Databricks releases Data Intelligence Platform for Energy

Databricks has announced the Data Intelligence Platform for Energy, a unified platform bringing the power of AI to data and people across the energy sector. Built on an open lakehouse architecture, Databricks’ Data Intelligence Platform for Energy enables enterprises to harness vast streams of energy data and develop generative AI applications without sacrificing data privacy or their confidential IP. Energy leaders gain a holistic view into their operations in real time to preemptively address maintenance needs, reduce unplanned downtime, accurately forecast energy generation, and take action for a more efficient, sustainable future.

The transition toward diversified energy sources is driving industry demand for a unified approach to data, analytics, and AI

The energy sector is experiencing a paradigm shift toward a smarter, cleaner and more reliable energy system, with renewables now providing nearly 30 percent of global power. With the Data Intelligence Platform for Energy, customers can democratize data access to their entire organization by delivering the full value of asset, operations, environmental, and customer data to optimize energy infrastructure and mitigate volatility. Databricks has been adopted by industry-leading organizations such as Australian Energy Market Operator (AEMO), Chevron Phillips Chemical, Cosmo Energy, Octopus Energy, Shell, TotalEnergies, Wood Mackenzie, and more.

“Embracing Databricks has been transformative for our organization’s digital transformation—it's the engine that powers our data-driven innovation for asset operations,” said Dan Jeavons, Vice President of Digital Innovation at Shell. “With Databricks, we've accelerated our data analytics and AI capabilities, helping to unlock real-time insights that drive strategic decisions and create process improvements, cost reductions, and production increases across our business."

“Octopus Energy is transforming energy systems through tech, delivering exceptional customer service while bringing bills down for customers. Databricks' Data Intelligence Platform for Energy plays a key role in this, allowing us to process and analyze large data sets generated by smart meters,” said David Sykes, Head of Data at Octopus Energy. “By gaining deeper insights into customer behavior and energy consumption, we can continue to create innovations and services our customers love so much, and ultimately drive the green energy revolution globally.”

Databricks delivers an open, flexible data and AI platform. With powerful tools and partners, Databricks’ Data Intelligence Platform for Energy enables customers across the energy sector to tackle critical challenges in the industry, including:

Real-time asset performance management and maintenance: Organizations can gather, analyze, and visualize vast amounts of sensor data from every physical asset — wind turbines, grids, pipelines, and machinery — to monitor and optimize performance in real-time, reduce downtime, and enhance overall operational efficiency.

Accurate, efficient renewable energy forecasting: Customers can minimize forecasting uncertainty and the unpredictable nature of wind, solar and hydropower sources with sophisticated predictive capabilities powered by machine learning (ML). By integrating weather forecasts, performance data, pricing trends and demand projections on a unified platform, the energy sector can more accurately manage demand and enhance resource allocation to maximize profitability in a volatile market.

A proactive, predictive approach to grid optimization: With the deployment of Advanced Metering Infrastructure (AMI), utilities can leverage advanced analytics and predictive modeling to gain real-time visibility into grid conditions. The Data Intelligence Platform for Energy enables companies to better forecast load, predict outages and balance supply and demand, reducing transmission losses, and improving overall grid reliability and resilience.

“Successful energy companies will set themselves apart by leveraging data, analytics and AI in novel ways to simultaneously minimize the risk of their strategies and tap new opportunities enabled by the energy transition,” said Shiv Trisal, Global Industry Leader for Energy and Manufacturing at Databricks. “This requires a different approach towards data intelligence that puts the power of AI in the hands of every user regardless of technical ability, allowing them to unlock unique insights from the company’s full knowledge base and data to power new innovations and shape a smarter, reliable and sustainable energy system for all.”

Pre-built data and AI solutions customized to tackle high-value customer use cases

The Data Intelligence Platform for Energy offers packaged use case accelerators that are designed to jumpstart the analytics process and offer a blueprint to help organizations tackle critical, high-value industry challenges. Popular data solutions for customers across the energy sector include:

LLMs for Knowledge Base Q&A Agents: Easily build an LLM-powered chatbot with Databricks that is pretrained with industry context and a customer’s knowledge base to offer an elevated, personalized experience to their end users.

IoT Predictive Maintenance: Ingest real-time Industrial Internet of Things (IIoT) data from field devices and perform complex time-series processing to maximize uptime and minimize maintenance costs.

Digital Twins: Process real-world data in real-time, compute insights at scale and deliver to multiple downstream applications for data-driven decisions.

Wind Turbine Predictive Maintenance: Analyze wind farm productivity and predict faulty wind turbines through a mix of AI/ML and domain-specific models.

Grid-Edge Analytics: Optimize energy grid performance and prevent outages by unifying data from various IoT devices and training a fault detection model to easily spot and address anomalies.

Real Time Data Ingestion Platform (RTDIP): Enables optimization, surveillance, forecasting, predictive analytics, and digital twins with a cloud-native open source framework focused on data standardization and interoperability.

Databricks partners deliver an ecosystem of purpose-built solutions for energy

Additionally, industry-leading Databricks partners including AVEVA, BKO, Capgemini, Celebal Technologies, CKDelta, Deloitte, Neudesic, and Seeq are driving the Data Intelligence Platform vision by delivering pre-built analytics solutions on the lakehouse architecture that are tailor-made for the energy industry. Featured partner offerings include:

BKO: BKO’s Common Model combines market and trading data with plant maintenance, inventory and operational data to produce a level of optimization far beyond simple plant maintenance and inventory management, all stored and managed by Databricks’ Unity Catalog.

Celebal Technologies: Leveraging its Power and Utility Forecasting Framework (PUFF), Celebal Tech provides an innovative renewable energy forecasting solution that delivers granular and holistic forecasts encompassing diverse categories like load, generation, price and weather. PUFF seamlessly integrates external factors like weather patterns and pricing to deliver accurate forecasting that empowers energy stakeholders to optimize resource allocation and planning.

CKDelta: CKDelta ∆Power is an AI intelligent application that utilizes extensive data analysis, including information on people movement, location attributes, and nearby factors, to identify the most strategic and high-demand locations for the installation of public charging stations for electric vehicles. It maximizes future revenue potential, calculates carbon offset and ensures reliable performance, all with a focus on a seamless user experience.

Neudesic: Smart Meter Analytics by Neudesic provides utility companies with a robust framework accelerator for ingesting, storing, and analyzing AMI (advanced metering infrastructure) data. This scalable solution orchestrates billions of daily data points with near-real-time analytical capabilities, empowering grid operators to utilize AI for understanding grid health, load demands, forecasts, and customer usage patterns.