Learnings

Outcomes

The project demonstrated that the weather has a strong influence on electricity demand at a local level and that there is a large degree of local variability that cannot be quantified by a system level correcting. It investigated the historical trends in customer behaviour at individual substations by correcting the varying annual peak demand to account for the effect of variable weather conditions.

Key findings of the project are as follows:

• Weather is masking underlying demand trends
• There is a clear relationship between demand and multiple weather variables including effective temperature, global horizontal irradiance and the cooling power of the wind.
• The effect of weather conditions at local substations will be different due to a combination of customer sensitivity to weather and local climate effects.
• The variability of weather driven demand at a local level is significant.

Understanding demand at this level of granularity is a key enabler for DSO and microgrids. It provides tools to improve the targeting of investment, provides earlier warning of shifts in demand and provides a platform to run scenarios upon.

Lessons Learnt

The project has created some learnings for the business for future projects; the outputs of the project have been used to better inform operational dynamic thermal ratings, which allow the network assets be used more effectively, particularly when combined with other forecasting tools and data.

In addition, there have been lessons to be learned for the prediction of future energy scenarios by providing a more accurate insight into how weather variance has affected demand over time, while highlighting the significance of weather effects in that area.