning,” IEEE Conference,2021.