Energy is the second largest operating cost for most water companies, after people. Treatment works - pumping raw water, running aeration systems, dosing chemicals, and distributing treated supply - are energy-intensive operations running continuously, in many cases with limited visibility of what is being consumed, where, and at what cost.

That lack of visibility is the core problem. Water companies have electricity bills. What most do not have is the analytical capability to decompose those bills into their underlying drivers, understand which processes and sites are performing inefficiently, identify where tariff structure is working against operational scheduling, and take targeted action to reduce spend. The data exists; the insight does not.

Energy usage and pricing analytics changes that. By building a clear, granular picture of consumption patterns, cost drivers, and tariff exposure across the treatment works estate, it gives operations and finance teams the information they need to drive meaningful, sustained reductions in energy spend - without compromising treatment quality or operational reliability.

The Energy Cost Landscape in Water Treatment

Water treatment and distribution typically accounts for between 25 and 40 per cent of a water company's total energy consumption. The largest single consumers are high-lift and service reservoir pumping stations, followed by aeration systems at wastewater treatment works, UV and ozone disinfection, and membrane filtration processes. The distribution of consumption across these processes varies by company, catchment geography, and treatment technology - but the aggregate cost is substantial and, under current energy market conditions, increasingly volatile.

Compounding the cost challenge is tariff complexity. Most large treatment works are on half-hourly metered contracts, with consumption charges that vary by time of day, network charging zones, and demand peaks. Triad charges - based on the three highest demand periods in the national grid across winter months - can add material cost to energy bills for sites that do not actively manage their load during peak periods. Reactive power charges, capacity charges, and climate levies add further layers.

Without granular analytics, navigating this landscape is largely guesswork. Procurement teams can negotiate competitive contract rates, but if the operational scheduling of energy-intensive processes is misaligned with tariff structure, a well-negotiated contract still produces an unnecessarily high bill.

Sector context:

The water sector in England and Wales spends an estimated £600 million per year on energy. Even a five per cent reduction across the sector would represent savings of £30 million annually - achievable, in significant part, through better data analytics and operational scheduling rather than capital investment in new technology.

What Energy Analytics Delivers in Practice

Effective energy analytics for treatment works operates across three interconnected layers:

Consumption visibility and baseline reporting

The foundation is a clear, site-by-site and process-by-process view of consumption - updated continuously from half-hourly meter data, SCADA systems, and sub-metering where available. This visibility alone often surfaces immediate opportunities: sites consuming significantly more energy per unit of output than comparable operations, processes running at times that attract peak tariff charges, and equipment drawing power outside of operational hours. For many water companies, building this baseline view is the first time consumption data from across the estate has been consolidated in one place.

Cost driver analysis and tariff mapping

Consumption data mapped against tariff structure reveals where the mismatches are greatest. Which sites are exposed to Triad risk and by how much? Which processes are running during peak charging windows when they could feasibly be shifted? What proportion of the energy bill is attributable to demand charges driven by short-duration peaks that could be smoothed? Answering these questions requires integrating consumption time series with tariff data in a way that most operational reporting does not currently do.

Operational scheduling optimisation

The most direct route to cost reduction is aligning the scheduling of flexible energy-intensive processes with the tariff profile. Pumping operations, dosing, backwashing, and UV treatment all have degrees of operational flexibility that can be exploited to shift load away from peak tariff windows - reducing Triad exposure, lowering demand charges, and in some cases enabling participation in demand-side response schemes that generate revenue rather than simply avoiding cost. Analytics provides both the insight and the operational decision support to make this scheduling optimisation systematic.

The Role of Benchmarking and Specific Energy

A critical metric in energy analytics for water treatment is specific energy consumption - the amount of energy used per unit of output, typically expressed as kWh per megalitres treated or distributed. Specific energy normalises for throughput variation, making it possible to compare performance across sites of different sizes, identify genuine inefficiency rather than scale effects, and track improvement over time.

Benchmarking specific energy across the treatment works estate - and against published sector comparators - gives operations leadership a ranked view of where the greatest efficiency gaps exist and where targeted investigation is likely to yield the highest returns. A site performing significantly above the sector median on specific energy is a priority for detailed process review; one consistently at or below median can be deprioritised.

Over time, specific energy trending at the process level also provides an early indicator of equipment deterioration. A pump whose specific energy consumption is creeping upward quarter-on-quarter is likely running less efficiently - a signal that predicts maintenance requirements before operational symptoms appear.

VE3 perspective:

Energy analytics programmes consistently demonstrate that the highest-value opportunities are not in the largest sites but in the sites with the greatest gap between actual and achievable specific energy. A medium-sized treatment works running at significantly above-median efficiency is often a more attractive target than the largest site on the estate - particularly where the root cause is scheduling practice rather than capital equipment condition.

Behavioural Change and Organisational Alignment

Data and analytics alone do not reduce energy costs. The insight has to translate into changed behaviour - by operational teams who schedule process activities, by site managers who monitor and respond to consumption patterns, and by procurement and finance teams who make tariff and contract decisions.

This is where many energy analytics programmes underdeliver. Dashboards are built, reports are produced, and then consumption patterns remain largely unchanged because the link between data and operational decision-making has not been made explicit enough to change habits.

Effective energy analytics programmes are designed with behavioural change as an explicit objective. This means:

1. Clear ownership: assigning energy performance accountability to specific operational roles with defined targets, not leaving it as a shared responsibility that belongs to no one.

2. Actionable reporting: designing dashboards and reports around the decisions operational teams actually make - shift scheduling, pump sequencing, maintenance planning - rather than the data that is easiest to collect.

3. Feedback loops: showing operational teams the cost consequence of scheduling decisions in near real time, so the connection between action and outcome is visible and motivating.

4. Escalation triggers: building alerts that flag anomalous consumption or tariff exposure to the right people at the right time, rather than burying the signal in a weekly report.

Demand-Side Response and Grid Flexibility

For water companies with sufficient metered capacity and operational flexibility, energy analytics opens the door to demand-side response (DSR) participation - agreements with grid operators or aggregators to reduce or shift load at times of grid stress, in exchange for financial payment.

Treatment works are well-suited to DSR participation in principle: they have significant flexible load (pumping, in particular), predictable operational constraints, and the ability to shift activity within defined windows without compromising service. The barrier has typically been the analytical and operational infrastructure required to participate reliably - knowing with confidence what load can be shifted, when, and for how long, without risking supply or treatment quality.

Energy analytics provides that confidence. A company with granular visibility of consumption by process and site, and a clear model of operational flexibility across its estate, can engage with DSR schemes from a position of accurate knowledge rather than conservative caution - and capture revenue that offsets energy costs further.

How VE3 Approaches Energy Analytics for Water Companies

VE3 Global delivers energy analytics programmes that connect operational data, tariff intelligence, and decision-support tooling into a coherent, actionable system. Our approach integrates half-hourly meter data, SCADA outputs, and asset registers into a unified analytics platform, with reporting designed around the decisions that drive energy cost - not simply the data that is available.

Our typical delivery scope covers:

‍1. Data integration and baseline build: consolidating consumption data across the treatment works estate, mapping against tariff structure, and establishing specific energy benchmarks by site and process.

2. Cost driver analysis and opportunity quantification: identifying the highest-value scheduling, operational, and procurement opportunities, with financial modelling of achievable savings.

3. Operational dashboard and alert design: building the reporting and alerting layer that puts energy insight in the hands of the people who can act on it, aligned to operational workflows.

4. Continuous monitoring and improvement: establishing the ongoing analytics capability that tracks performance against targets, updates as tariff structures change, and surfaces new opportunities as operational patterns evolve.

Conclusion: Visibility Is the First Step to Control

Water companies cannot manage what they cannot see. Energy spend at treatment works is a controllable cost - but only when the analytics exist to understand where it comes from, why it varies, and where it can be reduced. The investment required to build that visibility is modest relative to the cost base it acts on. For companies facing sustained pressure on operating expenditure, energy analytics is one of the clearest-return data investments available.

About VE3

VE3 is a UK-based technology and enterprise AI consultancy, partnering with water companies, regulated utilities, and public sector organisations to deliver AI, data, and digital transformation programmes that create measurable operational and commercial value. With offices in London and Pune, VE3 combines deep sector knowledge with cutting-edge AI capability to help clients navigate the full journey from data strategy to production AI deployment.

To know more, get in touch with us.