ForNet (FORecasting Services for Energy NETworks) is a collaboration project led by Professor Konstantinos Nikolopoulos from Durham University. The team is made up of Dr Yang Lu, York St John University and Dr Haoran Zhang, Imperial College London.
This project is looking at providing Forecasting services for the DAFNI platform combining quantitative
time series methods (Professor Nikolopoulos), judgmental/behavioural methods/adjustments (Dr Lu) and treatment for extreme events (Dr Zhang).
The project identifies a critical gap in current energy demand forecasting models: the lack of consideration for human behaviour and cognitive biases. By integrating insights from behavioural science into quantitative and judgmental forecasting methods, the project aims to develop more nuanced and accurate models that reflect real-world energy use patterns. This involves a multi-faceted methodology that includes collecting and analysing data on energy consumption, renewable energy adoption, and weather conditions, alongside behavioural data from UK surveys and studies. The project will leverage collaborative efforts, drawing on expertise from various fields to identify, quantify, and adjust for cognitive biases in energy demand models.
ForNet deals both with the demand end, the supply end and in between (forecast reconciliation), these forecasts are then fed to decision support systems – like network optimisation (but this is a follow up project) . The team will also provide some datasets that they will collect through the project.