How We Helped Client Predict Energy Demands & Achieve Operational Efficiency

There're few things more important to an energy services provider than its energy monitors and consumption analysis.

Any energy service provider strives to monitor their consumption metrics in real-time, with which they can forecast the demand and plan their production strategies. Unfortunately, manually monitoring every terminal and physically streaming the consumption metrics to the centralized system is close to impossible.

Delivering real-time energy consumption metrics with predictive analysis of market has become the standard for B2B executives. These cutting-edge tech solutions are beneficial for stakeholders, vendors, and end-users.

Gathering all the data droplets from the endpoints and making it flow seamlessly into a unified dashboard for energy consumption pattern analysis is a tedious task. Further, forecasting the market demand based on energy consumption patterns piles up with the complexity of data intelligence.

Energy consumption pattern analysis & demand forecasting were the biggest challenges for our client. However, without the expertise & experience in data intelligence, it would take at least 2-3 years to achieve these solutions and reach their organization goals.

"Our field engineers & operational staff were looking forward to making precise decisions in terms of electricity production. And that was challenging without robust data strategy & consumption patterns in place. Everything behind the scenes was siloed and we lagged in the data-driven decision." – Louis, Chief Strategy Officer

The client engaged with zeb to build a robust data strategy & data visualization solutions.

As a leading energy services provider, the client supplied electricity to households & commercial units across the country. They have energy monitors at every terminal (in households & commercial units) managed by a centralized application.

Our client aimed to build a real-time streaming dashboard that showcases energy consumption patterns. Based on these data insights, field engineers & operational staff would be able to optimize electricity production as per the market demands.

Therefore, we analyzed their IT landscape to understand their operations and requirements. We visited their household terminals, commercial units & centralized monitoring centers for an optimal understanding of the nature of their infrastructure.

Our solution involved building a data streaming pipeline and an energy consumption dashboard using Databricks and Power BI. This automated solution streamlined their end-to-end data lifecycle, saving the in-house IT team approximately 78 days per year.

Our team of data specialists collaborated with executives, field engineers and operational staff to endorse the business requirements and solution architecture.

Here's what our data streaming & data analytics solution encompassed:

• Energy monitors generate event files in JSON format and drop them into Azure Blob storage.
• Auto loader component in Databricks was leveraged to perform the incremental load of event files 24/7. This helped us achieve continuous streaming data pipelines.
• Considering the high volume of data and sudden surges, we carefully employed the Databricks cluster configurations and auto-scaling features based on speed, efficiency, and cost. As real-time streaming of energy consumption without any disruption is super-critical for their business, we specifically designed the solution to be resilient against data spikes. Also, the Databricks spark clusters assisted in faster & reliable real-time querying with efficient performance.
• Our data engineers built a robust data strategy on top of Azure Databricks Lake by adhering to medallion architecture (data-quality oriented platform- stage, raw, and curated). The raw zone will be loaded with JSON files. Then, these datasets will pass through the streaming aggregation pipelines (time-based aggregation) that will curate the data and load it into Delta tables.
• We have set the data constraints in Delta tables to ensure the quality of datasets.
• The curated datasets are fed to Power BI dashboard by leveraging the Databricks SQL warehouse live mode to enable real-time data refresh.
• Further, these energy consumption patterns were ingested to forecast market demands so engineers & operational staff could plan the production strategies.

Our client reaped significant business gains upon implementation of real-time data streaming and the analytics solution.

The top benefits of our Databricks implementation solution:

Intuitive energy consumption pattern analysis

Our data scientists designed & developed a unified energy consumption dashboard that showcases the real-time metrics with precision. We delivered the custom dashboard with drill-through & detailing based on their endpoints, network accessibility, work nature, and geographies.

As a result, the field engineers & operational staff could seamlessly analyze the energy consumption metrics at a regional level, on a weekly & monthly basis.

Demand forecasting & operational success

The client aimed for a data-driven culture & improved decision-making across their operations. Based on the insights derived from the real-time energy consumption dashboard, we developed a demand forecasting model.

These models helped the staff to predict market needs and therefore produce optimal levels of electricity to satisfy demands. These data-driven decisions gave our client a competitive edge, improved customer retention, reduced cut-offs, and sped up recoveries.

Automated data lifecycle

We orchestrated and streamlined their end-to-end data lifecycle into a master pipeline with best-in-class DevOps practices. From the data extraction of terminals to the visualization of the energy consumption metrics in an intuitive dashboard, and finally to distributing reports to the appropriate personnel, it has all been automated with appropriate schedules and customizations.

As per Louis, "Automated data lifecycle is giving back 3-4 hours every day to our IT & executives team. This made our production planning effortless & helped us achieve the operational success that's at the core of our business".

The intuitive energy consumption dashboard & demand forecasting models let our client save resources, reduce production planning hours, and cut out the guesswork. It may seem simple on the surface, but extracting the event-based JSON files from each terminal without discrepancies and streaming them as curated datasets to an energy consumption dashboard while creating demand forecasting models involved loads of brainstorming, planning, design, development, and deployment behind the scenes.

We made it possible with our institutional knowledge & technical expertise.

If you're striving to achieve data-driven culture for your business, feel free to reach us.