The water and wastewater sectors of England and Wales (E&W) are energy-intensive. Although E&W’s water sector is of international interest, in particular due to the early experience with privatisation, for the time being, few published data on energy usage exist. We analysed telemetry energy-use data from Thames Water Utilities Ltd. (TWUL), the largest water and wastewater company in the UK, which serves one of the largest mega-cities in the world, London. In our analysis, we: (1) break down energy use into their components; (2) present a statistical approach to handling seasonal and random cycles in data; and (3) derive energy-intensity (kWh m⁻³) metrics and compare them with other regions in the world. We show that electricity use in the sector grew by around 10.8 ± 0.4% year⁻¹ as the utility coped with growing demands and stormwater flooding. The energy-intensity of water services in each of the utility’s service zone was measured in the range 0.46–0.92 kWh m⁻³. Plans to improve the efficiency of the system could yield benefits in lower energy-intensity, but the overall energy saving would be temporary as external pressures from population and climate change are driving up water and energy use.

Digital connectivity is an essential infrastructure service, as more and more people and machines are connected to the internet. Mobile internet access is one of the cheapest ways to connect wirelessly, and new technologies (particularly 5G) can offer access speeds which can rival fixed broadband connections. Successive generations of mobile telecommunication technologies (3G-5G) have increased data transmission rates and reduced latency. However, user-generated data traffic has been growing exponentially over the past decade which is concerning for both mobile network operators and policy decision-makers, with future growth forecasts indicating this trend is likely to continue (Oughton et al., 2018).

Infrastructure systems, such as those that generate and transmit energy, process waste water and enable the transportation of people and goods, provide fundamental services that underpin modern society. The simulation model integration framework (smif) is an open source Python package which supports the coupling and running of infrastructure simulation models as a system-of-systems. smif connects individual simulation models-written in different programming languages and using different methods of data input and output-allowing users to compose a system-of-systems with explicit configuration of the connections and data transformations between models. Users can then run system-of-systems models using various data sources in order to explore the effects of uncertainty in external factors and model parameterisations, and to test alternative approaches to long-term decision-making and planning across the connected models.