Unlock the untapped potential of data analytics to slash costs

With data analytics at your fingertips, visualize and delve into the intricacies of energy consumption patterns, unveiling a treasure trove of insights for comparisons and investigations. Analyzing data on a granular level - daily or even hourly - unearths consumption profiles that expose anomalies and confirm the efficacy of systems such as heating and ventilation. Pinpointing consumption peaks and their origins empowers you to curb peak power and trim your basic payments.

Augmented data: The key to precision in consumption analysis

Marrying consumption data with data from IoT devices, such as temperature, humidity, and air quality sensors, supercharges the accuracy of your consumption analysis. Incorporating systems that gauge building usage, like domestic water and device-specific energy measurements, further refines the analysis. Normalizing data using temperature paves the way for comparability across diverse seasons and conditions, setting the stage for cutting-edge analytics.

Predictive models: The next evolution in data analytics

Machine learning algorithms propel data analytics to new heights, empowering you to create predictive models for energy consumption. Harness factors like outdoor and indoor temperature, windiness, solar radiation, and historical energy consumption to forecast energy usage in a building. These models aid in detecting deviations and pinpointing potential energy issues, such as ventilation complications or improper temperature settings.

Maximize demand elasticity with renewable energy

Regression models unveil the potential for savings and quantify demand elasticity, streamlining the shift of consumption from high-price hours to low-price hours or minimizing peak power. Demand flexibility paves the way for a more effective use of clean, renewable energy sources.

Data-driven investment decisions: The new gold standard

As solar panels gain traction, precise data is critical for making enlightened investment choices. Conventional counters fall short in considering a building's consumption profile or potential demand elasticity, possibly leading to misguided investment decisions. Estimates based on historical consumption deliver superior outcomes but demand additional effort.

The future of energy management: A scalable, modern data platform

The vast potential of data analytics in the energy sector can be harnessed most effectively through collaboration and a deep comprehension of customer needs. Cloud-based data pool solutions offer a scalable approach, merging energy measurement data, IoT sensor data, and external factors like outdoor temperature. Constructing a knowledge-driven management tool atop a contemporary data platform lays the groundwork for sophisticated data analytics, fueling efficiency and cost savings in the energy industry.