The Challenge of Energy Optimization

Nissha needs to have a detailed idea of production plans for the next period, as well as an estimation of wind and solar energy produced. The difference between the two is then the amount of energy they have to supply from the grid. Forecasting this manually proves to be a cumbersome task.

In order to avoid having exposure to fluctuating energy spot prices and negotiate energy contracts upfront with a pre-agreed rate, Nissha needed:

• A Unified Energy Data Ecosystem: This will enable capturing more insights from data, especially since energy comes from multiple sources—grid consumption, wind, and solar production.

• OAligning Production & Energy Usage: Achieving this alignment presented opportunities to further optimize efficiency and reduce exposure to fluctuating energy prices.

• Automated Planning & Forecasting: A more automated and predictive approach could further support data-driven decision-making and streamline planning.

Solution: An AI-Driven Energy Forecasting & Optimisation Tool

Faktion partnered with Nissha to design a robust tool that consolidates energy data, forecasts production and consumption, and simulates future scenarios for contract optimisation.

Key Features and Innovations:

• Integrated Data & Visualisation: Aggregates data from manufacturing plans, energy consumption, renewable production, and spot energy prices. The tool also tracks trends in energy demand, production, and prices to support data-driven decision-making.

• Advanced Forecasting & Simulation: Enables users to simulate alternative futures, such as high or low renewable production or unfavourable energy prices. Outputs include:

  • Predicted total energy demand and grid consumption

  • Renewable energy production

  • Spot price trends

• Energy Contract Optimisation: Evaluates energy contracts based on pledges and pricing, allowing Nissha to:

  • Define pledges and prices

  • Compare scenario-based cost estimates

  • Incorporate fixed renewable energy costs for realistic calculations

Approach

Faktion’s approach focused on iterative development and close collaboration with Nissha to deliver a tailored solution:

1. Data Integration and Exploration:

• • Consolidating diverse datasets (e.g., renewable production, spot prices, and manufacturing plans).

  • Validating inputs with domain experts to ensure accuracy and relevance.

2. Predictive Modelling and Simulation:

• • Developing machine learning models to forecast energy consumption and renewable production for various scenarios.

  • Simulating alternative futures, allowing NMS to assess risks and optimize contract strategies.

3. Tool Development: Building a modular and intuitive tool with functionalities to:

   • Upload and visualize input data.

   • Tweak parameters like renewable usage costs, pledge levels, and spot prices.

   • Generate visualised contract cost comparisons.

4. Deployment and Iteration:

   • Delivering the tool in phases, allowing user feedback and iterative refinement.

   • Ensuring compatibility with Nissha’s IT ecosystem and compliance with sustainability goals.

Outcome: Successfully navigating complex energy management & cost

The AI-powered energy forecasting & optimisation tool transformed how Nissha manages energy procurement, pricing, and consumption.

• Real-time insights into energy demand, consumption and production.

• Smarter energy contract selection, reducing exposure to spot prices.

• Reduced manual workload due to automated forecasting and planning.

• Much more sustainable energy management.

By adopting this innovative solution, Nissha now has the tools to navigate energy market volatility, maximise renewable energy utilisation, and achieve cost-effective, sustainable operations.