Stop Your Grid from Overloading: Predict Future Energy Demand with Customer Profiling
Exceeding network capacity is a growing concern with the rise of low-carbon technologies, so understanding where this demand will come from is critical. While macro forecasting like Distribution Future Energy Scenarios (DFES) give an overall view of adoption trends across the network, they often miss the crucial house-by-house and street-by-street details. It’s those micro-level hotspots that can catch your network off guard.
The good news is that you already have the data that reveals what’s at the end of every connection. The challenge lies in piecing it all together to form a consumer profile that’s both insightful and useful.
How Can You Know Your Customers Without Knowing Their Name?
Even without individual-level data, robust profiles can be built using the aggregated and anonymised datasets you already have access to.
Spatial Data
Spatial datasets are essential for uncovering key relationships within your network. For instance, the Ordnance Survey National Geographic Database (OS NGD) can be leveraged to identify spatial connections between neighbourhoods, substations, and other critical assets.
General property data is a valuable indicator for forecasting future demand. For example, terraced homes are typically less likely to install heat pumps due to space constraints, while properties with off-street parking are more likely to install electric vehicle chargers. Similarly, south-facing, lower-pitched roofs tend to be more suitable for solar panel installations.
You can get even more granular as OS NGD can provide you with rich detailed information about each building that connects to your network i.e., the number of floors in each building to understand consumption, building age to understand energy efficiency, and square footage to help model power demand.
Demographic Data
Census figures, Experian insights, and the Index of Multiple Deprivation provide valuable perspectives on customer behaviour. This data helps define archetypes, gauge adoption propensities, and forecast demand.
For example, middle-income areas are increasingly adopting electric vehicles and heat pumps. Additionally, government incentives and impending regulations—such as energy efficiency requirements for rental properties—are accelerating the uptake of low-carbon technologies in certain areas.
Operational Data
Operational data, when integrated with spatial information, provides an additional layer of insight. This includes details about substation and bearer capacity, substation metering, and aggregated smart meter data collected at each home (available as half-hourly data under Ofgem-approved privacy plans).
The Challenge: Making Sense of the Data You Have
Bringing together disparate datasets is no small feat, and spatial data, in particular, can be notably challenging. Unlike more standard data types, spatial data often comes in varied formats that have their own complexities and nuances.
FME, which you already have access to, is invaluable in this context, as it helps to collate and transform messy datasets.
Beyond format differences, spatial data also presents challenges with temporal mismatches. For example, while aggregated smart meter data may update every half-hour, spatial data linked to property or census information is often refreshed annually.
By standardising your data sources, you create a clean, structured dataset that is primed for advanced analytics and AI machine learning, ultimately revealing complex patterns and trends that might otherwise go unnoticed.
Stay ahead of demand
Without thorough customer profiling, planning for network capacity risk is impossible. Although personal data may not always be accessible, there is an abundance of aggregated, property-based, and spatial data available.
When integrated effectively using tools like FME, this data provides a holistic view of your customers and their behaviours. By adopting a forward-thinking approach to customer profiling, you can proactively manage emerging network risks.