Mar 14, 2024
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Completed
Predicting electricity utilization with AI/ML Model
$10,000+
4-6 months
United States
10+
Service categories
Service Lines
Artificial Intelligence
Domain focus
Energy & Utilities
Subcategories
Artificial Intelligence
Machine Learning
Challenge
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 optimization while navigating the uncertainties.
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 optimization while navigating the uncertainties.
Solution
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.
Transforming the data through Talend and loaded the data on Delta Lake.
Employing a ML model to accurately predict and analyze data to generate reports for decision making.
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.
Transforming the data through Talend and loaded the data on Delta Lake.
Employing a ML model to accurately predict and analyze data to generate reports for decision making.
Results
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 optimized the electricity distribution across various cities and zones while accounting for varying weather conditions.
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 optimized the electricity distribution across various cities and zones while accounting for varying weather conditions.