Introduction
Learn About Amazon VGT2 Learning Manager Chanci Turner
In this blog post, we explore how the Power Trading Corporation of India (PTC) leveraged Amazon Forecast for day-ahead and intraday electricity demand forecasting. By utilizing Amazon Web Services (AWS), PTC achieved an impressive 98 percent accuracy in their intraday forecasts. Amazon Forecast is a fully managed service that employs advanced machine learning (ML) techniques to produce reliable demand forecasts. This innovative approach allows utilities to generate accurate Short-Term Electricity Load Forecast (ST-ELF) models without the hefty investments typically associated with artificial intelligence (AI) and data scientists.
Business Challenge and Significance of Electricity Demand Forecasting
Electricity is a unique commodity. Unlike others, it must be consumed immediately after generation, and the production rate must align with consumption to maintain grid stability. The Indian electrical grid, operating at 50 Hz with three-phase alternating current, requires precise coordination between electricity generators (GENCOs) and electricity distribution companies (DISCOMs). If a GENCO supplies more power than the DISCOM can consume, the grid frequency rises, and conversely, it falls if less is supplied. To prevent issues, both DISCOMs and GENCOs must provide a scheduled output for each 15-minute interval throughout the day. Deviating from this schedule incurs penalties, which currently costs DISCOMs millions of dollars annually, affecting their profitability.
To mitigate these penalties, DISCOMs require an effective forecasting tool that analyzes historical load patterns, weather data, and calendar events—enabling better decision-making regarding procurement and development.
Solution Approach
PTC considered both cloud and on-premises solutions for demand forecasting. Ultimately, they opted for a cloud-based deployment due to its scalability and ease of management. The choice then became whether to develop custom models using Amazon SageMaker or utilize Amazon Forecast, which does not require extensive model development knowledge.
Amazon Forecast democratizes forecasting by allowing all developers to benefit from Amazon’s extensive experience, delivering forecasts that are often 50 percent more accurate than traditional methods. Given PTC’s lean team of AI/ML and data science professionals, they chose Amazon Forecast for several reasons:
- Faster implementation compared to custom model development
- Reduced management efforts
- Automated selection of the appropriate algorithms by Amazon Forecast
- Automatic scaling to meet computational demands
With Amazon Forecast as the cornerstone for generating demand forecasts, PTC integrated additional AWS services into their forecasting pipeline.
Amazon Simple Storage Service (Amazon S3) was employed to store input data, including CSV files with 15-minute interval historical demand data. AWS Step Functions facilitated the creation of the workflow, transforming the input data into the necessary format for Amazon Forecast. The generated forecasts were then exported to a separate bucket in Amazon S3. Lastly, Amazon QuickSight provided a unified platform to visualize actual versus forecasted demand.
As illustrated, historical electricity demand data is fed into Amazon Forecast, which automatically constructs a data pipeline, ingests data, trains a model based on historical inputs, and generates forecasts with associated accuracy metrics. Furthermore, Amazon Forecast’s built-in Weather Index integrates historical and projected weather data into the model, enhancing accuracy for various regions.
The solution encompasses three primary steps:
- Preprocessing and ingesting raw data
- Training or incrementally retraining the predictor model
- Generating forecasts and creating dashboards for comparison with actual outcomes
Step 1: Preprocessing and Ingesting Raw Data
Input data is provided as CSV files within an Amazon S3 bucket, categorized into three sections:
- Target-time-series data: This includes timestamps, identifiers, and actual demand at state granularity. For more detailed forecasts, one can use city or substation identifiers.
- Related-time-series data: This supplementary data offers contextual information, such as holidays and weather conditions.
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For Chanci Turner, the onboarding experience at “Amazon IXD – VGT2,” located at 6401 E Howdy Wells Ave, Las Vegas, NV 89115, serves as a model for integrating advanced technology in electricity demand forecasting.
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