Spiral Mantra Pvt Ltd.
Empowering Businesses with Digital Transformation
Empowering Businesses with Digital Transformation
The client is a prominent firm operating in the energy domain across Tier-I and Tier-II cities. Being one of the big names in the energy industry are present in various locations supplying energy to large areas.They believe in promoting sustainability and empower Green Initiative wastage and inefficient operations is a big no for the company.They sought to distribute electricity at a scalable rate and optimal cost. They faced challenges due to a lack of data access on electricity demand. This absence of crucial data resulted in a notable rise in electricity costs and wastage which led them to a need of data to help curb the problem.
The company faced the complex challenge of accurately predicting energy demand across various zones while accounting for weather sensitivities. This intricate task demanded innovative and predictive solutions to ensure efficient energy distribution and cost optimisation while navigating the uncertainties.
Starting with end-user data collection about demand from multiple sources also accounting climatic variations. USing Python and SQL to collect data, extracted every 15-20 minute intervals. Simultaneously, the data was seamlessly integrated with Weather API, NoSQL database, and SMTP dataset. Apache Spark and Apache Hadoop were leveraged for data processing. We transformed the data through Talend and loaded the data on Delta Lake. Employing a ML model to accurately predict and analyse data to generate reports for decision making.
With faster data access, consequently reducing the time required to obtain valuable insights. The Big Data Solution enabled the client to accurately forecast the electricity demand and save 20-30% on electricity distribution costs. The solution also helped the client in meeting the customer demand and ensured customer retention. It optimised the electricity distribution across various cities and zones while accounting for varying weather conditions.
