CogniTensor's Energy and Commodity Price Prediction is a combination of correlation analytics and prediction dashboards that are specially designed to assist you in detecting future prices of energy and various commodities and suggests when to buy optimally, minimizes risk exposure and optimizes portfolio management.
It enables you to pre plan the buy cycle in accordance with future predictions for the day ahead, real-time market. These objectives range from optimized management of resources for energy portfolio and knowing the best buy time for required commodities. We enable you to achieve these objectives with a combination of advanced data analytics and data-driven forecasting, in order to provide you with actionable insights.
The predictive analytics use bespoke combinations of cutting-edge machine learning and deep learning models, backed by advanced research. These algorithms have been tailored specifically to handle the complex time series data coming from certified sources as well as taking account of external factors as well such as holidays, weather, etc . The entire system is developed on a state of the art platform, backed by open source technologies.
Each component is highly customizable in terms of data and other relevant specifications in order to cater to your needs in today’s dynamic business environment.
Features and Capabilities:
The solution provides Price Prediction of Energy for Day-ahead-Market, Real-time and Derivative on IEX enabling users to know best time to buy
Predicts price of various commodities on LME
Leverages an “ External Events” database capturing relevant external factors that impact price and volume
Build, monitor, analyze, and optimize power system operation models in real-time
Deploy Load Forecasting now in advance exactly how much power one needs to buy for each level
Self-learning predictive models that adapt to your needs, constantly evolving to deliver superior performance
Easy integration support with databases management systems such as Oracle, Postgres, etc.
Round the clock monitoring via an intuitive web app and on-the-go tracking through a dedicated mobile app