Energy Trading

Reducing Cost by Optimizing Energy Procurement Portfolio

AI has been used in trading mechanisms since the 1980s. There have been major developments in the form of autonomous trading algorithms that can scour billions of bits of data, spot trends, adapt and make money. While such ideas can already be used for quick-win efficiency gains in conventional energy trading, AI will really take off in future markets. These will involve billions or even trillions of micro trades between millions of generating units and consumers, with AI balancing this smart market using intelligent trading.

Key Challenges

Maximize Uptime

Currently, keeping energy production to a maximum uptime is a highly challenging task. Seasonal variations in supply can cause a lot of disruptions and loss of revenue in the entire supply chain. However, with the help of AI, stakeholders can help preempt energy demand and align necessary energy sources to keep maximum uptime from a diverse source mix.

Bolster Procurement Decisions

With the help of predictive analytics, powered by AI, business stakeholders can reduce their procurement costs through preemptive demand forecasting and accurate price predictions on energy exchanges. This helps energy enterprises prepare inventory levels to keep the lowest time to market while preventing over or under stocking. Moreover, business stakeholders can identify the optimal strategies for procurement through holistic deep vendor grading and profiling.

Green Procurement of Energy

Keeping sustainability in mind, the power of Artificial Intelligence can be leveraged to generate a long term view of renewable energy demand and generate actionable recommendations on the steps to meet that demand based on different scenario requirements of the future. The power of ML can also help build simulations in order to visualize how renewable energy resources may be used in future, helping fuel the green revolution of the Energy industry.

Direct Benefits of AI Solutions

Accurately forecast energy demand

With the help of an external factors database, CogniTensor offers a unique method to optimize forecasting. While traditional enterprise data is being used all over the world to forecast future events, Cognitensor's forecasts include multiple factors that may be affecting future events as well. From geopolitical events to climate change, all factors are taken into the solution development process to provide accurate forecasting at your fingertips.

In addition to deep profiling, CogniTensor can help stakeholders plan procurement with data driven forecasting of energy prices on the Indian Energy Exchange. This helps reduce an organisation’s procurement costs through optimizing vendor selection based on delivery quality, timeliness and price.

Reduce procurement costs with price prediction

Gain a finger on the pulse on energy management

Cognitensor's AI powered solution enables organizations to select optimal channel mix for energy fulfillment enabling them in procurement cost reduction. It also automates the workflow to provide greater efficiency in managing energy distribution.