EPPM: Forecasting Energy Prices with Precision

17/06/2024 Team Cloudar

Imagine waking up to a world where you know tomorrow’s energy prices well before your morning coffee. Every day at precisely 2 a.m., ENTSOE releases the hourly electricity prices for the upcoming day, a critical piece of information for both consumers and businesses to manage their energy consumption effectively. But what if you could see these prices not just a day in advance, but up to two weeks ahead? Enter EPPM (Electricity Price Prediction Model), a groundbreaking tool that leverages AWS native services to predict electricity prices with remarkable accuracy.

 

1 day published by ENTSOE

12 days predicted by EPPM

 

The Need for Advanced Forecasting

In our era of energy efficiency and cost management, accurate electricity price predictions are a game-changer. For businesses, this means optimizing operations and reducing costs. For consumers, it translates to better planning and substantial savings. EPPM employs state-of-the-art machine learning algorithms and AWS services to deliver highly accurate electricity price predictions.

 

How EPPM Works

EPPM utilizes a comprehensive approach, integrating various data sources and sophisticated analytical techniques to generate precise forecasts. Here’s a step-by-step breakdown of the process:

 

  • Data Collection: Every day at 3 a.m., our system kicks off data collection using AWS Step Functions. We gather information from multiple sources, including weather data, historical energy prices, and other relevant factors, all stored in Amazon S3.

 

  • Data Preprocessing: The raw data undergoes meticulous cleaning and processing using AWS Step Functions and AWS Lambda, ensuring consistency and reliability—the foundation for accurate predictions.

  • Model Training: Every Monday, new models are trained using Amazon SageMaker with the latest datasets. Hyperparameters are optimized, and the most effective model is saved in Amazon S3 for future predictions.

 

  • Prediction Generation: Leveraging the best-performing model, predictions are generated daily using Amazon ECS at 11 a.m. These forecasts offer a detailed view of hourly electricity prices for the upcoming weeks, saved into another S3 bucket.

 

  • Data Delivery: The predicted data is delivered via a REST API running inside AWS ECS on a Fargate container. This API is consumed by a frontend application, also running as a Fargate container, ensuring scalability, reliability, and seamless integration. Users can select date ranges and view comprehensive graphs comparing predicted and actual prices through an intuitive interface.

 

Real-World Applications

The applications of EPPM are extensive. Businesses can optimize their energy consumption by scheduling energy-intensive operations during low-cost periods. Utility companies can better balance supply and demand, reducing the risk of outages. Regulatory bodies can use these insights to develop policies that promote sustainable energy practices.

 

Conclusion

Predicting electricity prices weeks in advance is set to revolutionize energy management. EPPM not only provides this capability but does so with exceptional precision and dependability, thanks to AWS native services. As EPPM evolves, it promises to make a significant impact on the energy sector, driving efficiency and sustainability.

Imagine a future where energy usage is perfectly optimized, leading to substantial savings and a greener planet. With EPPM, that future is within reach.

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