Stem Inc. Improves Energy Optimisation Using Predictive AI
Forecasting energy demand
Energy strategies can vary market to market. For example, Ontario, Canada is a specific market with a 'global adjustment' model requiring energy providers to predict on what hour of the day peak electricity demand occurs.
Knowledge about how the demand will regularly fluctuate is therefore crucial. However, it is challenging to predict how hundreds of millions of consumers will behave and how their energy consumption will affect the aggregate electricity demand on a national level.
Generally, electricity consumption has to be evenly matched with electricity generation at all times to ensure a safe, stable supply. Still, the demand for and production of electricity fluctuates heavily depending on different hours, days of the week, months, etc., with supply not always coinciding with the electricity grid demand.
Stem Inc., a global leader in AI-driven energy storage, approached Endava to deliver a study on the Global Adjustment model and a pilot project creating a program that would predict peak electricity demands. This program would help to reduce energy costs by predicting the most cost-effective moment to buy electricity and enable efficient usage during the more expensive peak hours.
A custom-made solution
Together with Stem, we devised an AI roadmap to help create a proof of concept and ultimately deliver a custom-made solution for forecasting electricity demand.
Endava’s domain experts closely collaborated with Stem to evaluate the right approach and to recommend the most suitable data sources, algorithm and enhancements.
Thorough testing techniques were implemented throughout the project, mainly:
- Unit tests to ensure all code met quality standards before deployments
- Component tests to verify the input and output behaviour of the forecasting solution
- Integration tests to eliminate any faults in the interaction of integrated units
Cutting costs by 60%
The solution for forecasting electricity demand was made to improve Stem’s fixed assets efficiency by charging the batteries during off-peak time and using the stored energy during peak time when the difference in electricity price is highest.
By using batteries for electricity storage effectively, we'd created a model that could substantially lower the electricity cost for Stem’s customers. In some cases, overall electricity costs were lowered by 60%.
Through accurate forecasting, we could cut down the battery charge cycles and charging time, improving their lifespan, thus reducing servicing costs and potentially improving Stem’s profit margins.
Balancing the pressure on the transmission and distribution network during peak time could help reduce the costs of electricity production during this period and increase the infrastructure's longevity.
We also created more opportunity to optimise the grid storage technology, paving the way for a greater adoption of renewable electricity sources such as solar power plants, tidal energy and wind.
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