Predictive Solutions for Efficient Energy Distribution

Client Overview

Our client is a household name in the energy sector and serves Tier I and Tier II cities in Indian mainly. Being a big name in the energy sector they care deeply about reducing energy wastage while serving multiple areas simultaneously. To reduce their operational and monetary challenges they decide to hire big data engineer to help curb the problem.

Challenge

Recently our client faced challenges due to rising energy demand and their inability to serve due to lack of data. Keeping their long-term goals in mind, they wanted a solution that accurately predicted energy demand across various zones while understanding weather sensitivities. This task demanded an innovative solution that ensured efficient energy distribution and was cost-effective as well.

Our Solution

To combat our clients’ problem efficiently, our engineers started with end-user data collection generated from multiple sources. Using Python and SQL, we collected data at an interval of 15-20 minutes. Simultaneously, the data was seamlessly integrated with Weather API, NoSQL database, and SMTP dataset. Apache Spark and Apache Hadoop were utilised for data processing.

Whatever data we received, we transformed them using Talend before loading them onto Delta Lake.

Business Outcome

With better insight availability, our client improved their decision-making process significantly. Post implementation, approximately 20-30% costs were saved on electricity distribution. Not only that, but it also helped our client meet their rising customer demands easily. Overall our solution optimized their electricity distribution across their serving zones.