Grid operators spend half their day preparing tomorrow’s load forecast. DeepTSF compresses that into minutes letting your analysts test, compare, and deploy production-ready forecasting models from a single application.
Challenge
Grid operators, utilities, and energy planners must continuously balance electricity supply and demand. Inaccurate demand forecasts can lead to inefficient resource utilization, higher operational costs, grid instability, and increased balancing requirements.
Developing advanced forecasting models typically requires specialized data science expertise, making it difficult for many organizations to leverage modern AI techniques effectively.
Solution
DeepTSF enables users to create, train, evaluate, and deploy advanced electricity load forecasting models through a no-code environment.
Users can upload historical consumption data, configure forecasting scenarios, and compare multiple AI forecasting approaches to identify the most suitable model for their operational needs.
The platform automates the forecasting workflow while providing access to state-of-the-art time series forecasting technologies.
Benefits
Improve Operational Visibility
Gain a clearer understanding of future demand patterns and expected network conditions.
Anticipate Demand Peaks
Identify periods of increased electricity demand before they occur and prepare accordingly.
Support Better Planning
Enable more informed operational, maintenance, and resource allocation decisions.
Increase Forecast Reliability
Leverage advanced AI forecasting techniques to improve prediction accuracy across different time horizons.
Reduce Operational Uncertainty
Provide decision-makers with actionable insights that support proactive rather than reactive operations.
Real-World Applications
Portuguese National Grid (REN)
DeepTSF was utilised for national electricity demand forecasting for the Portuguese transmission system operator (REN).
20 European Transmission System Operators
A global forecasting service built using DeepTSF generates aggregated hourly net electrical load predictions across 20 European TSOs using LightGBM as the core short-term load forecasting model.
ASM Terni: Predictive Maintenance Integration
DeepTSF time series outputs were combined with power flow simulation models for an Italian utility company (ASM), enabling predictive maintenance across grid assets.
Terni Energy Community: Reverse Power Flow Reduction
DeepTSF drove consumption and flexibility prediction inside a local energy community, allowing optimized aggregation and flexibility trading to reduce reverse power flow incidents.
