Data science to investigate temperature profiles of large networks of food refrigeration systems

The electrical generation and transmission infrastructures of many countries are under increased pressure. This partially reflects the move towards low carbon economies and the increased reliance on renewable power generation systems. There has been a reduction in the use of traditional fossil fuel generation systems, which provide a stable base load, and this has been replaced with more unpredictable renewable generation. As a consequence, the available load on the grid is becoming more unstable. To cope with this variability, the UK National Grid has placed emphasis on the investigation of various technical mechanisms (e.g. implementation of smart grids, energy storage technologies, auxiliary power sources), which may be able to prevent critical situations, when the grid may become sometimes unstable. The successful implementation of these mechanisms may require large numbers of electrical consumers (e.g. HVAC systems, food refrigeration systems) for example to make additional investments in energy storage technologies (food refrigeration systems) or to integrate their electrical demand from industrial processes into the National Grid (HVAC systems). However, in the situation of food refrigeration systems, during these critical situations, even if the thermal inertia within refrigeration systems may maintain effective performance of the device for a short period of time (e.g. under 1 minute) when the electrical input load into the system is reduced, this still carries the paramount risk of food safety even for very short periods of time (e.g. under 1 minute). Therefore before considering any future actions (e.g. investing in energy storage technologies) to prevent the critical situations when grid becomes unstable, it is also needed to understand during the normal use how the temperature profiles evolve along the time inside these massive networks of food refrigeration systems.