In today’s energy industry, one of the key priorities is finding new ways to cost efficiently keep up with insatiable demands for power, while also delivering renewable energy. You must be able to predict when events will occur and make the first move. Being first to respond to customer or market events could be the difference between success and failure. Often, there are only seconds or minutes to initiate a response. For this reason, you have to assess the data available to you and turn it from “white noise” into information that can be used in real time to automate decisions and predict and react to critical events as they happen. Event-driven applications are coming to the forefront to address this next level of intelligent energy system because they can deliver real benefits to the business.

How can you apply this? Let’s take a look.

Use case: Intelligent energy grid

As the energy industry undergoes a major innovation cycle, electricity generation, distribution and consumption are all experiencing dramatic change. The biggest challenge is how to generate, store and use sustainably electricity to meet the consumers’ needs, while also keeping prices down and costs manageable. All of this is necessary to keep pace with increasing demand for electricity across the globe, because new power plants are too costly and not always aligned to regulatory pressures. On the supply side, a new more intelligent approach is needed to understand the amount of stored energy capacity there is across a variety of renewable and traditional energy sources. This is complemented by smart metering to optimize electricity use on the consumption side.

New analytics systems are needed to monitor vast volumes of real-time data to find the generation and consumption patterns, predict potential shortages or surges in use, and automatically respond to ensure that affordable and consistent power is available. Such systems would revolutionize this market, manage costs more effectively and provide a cleaner industry overall. The energy providers that employ this level of intelligent grid applications will competitively differentiate themselves for consumers and provide higher profits to shareholders.

Event-driven data management and analytics

The key characteristics of systems that could deliver an intelligent grid solution include the following capabilities:

Actively ingest millions of record per second from many different sources

Analyze streams of events with intelligent time-series analysis to identify the patterns and predict an impending event

Self-learn using machine learning and cognitive capabilities that constantly adapt to changing conditions and learn from them in their next response

IBM Project EventStore for event-driven business processes

The IBM Project EventStore technology preview, built on the Apache Spark platform, provides an ideal data platform for such innovative new systems that rely on huge velocities and volumes of data being constantly generated. Project EventStore is designed to ingest streams of millions of events per second while running high-performance real-time analysis over current and historical data to provide the intelligence and precision needed for event-driven applications. Combined with machine learning and the Data Science Experience, it can analyze, act and learn in real time to deliver the next level of business automation and differentiation.