Renewables Integration
Manage the variability and complexity of renewable generation in grid operations.
SLIQ is a decision intelligence platform for modern energy grids. It enables utilities and energy operators to transform high-frequency operational data into governed, actionable grid intelligence — supporting load forecasting, demand response, renewable integration, and grid visibility at scale.
Energy grids are generating more data than conventional analytics systems were built to handle. SLIQ closes that gap — connecting raw telemetry, smart meter reads, weather signals, and market data into a unified intelligence layer that operations and planning teams can act on in real time.
Manage the variability and complexity of renewable generation in grid operations.
High-resolution analysis of grid behaviour across the network in real time.
Understand and anticipate demand patterns to optimise grid planning and operations.
Identify and activate flexible loads to balance supply and demand in real time.
Control room and operations engineers who need real-time grid visibility and anomaly detection to maintain reliability.
Flexibility programme managers who need to identify, activate, and settle flexible loads to balance supply and demand.
Trading and market analysts who need price signal analytics, forecast accuracy tracking, and position reporting.
Utility CDOs and transformation leads building the analytics infrastructure to support decarbonisation and grid modernisation goals.
Electricity grids are becoming increasingly complex. Renewable energy integration, distributed generation, electric vehicle charging, demand response programmes, and high-resolution smart meter data are creating volumes and velocities of operational data that conventional analytics systems were not designed to handle.
Utilities often have the data but struggle to convert it into real-time operational intelligence. SLIQ closes that gap.
SLIQ integrates multiple operational data streams to build a unified view of the grid.
High-frequency consumption readings from AMI infrastructure across the network.
Real-time operational data from substation and grid control systems.
Generation and storage data from rooftop solar, batteries, and microgrids.
Temperature, irradiance, wind speed, and other variables influencing demand and generation.
Wholesale and retail price signals for demand response and energy trading analytics.
SLIQ enables utilities to move from reactive grid management to predictive grid intelligence.
Predict short-term and long-term demand across the network using historical consumption, weather, and market data. Support planning, dispatch, and procurement decisions with accurate forward-looking demand signals.
Estimate appliance-level energy consumption from aggregate smart meter data — without additional sensors. Understand what is driving demand at the customer and network level to enable targeted demand-side programmes.
Identify flexible loads capable of participating in demand response programmes. Optimise dispatch and incentive design. Track participation and measure programme outcomes at customer and grid level.
Provide real-time and historical insight into consumption patterns across the network at high temporal and spatial resolution. Support operational planning, outage response, and network investment decisions with live grid intelligence.
Responsible for grid stability, operational planning, and real-time network management.
Monitoring demand and supply conditions to support trading, procurement, and pricing decisions.
Designing and managing flexible load programmes and measuring participation and outcomes.
Leading the modernisation of grid analytics and data infrastructure capabilities.
SLIQ integrates with modern data infrastructure without requiring replacement of existing systems.
Cloud data warehouse integration for large-scale grid data storage and querying.
Distributed data processing and ML platform for advanced grid analytics workloads.
Relational database integration for structured operational and historical grid data.
Integration with AWS S3, Azure Data Lake, and GCS for large-volume telemetry storage.
Direct integration with smart meter and SCADA telemetry pipelines for real-time data ingestion.
SLIQ transforms high-frequency AMI data into actionable customer and network intelligence — without additional sensor investment.
Disaggregate appliance-level energy consumption from aggregate smart meter reads. Understand what is driving demand at the customer and circuit level without additional hardware deployment.
Segment customers by consumption patterns, profile types, and behavioural characteristics. Enable targeted demand-side programmes and personalised energy services at scale.
Estimate individual appliance use and saturation rates across the customer base. Support product development, efficiency programme design, and demand-side management planning.
Analyse time-of-use patterns, peak demand behaviour, and seasonal consumption shifts from AMI data. Provide the customer intelligence needed to design effective demand response and tariff strategies.
Identify anomalous consumption patterns indicative of non-technical losses, meter tampering, or data quality issues. Reduce revenue leakage through data-driven anomaly detection at scale.
Aggregate smart meter data into substation and feeder level load profiles. Support network planning, capacity investment, and operational decision-making with high-resolution demand visibility.
From same-day dispatch forecasting to multi-week planning horizons — SLIQ delivers accurate demand intelligence at every timescale.
Hours-ahead demand forecasting for operational dispatch, balancing, and real-time grid management. Combines historical consumption, weather nowcasting, and intraday signals to produce accurate near-term demand curves.
Weeks-ahead planning forecasts supporting procurement, generation scheduling, and demand response programme activation. Extended forecast horizons account for seasonal patterns, weather forecasts, and economic signals.
Renewable variability creates forecast uncertainty that conventional models were not designed to handle. SLIQ incorporates solar irradiance, wind forecasts, and generation profiles to account for the impact of distributed and utility-scale renewable output on net demand.
Probabilistic demand forecasts quantify uncertainty ranges and tail risks, not just point estimates. Decision-makers get actionable confidence intervals to support risk-aware dispatch and procurement decisions.
Electric vehicle charging represents a growing and highly variable new demand source. SLIQ models EV adoption and charging behaviour patterns to quantify their impact on local and network demand forecasts.
Continuous tracking of forecast accuracy metrics including MAPE, RMSE, and bias. Automated model retraining triggers and performance dashboards ensure forecasting models remain calibrated as grid conditions evolve.
SLIQ enables utilities to design, operate, and evaluate demand response programmes with data-driven precision — from customer targeting to post-event settlement analytics.
Identify and quantify flexible demand available to shed or shift during peak events. Model peak load duration curves and prioritise demand reduction interventions by cost-effectiveness and reliability.
Provide the analytics layer to support real-time grid balancing decisions, including identification of flexible loads, quantification of available demand reduction, and coordination of dispatch across multiple programmes.
Optimise dispatch decisions across demand response assets using AI models that account for participant response probabilities, programme costs, and grid constraints. Maximise reliability while minimising dispatch costs.
Segment customers by flexibility potential, response reliability, and participation history. Target demand response recruitment and programme design at customers most likely to provide reliable, cost-effective flexible capacity.
Measure actual demand reduction against counterfactual baselines at participant level. Automate settlement calculations and produce event performance reports to support programme optimisation and contract management.
Track demand response programme KPIs including participant retention, response reliability, and MW delivered. Use performance analytics to improve programme design, identify high-value participants, and report outcomes to regulators.
Improved demand forecasting accuracy at network and customer level
Greater grid stability through predictive rather than reactive operations
Enhanced demand response participation and programme effectiveness
Real-time visibility into consumption patterns across the network
Data-driven energy planning to support renewable integration and sustainability targets
Unified analytics intelligence layer for the modern digital grid
SLIQ becomes the analytics intelligence layer for the modern digital grid.
See how SLIQ transforms smart meter and grid data into load forecasting, demand response analytics, and operational grid intelligence for utilities.
See SLIQ in action and discuss your requirements with our team.