What is CRISP?
Advanced Statistical Modeling for Energy Price Forecasting.
The CRISP model (Correlation and Relational Integration for Statistical Prediction) leverages advanced statistical methods and machine learning to forecast energy market trends. By analyzing Locational Marginal Prices (LMPs) and employing matrix-based techniques, CRISP identifies patterns in energy pricing to guide hedging and curtailment strategies.
Outperforming the Industry.
Statistically-Powered Operations.
The model predicts energy price trends over short and long durations, providing insights into when high prices may occur and their expected duration.
Energy Price Forecasting
Pattern Recognition
CRISP identifies visual and numerical patterns within large datasets, classifying energy prices into long or short-duration high-price groups for strategic analysis.
Matrix-Based Analysis
By employing advanced statistical methods such as correlation matrices and trailing averages, the model highlights consistent patterns in energy data while reducing noise.
Hedging and Curtailment Guidance
CRISP provides actionable recommendations for energy-consuming companies to hedge their consumption or curtail operations, optimizing financial outcomes.
Market Consistency Analysis
It detects variations in energy price consistency between long and short-duration trends, offering a clearer understanding of market stability.
Customizable Framework
The model is adaptable, allowing businesses to tailor it to specific energy grids, locations, or operational needs for more precise insights.