The three options, myth-free

Public cloud (the platform hosted on the vendor’s infrastructure, in European data centres). What you get: immediate deployment, scaling without capital investment, continuous updates, and a level of physical and operational security that a medium-sized company rarely matches on its own. What it demands: contractual trust in the vendor — data jurisdiction, encryption, access, incident notification — and outbound connectivity from the plant.

Your private cloud (the platform deployed in the cloud environment your company already contracts and governs — your tenant, your policies, your IT team in charge). What you get: the governance and compliance control of the previous option without giving up elasticity. What it demands: an IT team capable of operating that environment and a vendor whose platform can be deployed there — not all of them can.

Your own servers (on-premises). What you get: the data never leaves your perimeter, no dependence on external connectivity for plant operations, and the simplest answer to sovereignty audits or critical-infrastructure requirements. What it demands: hardware, maintenance, backups, patching and continuity, all your responsibility — and the honesty to admit that a well-run in-house server room is more expensive and more demanding than it looks.

The myth to debunk is that one of the three is ‘the secure one’. Security is not about location: it is about management. An unpatched on-premises system is more vulnerable than a well-run cloud; a cloud without access control is more vulnerable than a well-locked-down in-house server.

The five deciding criteria

1. Data classification. What is on the platform? Load profiles and costs are one thing; if it also integrates process data from SCADA, recipes or production parameters, sensitivity rises and the decision leans towards keeping control in-house.
2. The regulatory framework that applies to you. If your company is an essential or important entity under NIS2, critical infrastructure, public administration or in the defence sector, sovereignty and continuity requirements usually decide the answer for you. If not, you have more leeway.
3. Your IT team’s real capacity. On-premises and private cloud mean running infrastructure; public cloud means governing a contract. The option your team can sustain in year three is better than the one that is perfect on paper.
4. Plant connectivity. If your sites have poor connectivity or policies that isolate the OT network, the architecture needs local (edge) components regardless of where the central platform lives.
5. Growth horizon. One plant today, five sites in three years, gas and thermal integration further down the line: the option you choose must be able to scale and, if it comes to it, migrate without redoing the project.

Questions to ask the vendor

Whichever way you lean, four questions separate the vendors you can build with from those you can’t:

• Can the same platform be deployed in all three modes? If it only exists in one, you are not choosing: the choice is being made for you.
• What contractual guarantees does it give on jurisdiction, encryption, access and incident notification? — the four questions that we already raised in June (in Spanish) are still the test.
• How does it integrate the OT network without exposing it? On-site gateways, encrypted outbound communication, segmentation.
• What happens if I want to change deployment mode in two years’ time? The answer says more about the vendor than any brochure.

The PowerCloud view

PowerCloud’s position is the one the method above calls for: the same platform, deployable wherever your security requirements demand — in a European public cloud, in your private cloud or on your own servers — with plant integration solved in any of the three cases. Companies that start in the cloud and grow into their own private cloud as their IT team matures; organisations with sovereignty requirements that start on-premises and add sites without changing tools. The whole organisation’s energy data in a single view — hosted wherever it needs to be. We grow with you, not the other way round.

Are you clear on where your company’s energy data should live — and whether your current platform could move if it needed to? We help you work through the five criteria for your specific case: the data you handle, regulatory framework, IT capacity, plant connectivity and growth horizon. You come away with a reasoned recommendation, not a preference. Diagnostic session: 30 minutes, no obligation.

Sources: CyberSecurity News and S2 Grupo (IT/OT convergence and security in IIoT/edge environments); enerTIC (Smart Energy Congress 2026 agenda: OT cybersecurity, data governance); NIS2 framework (INCIBE, Spain’s National Cybersecurity Institute); previous posts in this series at powercloud.es/noticias/.

La entrada Where to host your energy management platform: your private cloud, your own servers or the public cloud — how to decide se publicó primero en Powercloud | Energy monitoring and management.

This is how you end up with energy silos: data that exists but remains separate, making it hard to understand how much each process costs, why consumption changes or where action is worth taking.

The Smart Energy Congress 2026, organised by enerTIC, will take place on 14 and 15 October at IFEMA Madrid. Its agenda covers digitalisation, IT/OT integration and the use of data to improve energy competitiveness. For industrial companies, that debate starts with a practical question: can we link the information we already have to manage our energy better?

What an energy silo is, with examples from the plant

An energy silo is a source of information on consumption, costs or operations that is difficult to relate to the others. The separation may be due to incompatible systems, access permissions, different formats or unclear responsibilities.

In an industrial plant, you may find side by side:

• The main meter, with the load curve available through the distribution company or Datadis (the Spanish distributors’ consumption-data platform), depending on the supply and the data accessible.
• The plant supervision and control system (SCADA), with temperatures, machine states and other operating variables.
• Power analysers and submeters, which record the consumption of distribution boards, lines or equipment.
• Gas, boiler and industrial refrigeration records, with their own control systems.
• Invoices, production and the operating calendar, spread across administration, production and other departments.

Each system may work properly. The problem arises when cross-referencing their data requires exports, emails and spreadsheets every time someone needs an answer.

With metering by machine and process we can identify what is consuming energy. To interpret that consumption, we also need to know what was being produced, for how long and under what conditions.

What energy silos cost

Their cost shows up in three places: the opportunities that go unnoticed, the work of rebuilding information and the time it takes to respond.

Decisions that are never even considered. If consumption and price remain separate, it becomes harder to calculate the real energy cost of each process. If production and energy are not linked, a line that manufactures more may look less efficient simply because it consumes more.

Work that keeps being repeated. Preparing information for senior management, an audit or a formal dossier may mean exporting from several systems, reconciling dates, checking units and finding gaps. When that process is not organised, it has to be rebuilt for every review.

Deviations spotted too late. Discovering a piece of equipment running outside operating hours when the bill arrives leaves less room to respond than identifying it during operation. To make the most of the data, you need a suitable update frequency, a threshold and someone responsible for taking action.

That is why industrial energy efficiency indicators need consistent information and context. A chart can show the change; explaining its cause requires relating the variables that drive it.

How to connect IT and OT without creating another silo

IT refers to the information technologies that run the business: applications, databases and corporate systems. OT refers to the technologies that monitor or control physical machines and processes.

Integrating both environments makes it possible to relate what happens on the plant floor to its energy and financial consequences. Doing it well means reviewing three aspects.

• Genuine compatibility. Industrial protocols, application programming interfaces (APIs) and gateways make connection easier. But sharing a protocol does not guarantee that all the data is accessible or that it means the same thing. You need to check equipment, versions, permissions, documentation and any licensing requirements.

• Data quality. A platform can receive information from several systems and still show incorrect comparisons if it mixes units, intervals or dates. Integration must identify gaps, duplicates and changes in measurement criteria.

• Security and operational continuity. The NIST OT security guide addresses the particular needs of these environments. Access to information must respect plant availability and security, with appropriate permissions and communications.

Local processing, known as edge computing, can help collect and transform data close to the equipment. Some solutions can also store data during a communications outage. How useful it is depends on the architecture and on the functions that have been planned.

The question when choosing a platform should be specific: which sources at our site can you integrate, how do you prove it and what limitations are there?

What companies can do before the Smart Energy Congress

The first step is to locate the information available and choose a decision worth resolving. That inventory lets you approach suppliers, production and maintenance with specific questions.

With that preparation, the conversation changes: these are our sources, this is the decision we need to make and these are the criteria for checking whether the integration works.

A well-defined first case also helps you assess what is worth extending to the rest of the plant.

The PowerCloud view

PowerCloud brings together energy and operational information so you can analyse consumption, costs and performance in a shared environment. Integration starts from the equipment, systems, access and communications available at each site.

By relating the overall load curve to process-level measurements and activity variables, a company can compare lines, pinpoint deviations and keep a history to review what happened before and after an intervention.

Update frequency and scope are tailored to the analysis you want to carry out. The aim is for production, maintenance and senior management to work with consistent information and be able to check the outcome of their decisions.

The Smart Energy Congress will put these connections between technology, operations and efficiency on the table. For each company, the practical step starts at its own site: identifying which information is still kept separate and which decision could improve by connecting it.

La entrada Energy silos: the challenge of connecting data at the Smart Energy Congress 2026 se publicó primero en Powercloud | Energy monitoring and management.

The king of indicators: specific energy consumption

The starting point for almost all industrial energy management is specific energy consumption: energy per unit produced — kWh per tonne, per part, per batch, per square metre treated. It is the sector’s EnPI par excellence because it speaks the factory’s language: it relates energy to what the plant exists to do.

Below it sit the process-specific indicators that submetering makes possible: the oven’s consumption per tonne baked, the compressor’s per cubic metre of air delivered, refrigeration’s per hour of cold store held at set point. And, with yesterday’s instalment, its version in euros: energy cost per unit produced — the figure a management committee understands without translation.

The four mistakes that invalidate an indicator

Most EnPIs that fail don’t fail for lack of data: they fail by design. Four patterns keep recurring.

1 · Comparing without normalising. January’s kWh/tonne is not comparable with August’s if the plant is heated in winter, nor is a month at full load comparable with one at half load. A serious indicator is normalised for its explanatory variables —production, outdoor temperature, product mix, operating hours— just as required by the baseline of a CAE (Spain’s Energy Savings Certificates) application (in Spanish). Without normalisation, the indicator measures the calendar, not efficiency.

2 · Ignoring partial load. Almost no process consumes proportionally: an oven at half load uses far more than half of what it uses at full load. Specific consumption gets worse when production falls, even though nobody is operating any worse — and improves when production rises, even though nobody is operating any better. Anyone who doesn’t know this ends up praising and scolding the production team for commercial decisions.

3 · Changing the formula without leaving a trail. An EnPI that is recalculated differently every quarter —a different boundary, a different unit, a different allocation criterion— cannot demonstrate improvement to anyone: not to management, not to the ISO 50001 auditor, not to a verifier. Methodological consistency is worth more than sophistication.

4 · Publishing it late. A monthly indicator that arrives on the 20th of the following month describes history. The same indicator, daily or weekly, on the production whiteboard, drives behaviour — which is its only real purpose.

The minimum scorecard that works

For a medium-sized industrial plant, experience keeps showing the same pattern: four or five well-chosen indicators beat forty.

1. Plant-wide specific consumption (kWh/unit), normalised, weekly — the big picture.
2. Specific consumption of the dominant process, daily — where the game is won or lost.
3. Non-production consumption (nights, weekends, shutdowns) — the phantom-consumption detector, and the cheapest saving there is.
4. Energy cost per unit (€/unit, from yesterday’s instalment) — the one you take to the committee.
5. If ISO 50001 applies or there is a CAE application on the horizon: the EnPIs for the significant energy uses required by the standard, with their documented baseline — as we saw when explaining what the auditor asks for (in Spanish).

Each one with an owner, an alarm threshold and a fixed review time. An indicator without those three things is just a chart.

The PowerCloud view

EnPIs almost always die of maintenance: someone has to gather consumption, production and temperature data every week, recalculate, normalise and publish — and by the third month that someone has another urgent priority. PowerCloud keeps them alive: it calculates the indicators from the utility meter and submeter readings, normalises them by the variables you define, alerts you when they go outside their thresholds and keeps the historical series with the methodological consistency that an auditor —or your own committee— will want to go through. The production whiteboard is still the whiteboard; what changes is that nobody has to put together the number written on it by hand any more.

That brings the series to a close: measure by process, cost in real euros, manage by indicators. Three steps, each useless without the one before — and the third is the only one visible from the boardroom.

How many energy indicators does your plant have today — and how many are actually reviewed every week? We help you set up the minimum scorecard: which four or five EnPIs fit your process, which variables to normalise them by and how to keep them alive without anyone recalculating them by hand. We start from your real load profile — we obtain it from Datadis (the official platform of Spain’s electricity distribution companies) with your authorisation. Diagnostic session: 30 minutes, no obligation.

Sources: UNE-EN ISO 50001:2018 (EnPIs and significant energy uses); QUAMA and Gradhoc (industrial energy performance indicators); Cuerva and Apolo Energies (specific consumption and industrial practice); previous posts in this series at powercloud.es/noticias/.

La entrada Energy efficiency indicators in industry: the ones a committee understands and the mistakes that invalidate them se publicó primero en Powercloud | Energy monitoring and management.

Why the average price lies

The average price on your bill is the result of when the plant as a whole consumed. Applying it equally to every process assumes they all consume with the same hourly profile — and that is almost never true.

An example with round numbers. A plant with two processes consuming 100,000 kWh a month each: process A runs from 8:00 to 18:00 (solar hours, low prices); process B is a night shift with an evening peak (expensive hours). With an average bill price of €110/MWh, traditional accounting allocates €11,000 to each. Using the actual prices for each time slot, process A may have cost €7,500 and process B €14,500 — one nearly double the other, with identical consumption.

The consequences are not cosmetic: the product cost sheet for what B makes is understated (and A’s is penalised); efficiency investment is poorly prioritised, because a kWh saved isn’t worth the same in A as in B; and load shifting — the star lever of the quarter-hourly market — doesn’t even appear in the analysis, because the average price wipes it out by construction.

The right formula, layer by layer

The real cost of a process is built from three ingredients, from coarsest to finest:

1 · Its load profile — from submetering if you have it; if not yet, an estimate based on operating hours is the honest first step (and the reason to install metering wherever the load is significant).

2 · The price of each time slot. For indexed contracts, the quarter-hourly price from OMIE (the Iberian electricity market operator) plus the regulated components; for fixed contracts priced by period, the price of each tariff period P1–P6. In both cases, the energy charge stops being an average and becomes a sum: consumption in each quarter-hour × its price.

3 · The allocation of power and fixed costs. The capacity (power) charge is allocated not by kWh but by contribution to peak demand: the process that is running when the plant hits its maximum is the one that sizes the contracted power — and its excess power charges. Submetering tells you who was consuming in the quarter-hour of the monthly peak; without it, this allocation is impossible and the cost tends to be loaded unfairly onto the largest energy consumer, which is not always the one that sets the peak.

With all three layers, each process gets its cost in euros per month and per unit produced — and that second figure is the bridge to the third instalment of this series: efficiency indicators, on Friday.

What changes when the cost is real

Product cost sheets get corrected — sometimes by several percentage points for electricity-intensive products, enough to review pricing or the production mix.

Investments get re-prioritised: a battery, load shifting or heat recovery (in Spanish) are assessed against the cost of the hours they actually avoid, not against an average that dilutes them.

Internal conversations change tone: telling production ‘the plant uses a lot of energy’ changes nothing; showing them that their night shift costs twice as much per kWh as the morning shift opens a concrete negotiation about schedules and setpoints.

The PowerCloud view

Done by hand, this calculation is a heroic spreadsheet that gets abandoned by the second month. PowerCloud does it continuously: it cross-references the main meter and the submetering data with the actual quarter-hourly price in your contract, allocates the capacity charge by contribution to peak demand, and publishes the cost per process, per area and per unit produced — every day, without anyone having to rebuild it. Energy accounting stops being an annual estimate and becomes just another view of operations.

Do you know what your most important process really costs — in euros, not in kWh? We’ll calculate it for you using your actual load profile and your contract prices, and show you the difference compared with the average-price allocation you are probably using today. If you don’t have submetering yet, we start with the main meter’s load profile — we obtain it from Datadis (the official platform of Spain’s electricity distribution companies) with your authorisation. Diagnostic session: 30 minutes, no obligation.

Sources: OMIE (quarter-hourly prices); previous posts in this series and on the blog at powercloud.es/noticias/; Schneider Electric and Linkener (energy accounting practice with submetering).

La entrada How to calculate the real energy cost of each process: from the meter and submetering to euros per line se publicó primero en Powercloud | Energy monitoring and management.

What the main meter can’t tell you

The smart meter on your supply (in Spanish) is a great starting point: a quarter-hourly load profile, free of charge and already installed. But it gives you a single figure for the whole plant. It tells you that at 11:15 yesterday you were drawing 480 kW; it doesn’t tell you whether that was the compressors, the furnaces, the refrigeration or a valve left open that nobody spotted.

That blind spot has concrete consequences: phantom (idle) consumption at weekends and during shutdowns stays hidden within the total; two identical lines that consume differently (in Spanish) can’t be compared; and any efficiency project — or its application for CAEs (Spain’s Energy Savings Certificates) — lacks the measurement boundary that a verifier requires.

What submetering means in practice

It means installing secondary metering devices — panel-mounted power analysers, current clamps, line meters — in the areas, processes or machines that carry real weight. You don’t need to cover the entire plant: the rule of thumb is to start with the significant energy uses — the three to six points that account for the bulk of consumption or of uncertainty.

The technology has matured and become cheaper: IIoT (Industrial Internet of Things) devices that speak the usual plant protocols—Modbus, BACnet, M-Bus, even over Wi-Fi and/or LoRaWAN radio where cabling is expensive—with panel-by-panel installation that rarely requires a shutdown. For an industrial SME, basic metering by line or process has gone from being an engineering project to a job of a few days.

The savings ranges documented by the industry when moving from a main meter to process-level metering run from 20% to 30% of consumption in facilities with no prior monitoring — not because measuring saves anything in itself, but because of what shows up when you look: idle consumption, equipment running outside operating hours, performance drift, compressed-air leaks, forgotten setpoints.

Where to start: the five points that almost always pay for the installation

1. Compressed air — the king of leaks and idle consumption; almost always the first finding.
2. Industrial refrigeration and HVAC — big consumers running on schedules that nobody reviews.
3. The line or process with the greatest weight in production — the basis for the cost per unit produced that we’ll look at in the second instalment.
4. Duplicated processes — two furnaces, two lines, two cold rooms: metering both turns comparison into a method.
5. The main feeder of each building or area — so the total always adds up and any gaps come to light.

With those points metered, every new reading is cross-referenced with the main meter’s load profile and the market price — and that’s where the second instalment of this series begins: what each process really costs, which we publish on Wednesday.

The PowerCloud view

Submetering without a platform is just a collection of little screens on electrical panels. PowerCloud integrates the secondary measurements — via standard plant protocols — alongside the main meter’s load profile and the quarter-hourly price in your contract, and turns them into what actually gets used: comparisons between processes, alerts for consumption that breaks its usual pattern, detection of phantom loads during shutdowns, and the documented measurement boundary that any submission — CAEs, ISO 50001, grants — will sooner or later require. Metering by machine is half the job; having someone look at the data every day is the other half.

Does your plant have a single meter for everything? It’s the most common starting point. We’ll help you design a minimum submetering map — which three to six points to meter first, with what equipment and what return to expect — using your actual main-meter load profile as the basis.

La entrada Industrial submetering: the meter tells you how much your plant consumes — machine-level metering tells you who se publicó primero en Powercloud | Energy monitoring and management.

What has changed to make the numbers add up

Until recently, industrial batteries were a hard investment to justify: expensive, with an ambiguous regulatory framework and with moderate spreads. In 2026, all three conditions have changed at once.

The spread has soared, and it is structural. Solar PV is sinking midday prices —this summer there have been days close to zero— while the evening peak is set by gas-fired combined-cycle plants, under strain from the Middle East crisis. The duck curve we have been following on this blog is not an anomaly: it is how the system now works with ever more solar. And the quarter-hourly market, in operation since May, makes the troughs and peaks even more visible and easier to exploit.

Battery costs keep falling. A complete commercial and industrial system (from 100 kWh to 10 MWh, including inverters and management system) currently sits in the range of $200-350 per installed kWh, with lithium iron phosphate (LFP) chemistry dominating on cost and safety. Cells keep getting cheaper, and the first sodium battery factory in Zaragoza looks set to push prices down even further.

The regulatory framework has turned favourable. Spain’s Royal Decree-Law (RDL) 7/2026 introduced three specific measures for storage:

• Flexible grid access: access and connection permits for storage become flexible from the demand side, making use of grid margins that the traditional firm model left unused.
• Free depreciation: a company can depreciate its investment in renewables-linked storage on an accelerated basis —up to 100% in a single year— subject to a ceiling of €500,000 a year.
• Exemption from charges for storage facilities that move from firm to flexible access.

And there is a fourth piece for the future: the capacity mechanism approved by the European Commission on 29 May, with funding of up to €9 billion for 2026-2036, technology-neutral and explicitly including batteries. It is an additional revenue stream that does not yet exist in practice, but it will exist during the lifetime of any battery installed today.

How an industrial battery earns its keep (not just through arbitrage)

The usual mistake is to assess a battery on a single source of value. Well-managed installations stack several — what the industry calls revenue stacking:

A battery that only does arbitrage may end up with a modest return. One that combines three or four of these sources can double or triple the return. Industry estimates for industrial users put the achievable reduction in energy costs in the range of 15-30% for suitable profiles.

The part almost nobody talks about: a battery is only as good as its operating strategy

This is the nuance that separates profitable installations from disappointing ones. A battery does not decide on its own when to charge and when to discharge. It needs an operating strategy that, every day —every quarter of an hour, in fact— answers a question with several variables: do I buy from the grid now, draw on the battery, or hold the charge for a better peak?

That decision depends on things that change constantly: tomorrow’s quarter-hourly price (published the day before), the forecast of your own consumption, expected solar output if you have any, the current state of charge, the degradation each cycle causes, and your forecast power peaks. Running the battery on ‘fixed rules’ —always charging at 14:00, always discharging at 21:00— captures only part of the value and, on some days, actually loses money.

In practice, this turns the battery into a problem of data and optimisation, not just of electricity: you need the plant’s actual consumption profile, market prices at their current granularity, and a system that cross-references the two and decides —or recommends— each day’s operation.

What companies can do: the right order

1 · Before asking for quotes, measure. The right battery size comes from the plant’s actual load profile —twelve months, at hourly or, better still, quarter-hourly granularity— not from the contracted power or the bill. Oversizing is the quickest way to ruin the return.

2 · Simulate with your data, not with averages. The same 500 kWh system can deliver an excellent return at a plant with an evening peak and a mediocre one at a plant with flat night-time consumption. The simulation that matters cross-references your actual profile with last year’s actual prices: how much you would have saved, with what charging and discharging pattern, and with how many cycles.

3 · Make the most of the tax window. Free depreciation under RDL 7/2026 applies to investments made in 2026. For a €300,000 investment, bringing all the depreciation forward into the first year has a financial impact that can swing the decision — and it has a deadline.

4 · Design the operation from day one. Buying the battery without defining who will operate it and how means buying half an asset. The operating strategy —and the system that executes it— should be part of the project, not an afterthought.

The PowerCloud view

PowerCloud addresses exactly the two phases in which battery projects win or lose on profitability. Before investing: by cross-referencing your actual load profile with quarter-hourly prices from OMIE (the Iberian electricity market operator), we simulate what batteries of different sizes would have done over the last twelve months — savings from arbitrage, peak shaving, use of surplus solar — so that the sizing comes from your data and not from a catalogue. After installation: the platform tracks day-to-day operation, compares actual behaviour with the theoretical optimum and detects when the charging and discharging strategy is leaving money on the table.

The spread is out there, every day, published at 13:00 the day before. The difference between capturing it and watching it go by is having the data and the decision connected.

Would you like to know what a battery would have saved at your plant over the last twelve months? It is a calculation we run with your actual consumption profile and OMIE prices for the period — no catalogue assumptions, just your numbers. If you have not downloaded your profile, we obtain it from Datadis (the official platform of Spain’s electricity distribution companies) with your authorisation. The result gives you the right sizing or, just as valuable, the conclusion that it does not pay off for you yet. A diagnostic session lasting 30 minutes, with no obligation.

Sources: OMIE; Konery (hourly curve, June 2026); EDP (industrial BESS guide); BESSolutions (2026-2036 capacity mechanism); Bird & Bird (storage measures in RDL 7/2026); cntepower and LZY (2026 BESS costs by segment); Merca2 (July 2026 prices).

La entrada On-site industrial batteries: when to buy from the grid and when to draw on your storage se publicó primero en Powercloud | Energy monitoring and management.

Factors such as energy price volatility, European regulatory pressure, sustainability targets and the need to improve operational efficiency are changing the role of energy within organisations.

Against this backdrop, more and more companies are adopting energy management systems (EMS) that make it possible to monitor, analyse and control energy consumption continuously.

The aim is no longer simply to know how much energy is consumed.

The aim is to understand the organisation’s energy behaviour and make decisions that improve efficiency and reduce the financial impact of energy consumption.

What is industrial energy management?

Industrial energy management involves monitoring, analysing and optimising an organisation’s energy consumption in order to improve operational efficiency, reduce energy costs and meet sustainability targets.

In practice, it means collecting energy data, analysing it and using it to make decisions that improve the energy performance of the company’s facilities, production processes and operations.

From energy as a cost to energy as a strategic variable

Energy has not always held this position within companies.

Many experts place a turning point in 1973, when the oil crisis caused a sharp rise in energy prices and forced many industrialised countries to rethink their relationship with energy.

From then on, energy efficiency policies and energy management programmes began to be developed in industry.

Decades later, the publication of the international standard ISO 50001 in 2011 consolidated this approach by establishing a global framework for managing energy within organisations as a structured process of continual improvement.

Today, energy management is increasingly seen as an integral part of business management.

The new energy landscape for industry

Industry now operates in a far more complex energy environment than it did a decade ago.

The main factors driving the development of energy management systems include:

• energy price volatility
• pressure to improve energy efficiency
• decarbonisation targets
• European regulation on efficiency and sustainability.

In Europe, for example, the European Union’s Energy Efficiency Directive requires many companies to carry out periodic energy audits or implement energy management systems to monitor and improve their energy use.

This context has accelerated the adoption of digital platforms capable of providing visibility and control over energy consumption.

What is an energy management system (EMS)?

An energy management system (or EMS) is a platform that collects and analyses energy data to improve an organisation’s energy efficiency.

These systems help companies understand how energy is consumed across their facilities, identify deviations and make data-driven decisions to optimise their energy consumption.

Having energy data does not mean managing energy

Despite this technological progress, many organisations still face a common problem. They have energy data. But that data does not always translate into decisions.

In many industrial companies, energy information:

• is scattered across different systems
• is analysed only occasionally
• or is simply reviewed when the energy bill arrives.

In that scenario, energy is observed but not managed. And that difference is key.

Because energy management is not just about measuring consumption, but about understanding what is happening and acting before the impact reaches the business.

How energy management evolves within a company

In many organisations, energy management evolves gradually as analysis tools and processes are introduced. This process usually goes through three levels:

What is energy control?

Energy control means using real-time energy data to detect deviations, anticipate financial impacts and make decisions that optimise an organisation’s energy consumption.

Energy management platforms such as PowerCloud make it possible to centralise energy data from different sites, analyse it continuously and detect deviations before they have an operational or financial impact.

The platform offers a range of specialised services, such as EnergyControl, a service designed to help organisations understand, control and optimise their energy consumption.

EnergyControl enables you to analyse electricity consumption in real time, identify deviations from energy targets and anticipate the impact of consumption before it shows up on the bill or affects operations.

From energy analysis to decision-making

The real value of energy management lies not in collecting data, but in turning that data into useful information for decision-making.

When an organisation has visibility over its energy consumption and tools to analyse its behaviour, it can start to answer key questions:

1. Which processes consume the most energy?
2. Which sites show deviations from energy targets?
3. How does energy consumption affect operating costs?

This type of analysis means energy stops being merely an operating cost and becomes a variable that can be actively managed within the business.

In this way, energy analysis is no longer a review of the past but becomes a tool for making decisions that improve the company’s efficiency and energy performance.

A matter of business competitiveness

Industrial energy management is no longer limited to one-off efficiency initiatives.

More and more companies are building energy into their management and planning processes, recognising that energy consumption directly affects the organisation’s competitiveness.

When a company is able to analyse its energy behaviour and make data-driven decisions, it gains clear benefits:

• lower energy costs
• improved operational efficiency
• a greater ability to anticipate deviations
• better preparedness for regulatory or energy market changes.

In this context, energy is no longer just an operating cost and becomes a strategic variable within business management.

Organisations that develop energy control capabilities not only improve their efficiency but also strengthen their ability to adapt in an increasingly complex energy environment.

The question many companies are starting to ask

As the energy environment becomes more complex, many organisations are starting to ask themselves a simple question. It is not how much they consume. It is something more fundamental.

Are we really managing our energy…
or simply watching what happens?

La entrada Industrial energy management: from consumption to control se publicó primero en Powercloud | Energy monitoring and management.

1. More efficient data handling: an automated energy management system greatly reduces the time spent collecting and analysing data. In Excel, data is entered and manipulated by hand; automation can cut that effort by 50% to 70% or more, depending on the complexity and volume of the data.
2. Fewer errors: manual data entry and analysis in spreadsheets are prone to human error. By automating data collection and processing, an energy management system can reduce errors by between 30% and 60%, depending on how effective the system is and how accurate the previous manual methods were.
3. Faster analysis and reporting: energy management systems usually include advanced analysis and reporting tools, which can cut the time spent on these tasks by 40% to 60% compared with Excel.
4. Real-time monitoring and alerts: monitoring consumption in real time and receiving automatic alerts about anomalies or excess consumption has no real equivalent in Excel, and is one of the biggest steps forward in energy management.

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