Predicting energy consumption levels in the district heating network
In close collaboration with Helen, our experts developed an AI solution that predicts energy consumption levels based on historical data and local weather forecasts, reducing errors by a third.
In close collaboration with Helen, the electricity and heat energy producer in Helsinki, we developed an AI solution that predicts energy consumption levels based on historical data and local weather forecasts, reducing errors by a third.
The AI-driven solution improves forecasting of district heat demand and makes production planning more efficient. The solution is in use in the capital area in Finland.
Reducing error rate between heat forecasts and actual demand by a third
The intelligent solution leverages historical forecasting data in addition to weather and time related data. The historical data covers both previous demand forecasts made and actual demand for the last two weeks. In addition, the solution adapts to new data and improves over time.
The intelligent application enables the planning of district heating production as close as possible to the actual demand. This is important in order to produce the right amount of district heat. Most of the houses in Helsinki are heated with district heating all year round. With the machine-learning based solution Helen can now better plan its heat production throughout the year.
Results
- Reducing heat demand forecasting errors by a third
- Improving foresight based on dynamic data with machine learning
- Better heat production planning
Read more in the press release.
Janne Inkinen
Advisor
,
Helen
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