Energy & Utilities | Lucid Sphere AI

Key Analytics Software Capabilities for Energy & Utilities Companies

Tracking grid reliability metrics (e.g., SAIDI, SAIFU, CAIDI).
Calculating transmission and distribution losses.
Calculating and predicting the share of energy from DERs and assessing their impact on the grid.
Issue root cause analysis (e.g., for voltage fluctuations, outages).
Predictive maintenance for transformers, generators, power towers, and other grid assets.
Load demand forecasting and scenario modeling.
Dynamic pricing optimization.
Automated network load adjustment.
Instant alerts on urgent and potential issues, such as outages, congestion, and capacity constraints.

Water supply monitoring (e.g., energy consumption per unit of water produced, NRW percentage, water loss rate).
Real-time monitoring of water supply networks across various parameters (e.g., flow rates, pressure levels).
Leak detection and localization with prompt alerting.
Water demand forecasting and what-if modeling.
Predictive maintenance of network assets, such as pumps, valves, and reservoirs.

Real-time monitoring of heating network performance across multiple parameters (e.g., load, capacity, heat consumption, flow rates).
Predictive maintenance for heat exchangers, boilers, pumps, valves, and other equipment.
Detecting potential heat loss reasons (e.g., leaks, inadequately sized steam traps) based on the analysis of temperature differentials, flow rates, and insulation levels.
Heat demand forecasting based on identified heat consumption patterns and weather modeling results.
Real-time alerts on anomalies (e.g., sudden fluctuations in temperature or flow rates).

Upstream sector analytics

Midstream sector analytics

Continuous monitoring of storage tanks and distribution pipelines with alerts on issues like leaks or overfills.
Analytics of the supply chain, energy consumption, and inventory management.

Downstream sector analytics

Process efficiency analytics for petrochemicals production and refinery.
Quality control.
Monitoring transportation safety and efficiency.
Predictive maintenance of shop floor and transportation equipment.
Supply-demand forecasts and what-if models based on historical demand trends, weather conditions, and market changes.

Specialized monitoring and analytics solutions for coal, natural gas, hydroelectric, nuclear, geothermal, wind, solar, biomass, and CHP plants.
Analyzing energy and fuel consumption patterns within the power plant.
Analyzing the condition and performance of assets (e.g., boilers, heat exchangers, turbines, reactors).
Predictive asset maintenance.
Combustion optimization insights (for coal power plants).
Continuous safety analytics (e.g., tracking temperature and pressure within systems, gas concentration and radiation levels).
Monitoring emission levels.
Instant alerting on issues with automated emergency shutdowns.

Monitoring asset KPIs, e.g., asset uptime, maintenance costs as a percentage of asset value, MTBF, MTTR.
Continuous analytics of asset performance and state with insights into utilization optimization.
Predictive and preventive asset maintenance.
Asset lifecycle cost analysis.
Identifying root causes of asset inefficiencies and failures (e.g., operating errors, environmental factors).
Predicting the end of assets’ useful life.
Immediate alerting on asset performance issues.

Tracking the emissions of air pollutants (CO2, NOx, SO2).
Water quality analytics (e.g., pH levels, turbidity, contaminants).
Analyzing data on waste generation, composition, and disposal.
Monitoring noise levels.
Assessing the environmental impact of potential projects and policies through what-if scenarios.
Comparing the level of environmental impact against thresholds set by internal policies and regulatory authorities (e.g., EPA, FERC).
Sending instant non-compliance alerts.

Multidimensional customer segmentation (e.g., by demographics, location, industry, company size).
Identifying customer- and segment-specific resource usage patterns, such as peak demand periods and seasonal variations.
Customer lifetime value analysis.
Customer churn prediction.
Customer sentiment analysis based on data from surveys, review platforms, and call transcripts.
Performance insights for demand-side management (DSM) programs to optimize program design, target audience selection, and incentive structures.
Personalized energy usage and cost analytics for customer portals and mobile apps.
AI-powered recommendations on customer-specific billing options (e.g., fixed monthly payment plans, time-of-use pricing).

Analyzing costs across all business directions (e.g., planned costs of maintenance and business development).
Segmenting costs by labor, maintenance, fuel, overhead, and other expense categories.
Revenue analytics with revenue stream attribution (e.g., by customer segment, service offering).
Profitability analytics.
CAPEX and OPEX budget analysis.
Financial risk management.
Insights into the minimization of tax liabilities (e.g., pinpointing relevant tax credits and incentives).
Payroll analytics with employee performance vs. compensation benchmarking.
Financial planning and forecasting.
Financial performance and financial compliance reporting.
Scenario analysis for optimized energy trading operations.

Identifying cases of tampering, meter bypass, and unauthorized access by detecting anomalies and outliers in meter readings.
Detecting fraudulent customer behavior through the analysis of resource consumption patterns, service requests, and payment history.
Identification and monitoring of at-risk customer segments.
Identifying discrepancies in billing data.
Instant alerts on fraud detection.

Continuous monitoring of inventory levels (e.g., fuel, spare parts, chemicals).
Inventory demand forecasting based on asset condition and performance, historical demand, and environmental data.
Tracking and optimization of logistics operations to ensure timely inventory availability.
Supplier performance insights.
Monitoring the adherence of SCM processes to the required ESG regulations.
What-if scenarios of delivery routes based on historical SCM data.

Tracking recruitment and hiring KPIs (e.g., time-to-fill, cost-per-hire).
Identifying the sources of high-quality hires (e.g., referrals, job boards).
Evaluating employee performance against set goals and other internal parameters and pinpointing top-performing employees.
Workforce resource forecasting based on past and current service requests and employee vacation schedules.
Tracking employee participation in training and development activities and evaluating their impact on productivity.
Identifying skill gaps based on data about employee certifications and training.
Tracking employee attrition rates and providing insights for efficient retention strategies.
Identifying trends and root causes in safety incident data.
Insights into employee satisfaction and engagement based on survey data analysis.