Supply and Value Estimation of Electricity in Smart Grids using Convolutional Neural Networks and Big Data Analytics

Electrical power systems are evolving into more intelligent and green frameworks. These tendencies have several beneficial outcomes, including the active engagement of consumers in power marketplaces. Nevertheless, the resultant consumption-side flexibilities induce significant demand variations, complicating the maintenance of power equilibrium and dependability inside smart grids (SG). Users may modify their power consumption behaviors in SG due to electricity pricing and subsidies. This leads to prices that may deviate from the original pricing structure. Predicting electricity supply and value is essential for SG's dependability and ecological viability. The use of big data analytics (BDA) for predicting has emerged as a prominent study area due to the substantial volume of data created and stored in the SG setting. With enhanced awareness of pricing and demand, power consumers may optimize their load management effectively. This research employs a Convolutional Neural Network (CNN) and BDA for SG Supply and Value Estimation of Electricity (SVE). Consequently, the proposed model uses many variables to forecast prospective energy usage and value metrics. The configuration of variables is essential since the efficacy of the prediction model is contingent upon these values.