Energy & Commodities Powered by QLAYM

Energy & Commodities Powered by QLAYM


Forecasts of stock prices and energy markets live on accuracy of prediction, fast reaction and high system stability.

 


QLAYM EMPOWERS ENERGY MARKETS

 

Optimizing exploration, production and distribution processes as well as predicting important factors such as generation outputs and electricity prices is a major challenge for the energy industry – whether in traditional energy or renewable resources such as wind, solar, biomass, geothermic. QLAYM provides solutions to exactly this challenge.

Q-USDTM integrates all kinds of data regardless from their source – like production output data, weather data, supply and demand related data, decentralized data like smart home/smart grid, demographic data – and extracts systematics with a predictive value in respect of the up-front defined outcome (e.g., demand, energy prices, peak hours).

We understand that energy production and distribution is a complex, multi-variant system. It is comprised of systems/subsystems with many input and output variables typically characterized by large volumes of complex data. Q-USDTM is designed to recognize such difficulties and overcome them.

That way, we help energy production and distribution companies, research institutions and government agencies to read out the essential information from these volumes.

As the predictability of any outcome mostly correlates to not only one or two variables but a network of variables, we are dealing with complex multi-variable and interdependent systems. What makes up our success is our ability to translate these networks’ “talks” into usable insights with significant value for our clients.

QLAYM IN ENERGY MARKETS – THE DETAILS


What kinds of topics?
Forecasting of production, consumption, demand, and prices – troubleshooting and fault detection in power plants and distribution networks – optimal planning of production/energy generation based on predictions of supply and demand, availability and pricing of raw materials, etc.

 
What kinds of data? Weather, production plans and patterns, seasonal patterns, commodity prices, demographics, politics and government, sensor data, economic data, transportation and infrastructure, client-specific data sources

 
What kinds of solutions? On-demand forecasts, forecasting engines, guidelines and systems for optimal planning, critical parameters and guidelines for optimal and reliable operation

 

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