Step up your data-driven energy model

Energy business is asset-based but nowadays a new resource is in the spotlight - the data. The digitalization of the sector is mainly driven by Industry 4.0 and transition to new business and operation models is ongoing.?

But why is data important for the energy business??

Electricity markets are going towards near real-time operations and energy companies need to work even at a higher speed if they pursue success. The volatile market conditions sharpen the need for higher responsiveness to intensified changes. And to achieve proper responding and adjustments of the commercial strategies, portfolio managers and traders have to operate with larger data volumes in a timely manner.?

The utilization of data from various sources is a? differentiating factor for successful operations as it gives the higher chance to extract actionable knowledge for the growing complexity on the market.

Then we end up to the point if it is possible for humans to operate with millions of data records on a daily basis? Even though Excel is still a ground layer for many energy operations, the data sorting, cleaning, and aggregation process are evolving towards more automation.

So the recipe to build data-driven energy operations is to structure and orchestrate the entire journey from data to wisdom.

The journey from data to wisdom:

Manage correctly your data - it is the most valuable asset?

Besides asset-based, the energy sector is also data-heavy. Let’s take for example the management of renewable portfolios. Usually, data records are received at every 15-minutes interval for updated weather and production forecasts, actual metering volumes, price signals, and additional grid information. And to build an effective commercial strategy for spot markets (next day or within the day) - one shall be able to quickly decode the hidden knowledge.?

This process requires proper management to ensure your commercial decisions don’t suffer from GIGO (garbage in, garbage out). So you don’t trade solar capacity in the middle of the darkest night or shall we even mention the struggles during day-light saving time?..

How to start with data management?

• Bottom to top is the suitable approach for energy data management - first, define the hierarchy and organize the data according to it.
• Model your data to accommodate different business scenarios (keep in mind your business role - trader, producer, aggregator, supplier).
• Structure with rules the data sourcing and how it is format into a single unified data flow model
• Set high standards for data quality and how to maintain the physical and logical data integrity
• Never forget the security aspect in the different phases (at rest, in transit, and in use)

Data management is no longer stuck in the IT department - it’s part of all energy processionals daily life. And before powering data-driven decisions, make sure the data you based your decisions on is solid enough.

If you are looking for more details on the topic, explore our eBook dedicated to energy data management here.

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TokWise is a tech-driven company with innovative solutions to maximize the return of your renewable portfolio. Leveraging resilient technology and proprietary ML-based algorithms, the solutions are adding an extra intelligence layer to your trading operations reflecting the market dynamics and minimizing your cost.