The Future of Energy: Blossom Sky enables data driven products

Blossom Sky utilizes AI and ML to enhance energy efficiency in utilities, automating tasks, improving decision-making, and providing insights for demand-side management and renewable integration.
The Future of Energy: Blossom Sky enables data driven products


Blossom Sky powered artificial intelligence (AI) can support utilities in becoming more digital by automating tasks, improving decision-making, and providing new insights. Two major use cases for AI in the energy industry are demand-side management and renewable energy integration. Our low-code data science UI reduces the time to market for the development of sophisticated AI and ML applications.

Use Case 1: Demand-side management

Automated demand-side management is an ML model for reducing or shifting peak electricity demand. This is useful for reducing grid pressure and avoiding blackouts. To give some clear examples, AI can be used to semi-automate demand-side management by collecting information from smart meters and other sensors in both residential and commercial properties. This data is used to develop demand-based tariffs that are tailored to the individual requirements of different communities, businesses, or persons. As example, an energy provider could offer consumers an incentive to shift their electricity usage to off-peak hours.

By using Blossom Sky and developing demand-side management and energy forecasting applications, utilities and energy providers can increase the amount of energy generated from renewable sources by using AI technology combined with decentralized data management to include private SCADA networks without compromising data and infrastructure security.

Renewable energy sources, including solar and wind power, are typically unstable energy sources, meaning they do not always produce electricity. Statistically, a year has at least five (up to 120 hours / year) days in which no sun or wind is available, so called dunkelflaute. Using ML or AI to predict energy generation helps grid operators plan when they should buy energy to stabilize the grid.

Here are a few use cases of how AI can be utilized to improve demand-side management and energy forecasting:

  • Demand-side management: PG&E in California has created a demand-side management tool called "Flex Alerts." Consumers receive Flex Alerts and are advised to cut their electricity usage for a brief period of time. According to PG&E, Flex Alerts can lower electricity use by up to 10%. FlexAlerts is an AI which continuously watches the grid to generate those alarms.
  • Energy forecasting: ERCOT in Texas is using AI to improve their energy forecasting. They use models to examine historical data and weather forecasts and predict how much electricity will be generated on any given day. These insights are utilized to plan generators and optimize the power grid for maximum efficiency. The technology has boosted ERCOT's energy forecasting accuracy by up to 15%, according to the company.

Use Case 2: Renewable energy integration

Renewable energy sources, such as solar and wind, are unreliable, which means they do not always produce electricity. This can make connecting these sources to the grid without producing instability problematic. AI can be used to forecast when and where renewable energy will be available. This data can then be utilized to dispatch generators and optimize the grid for peak efficiency. DataBloom AI has developed an LSTM model to predict energy consumption based on historical data. But there are other already implemented use cases to support  the energy transition from traditional to environmentally friendly energy sources.

  • Predictive maintenance: Artificial intelligence (AI) can be used to forecast when and where equipment will break, allowing preventive repairs to be undertaken before a failure happens. Predictive maintenance helps to save money and plan on expensive repairs and downtime upfront, reducing black-outs and stabilizing grid operations.
  • Asset optimization: AI can be used to optimize the installation and performance of renewable energy assets like solar panels or wind turbines. For example, optimizing the turbines in a large windpark and avoiding interference between the turbines results often in a 2-3% better energy output.
  • Demand-side management: AI can be used to create demand-side management algorithms that incentivize consumers to cut or shift their electricity consumption during peak hours. This can assist to reduce grid stress and avert blackouts, saving money on repairs and revenue loss.
  • Energy forecasting: AI can be used to improve energy forecasting, enabling utilities to better manage their resources and assure that they have enough to meet demand. As a result, the energy provider can optimize energy purchase expenses.
  • Grid optimization: AI can be used to optimize the operation of the grid by deploying generators and managing transmission lines, for instance. 

Using Blossom Sky to develop machine learning and artificial intelligence to strengthen the digitization of energy suppliers and utilities has the potential to dramatically increase the profitability of renewable energy facilities and make renewable energy more competitive against traditional power generation methods.

Benefits of AI for utilities

Decentralized data analytics with Blossom Sky can help energy providers and utilities become more efficient in their capabilities to maintain the power grid and install more renewable energy resources. By automating tasks, improving data analytics, and providing timely data driven insights, AI can help improve efficiency, reduce costs, and improve grid reliability. The three main pillars for outstanding grid operations using Blossom Sky are:

  • Increased efficiency: AI can help improve the efficiency of utilities by automating tasks and improving decision-making.
  • Reduced costs: AI can help reduce the costs of operating utilities by identifying and reducing energy losses.
  • Improved reliability: AI can help to improve the reliability of utilities by making them more resilient to disruptions.

We want to showcase three real-world examples of using AI in utilities that showcase how AI can be used to enhance energy commissioning and distribution and help transition to a cleaner energy future:

  • Demand-side management: The New York Power Authority (NYPA) uses sophisticated AI to create demand-side management algorithms that encourage consumers to reduce or shift their use of electricity during peak hours [1]. This has enabled NYPA to avoid blackouts while also saving money on repairs.
  • Energy forecasting: The Australian Energy Market Operator (AEMO) uses AI to improve energy forecasting in Australia. This helped AEMO to better manage its resources and cut costs. [2]
  • Grid optimization: The Danish Energy Agency uses AI to optimize grid operations in Denmark [3]. This has resulted in better efficiency and lower expenses.

[1] NYPA Selects Technology Firms to Help Accelerate Intelligence Capabilities

[2] Using deep learning to forecast renewable energy generation

[3] Application of energy informatics in Danish research projects

About Databloom

DataBloom AI is a distributed data access and analytics startup who provides "Blossom Sky," an AI-powered Virtual Data Lakehouse that allows machine learning, AI models, and data analytics to operate at the data source rather than a central data lake, consequently avoiding difficult data management processes.
Blossom Sky stands for federated data lake technology, data collaboration, increased efficiency, and helping to create new insights by breaking data silos in a unified manner through a single system view. The platform is designed to adapt to a wide variety of AI algorithms and models. Blossom Sky integrates with all major data processing and streaming frameworks like Databricks, Snowflake, Cloudera, Hadoop, Teradata, Oracle, Apache Flink as well as AI systems like Tensorflow, Pandas, PyTorch.

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