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Databloom AI releases LSTEnergy, an energy consumption forecasting model
Databloom AI has released LSTEnergy, a Long Short-Term Memory (LSTM) model designed to predict energy consumption. The model is highly accurate, with the ability to analyze large datasets and forecast energy consumption for future periods.
By: Databloom AI
LSTEnergy is built using the latest machine learning techniques and is designed to be highly accurate. The model is also highly scalable and can be used to analyze large datasets.
"We are excited to release LSTEnergy on Github and HuggingFace,"
LSTEnergy is available for download on Github at https://github.com/
Databloom AI builds Blossom Sky, a federated data processing platform that enables distributed data processing across multiple data processing engines. Blossom Sky allows users to train specialized Long Short-Term Memory (LSTM) models that are tailored to specific domains and tasks. These models can leverage domain-specific data and knowledge to achieve higher accuracy and efficiency than big LLMs. And, more important, these models can be more transparent and explainable, allowing users to trust and control them better.
LSTEnergy is a powerful tool that can help businesses and organizations forecast their energy consumption with greater accuracy. By using LSTEnergy, businesses can optimize their energy usage and reduce their carbon footprint.
In addition to its energy forecasting capabilities, LSTEnergy can also be used for other applications such as predicting stock prices, water level forecasting, and more.
Databloom AI is committed to developing cutting-edge machine learning models that can be used to solve real-world problems. With the release of LSTEnergy on Github, Databloom AI has taken another step towards achieving this goal.
About Databloom AI
Databloom is a federated data access and data platform integration company that develops "Blossom Sky", an AI-Powered Data Platform Integration as a Service. Blossom Sky enables decentralized data processing over multiple data lakes or data warehouses in order to train AI and machine learning (ML) directly at the data source, reducing data management costs without sacrificing growth.
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Page Updated Last on: May 13, 2023