Energy Management Enhanced with AI-Powered Grid Forecasting

Challenges

• Capturing data of the whole grid in a single place while maintaining data from offline sources.
• Establishing a data pipeline to take in real-time data for real-time prediction of usage or consumption.
• Dividing grids based on sectors and technicalities of processing electrical data was challenging.

• Our development team proposed two portals to cater to domestic consumption needs and another to predict industrial utility demand. There were, in total, three AI models working together to predict domestic demand and appliances each household can have to analyze future consumption. This combination of models will then be able to accurately predict the type of household appliance use while also predicting exact consumption per hour, day, and week.
• The industrial portal did not rely solely on electricity consumption; it also had water, gas & fossil fuel consumption as part of utility prediction. Our AI engineers developed four different AI models that fed prediction data to the portal & required decisions were taken based on in-portal analytics.