Residential Power Load Prediction in Smart Cities using Machine Learning Approaches
Waleed Alomoush, Tahir Abbas Khan, Mehwish Nadeem, Jamshaid Iqbal Janjua, Anwaar Saeed, Atifa Athar
20222022 International Conference on Business Analytics for Technology and Security (ICBATS)21 citationsDOI
Abstract
Accurate load prediction plays a vital role in energy planning and load management and offers a distinctive opportunity for applying advanced analytics. Stake holders of power markets gains benefits with better integration of load management, smart grid control and metering in smart cities. It helps to improve efficiency of power load consumption. The paper proposed hybrid method based on Machine learning for predicting residential power load. We positioned correlated feature extraction and applied with system model to generate predictive results. The loss function and RMSE were calculated for accuracy of the prediction results.
Topics & Concepts
Smart gridComputer scienceMetering modeLoad profileEnergy managementPredictive analyticsElectric power systemLoad managementControl (management)Predictive modellingPower (physics)Machine learningElectricityArtificial intelligenceEnergy (signal processing)EngineeringMathematicsMechanical engineeringPhysicsElectrical engineeringQuantum mechanicsStatisticsEnergy Load and Power ForecastingTraffic Prediction and Management TechniquesSmart Grid Energy Management