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ProServ

Overview

Perks: Reusable and Easy Deployment: Our solution is designed to be reusable, so it can be easily deployed on AWS infrastructure and does not require significant modifications when applied to different farms or projects. Accurate and Reliable Power Forecasts: Our solution uses advanced analytics and machine learning to provide accurate power forecasts, which can help you make better decisions and improve operational efficiency. Cost-Optimized Solution: Serverless Architecture in AWS enables our Solution to deliver forecast in minimal cost & efficient resource utilization and eliminating the need for extensive infrastructure investments & management. Continuous Improvement and Refresh: We continuously improve our solution by monitoring the performance of the models using KPIs such as nMAE. If the performance exceeds the acceptable threshold, the models are retrained and refreshed to maintain accuracy. Data Security and Compliance: Our solution complies with AWS security guidelines and protocols to protect and ensure the integrity of your data. We also have compliance support embedded within our system to meet regulatory standards.

Sold by XenonStack
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Fulfillment method Professional Services

Pricing Information

This service is priced based on the scope of your request. Please contact seller for pricing details.

Support

Power Forecasting Support: System Support: This solution provides robust system support to ensure the smooth operation and reliability of the power forecasting models. We address failures of models through proactive monitoring and troubleshooting mechanisms. Alerts for Model Failures We have set up alerts using AWS SNS (Simple Notification Service), ensuring reliable and scalable communication regarding model failures, to promptly notify the stakeholders in case of model failures. These alerts are triggered whenever an issue is detected within the model. CloudWatch for Log Capture To assist in troubleshooting and root cause analysis of model failures, we have enabled CloudWatch logs. It captures detailed logs of relevant events related to the models valuable for diagnosing any issues occurring during the forecasting process.