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Production-grade forecasting models for energy trading
48-hour-ahead forecasting for solar generation, load consumption, day-ahead prices, and intraday sessions with up to 94.7% accuracy, designed for energy management and trading.
- EMS Platforms
- BMS Solutions
- IPP Companies
- BESS Operators
- OMIE Traders
- EPEX Traders
Built to operate within your infrastructure
Forecasting models are deployed as an integration layer on top of existing energy systems, processing real-time and historical data to deliver continuous predictions.
Solar Generation Forecast
Predict photovoltaic energy production based on weather data, historical generation, and asset performance
Load Consumption Forecast
Forecast energy demand across assets, buildings, or portfolios using historical usage patterns and real-time signals
Market Price Forecast
Predict day-ahead and intraday electricity prices using market data, historical trends, and external signals
Built to operate within your infrastructure
Forecasting models are deployed as an integration layer on top of existing energy systems, processing real-time and historical data to deliver continuous predictions.
Solar Generation Forecast
Predict photovoltaic energy production based on weather data, historical generation, and asset performance
Move from energy data to predictive decisions
Driving performance at operational decisions
Accurate forecasts enable proactive decision-making across energy operations, trading, and asset management, improving performance across processes.
Use price forecasting to support day-ahead and intraday trading strategies.
- Predict price movements
- Improve bid/offer strategies
- Enable data-driven trading decisions
Forecast electricity prices across day-ahead and intraday markets to improve planning and market participation.
- Predict price fluctuations
- Improve procurement and sales timing
- Increase market visibility
Identify opportunities to buy, store, and sell energy at optimal times based on forecasted prices.
- Maximize spread opportunities
- Optimize charging and discharge windows
- Increase revenue from volatility
Support bid and offer creation with forward-looking market intelligence and price forecasts.
- Optimize bid placement
- Improve offer competitiveness
- Reduce bidding uncertainty
Use predictive insights to identify risk factors and adapt trading strategies before market conditions change.
- Monitor volatility exposure
- Improve portfolio resilience
- Support proactive risk management
Supporting all kinds of energy operators
Forecasting models are designed to operate across different layers of the energy ecosystem, supporting both operational systems and market participants.
How much could you save with better forecasts?
Estimated monthly impact
- Imbalance penalty savings€3.2K
- Day-ahead arbitrage improvement€2.4K
- Intraday revenue gain€2.7K
- Continuous Intraday capture€570
- Net monthly gain+€5.6K
Designed to align with your energy strategy
From integration to production in days
Forecasting models are integrated into existing energy systems within 1 day through an application programming interface.
01. Data Connection
We connect historical and real-time data from energy systems, including telemetry, weather sources, and market feeds
02. Model Configuration
We configure forecasting models for solar generation, load consumption, and market pricing based on operational requirements
03. Software Integration
We integrate forecasts into energy software or trading systems, and validate performance in real operational scenarios
04. Cloud Deployment
We deploy models into production with continuous monitoring, retraining, and performance optimization
Inside Nexenergie’s office today
Thinking behind the systems
Sharpen your vision of the industry with our research, reports, and perspectives that shape how we design intelligent systems.
Designing Predictive Energy Systems for Modern Energy Forecasting
A structured approach to solar generation forecasting, load consumption forecasting, and market price forecasting across EMS, BMS, BESS, IPP, OMIE, and EPEX environments
Integrating Forecasting into Energy Systems
Key patterns for deploying forecasting models within EMS, BMS, and OMIE trading environments
Managing Volatility in Energy Markets
Understanding how forecasting models improve performance in day-ahead and intraday markets
From Energy Forecasts to Operational and Trading Decisions
How forecasting models enable real-time operational and trading decisions across energy systems
Energy forecasting insights, delivered to your inbox
Frequently Asked Questions
Models are delivered via API and integrate directly into EMS, BMS, BESS, and trading systems without replacing existing infrastructure. Forecasts can be consumed in real time or batch workflows.
Historical energy data, weather data, and optionally market data. The exact requirements depend on the use case, but models can be configured based on available datasets.
Typical integration takes between 5 to 10 days, depending on data availability, system complexity, and required customization.
Forecasting models achieve up to 94.7% accuracy, depending on the dataset, asset type, and forecasting horizon. Performance improves over time through continuous retraining.
Forecasts can be updated in real time or at predefined 15-min intervals, depending on system requirements.
Yes. Models are continuously retrained using incoming operational data to maintain performance under changing weather, demand patterns, and market conditions.
Pricing is based on the number of locations and models used, with a monthly structure designed to scale with your energy operations.
Yes. Forecasting models support both operational decision-making and market day-ahead and intraday trading.
Ready to forecast what’s coming?
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