Project Summary

The client is a forward-thinking company dedicated to using cutting-edge technology to improve the management and prediction of energy usage in homes and businesses. To change how we understand and optimize energy consumption, they focus on developing and deploying a specialized AI model that predicts energy usage (Grid Forecasting Tool), emphasizing efficiency and sustainability.

Technical Stack

  • PostgresSQL
  • MySQL
  • Redis
  • Python
  • Python 3
  • Industry

    Information Technology

  • region
  • Region

    USA

  • project-size
  • Project Size

    $45,000-$50,000

Highlights

Reduced Manpower Costs

Grid Operations Optimization

Early Energy Source Security

Data-Driven Decision Making

Challenges

  • Adjusting to the dynamic needs of a household and simultaneously predicting the needs of utilities (energy) at an industrial scale consumed a lot of workforce and marketing spending to adjust to the grid's needs.
  • Utilization of resources to ensure their optimal usage is what companies & governments are trying to achieve. They can only do so with exact numbers of needed usage, which results in substantial operational costs.

Technical 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.

Solutions

  • 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. We developed four different AI models that fed prediction data to the portal & required decisions were taken based on in-portal analytics.

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Benefits Post-Implementation

With the usage of AI models and a centralized web application, clients benefited in the following ways:

  • Marketing and workforce costs were significantly reduced by utilizing the AI model to observe and analyze all the data from electric meters.
  • Usage of maintenance services & ordering of parts in advance based on warnings from AI prediction resulted in very accurate grid operation.
  • Analytics on utilities have enabled decision-makers to secure their energy sources early on.
  • no.-of-resources
  • No. of Developers

    12

  • time-frame
  • Time Frame

    April 2020- September 2022

Experience With Bacancy

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