Residential Power Load Prediction in Smart Cities using Machine Learning Approaches

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.