AleaSoft Energy Forecasting, May 15, 2026. Electrification, self‑consumption, batteries, PPA and market volatility are transforming industrial energy management. In this new environment, AI agents, supported by reliable forecasting and advanced models, will be essential for optimising costs, flexibility, investments and competitiveness.
Decarbonisation is increasing the complexity of industrial energy management
Industrial decarbonisation has already begun. The electrification of processes, the growth of photovoltaic self‑consumption, the incorporation of batteries, thermal storage, demand‑side flexibility and exposure to increasingly volatile electricity markets are transforming the way industries consume, purchase and manage energy.
For decades, energy was a significant but relatively stable and predictable cost for many industrial companies. That context has changed. The International Energy Agency (IEA) identifies growing electrification, the expansion of electricity systems and the rise of weather‑dependent generation technologies among the major trends affecting the electricity sector.
This transformation is introducing unprecedented operational complexity. A single industrial facility may combine photovoltaic energy generation, batteries, thermal storage, partially flexible consumption, PPA contracts, exposure to the spot market, electrified processes and, in some sectors, the coexistence of electricity, gas, industrial heat or new energy vectors such as hydrogen. The result is an increasingly broad decision‑making environment in which energy‑related decisions can no longer be analysed in isolation.
More volatile markets and negative prices
Electricity market volatility is one of the factors accelerating this transformation most rapidly. ACER, the European Union Agency for the Cooperation of Energy Regulators, notes that the increase in very low and negative prices observed in 2023 intensified in 2024 and 2025, and that on 70% of days intraday electricity price variations reached €50/MWh or more. According to data from AleaSoft Energy Forecasting, the average spread of hourly prices in the day‑ahead electricity market in 2025 reached €98.44/MWh in Spain, €124.13/MWh in Germany and €89.73/MWh in France.
The number of hours with negative prices has also increased. Although they are not yet dominant in most markets, they are becoming more frequent in several regions. These episodes often reflect a lack of flexibility in supply or demand, particularly during periods of low electricity demand and high renewable energy generation.
For industry, this new environment raises increasingly complex questions. When to charge a battery, when to shift consumption, when to sell surplus energy, when to consume electricity instead of gas or when to participate in flexibility services are decisions that depend on prices, forecasts, technical constraints, contracts, production requirements and emissions targets. Energy optimisation based solely on manual decision‑making is becoming insufficient in facilities with multiple assets, hourly market exposure and simultaneous objectives relating to cost, competitiveness, emissions and operational continuity.
AI agents as copilots for energy‑related decisions
In this context, artificial intelligence agents specialised in energy are expected to play an increasingly important role. In most cases, these are not autonomous systems designed to replace human decision‑making entirely, but supervised tools capable of analysing large volumes of data, anticipating scenarios, assessing constraints and proposing more efficient operational decisions.
These agents will act as copilots for industrial energy management. Their role will be to coordinate assets such as batteries, self‑consumption systems, flexible loads, thermal storage and market exposure, taking into account both economic signals and the real constraints of each industrial process.
Artificial intelligence can help optimise complex energy systems, improve production, reduce costs, increase efficiency, enhance operational availability, reduce emissions and strengthen security. Applications are also emerging in electricity systems, predictive maintenance, renewable integration, industrial efficiency and weather forecasting applied to energy operations.
The economic potential could be significant. The IEA has estimated that AI‑driven process optimisation could reduce energy costs by between 3 and 10 percentage points in energy‑intensive industries, provided barriers such as insufficient digital capabilities, fragmented data and cybersecurity risks are overcome.
AI does not eliminate industrial constraints
The application of artificial intelligence to industrial energy management should not be interpreted as unlimited automation. Not all loads can be shifted and not all processes can be stopped or modified according to electricity prices. Industry operates under production, quality, temperature, technical ramping, maintenance, shift scheduling, contractual penalties and opportunity cost constraints.
For this reason, AI energy agents will need to be integrated with the operational knowledge of each facility. Optimisation cannot be limited to identifying the hours with the lowest electricity market prices; it must also respect the physical, production and commercial conditions of the plant. In many cases, the value of these systems will lie precisely in finding the balance between energy savings, operational stability, emissions reduction and compliance with production commitments.
The European Commission has also identified digitalisation and artificial intelligence as important components of the future energy system. In 2025, it launched consultations to prepare a strategic roadmap on digitalisation and AI in the energy sector, covering both the opportunities offered by these technologies and the need for safeguards as they are deployed at scale.
Energy forecasting will be a critical component
One of the most important elements of this transformation will be the quality of energy forecasting. An AI agent for energy management will only be as effective as the forecasts on which it bases its decisions. If forecasts for prices, demand, renewable energy generation or asset availability are poor, the recommended decisions will also be flawed.
Industrial energy management will require combining forecasts across different time horizons. Short- and mid‑term forecasts will be essential for operating batteries, shifting consumption, managing surplus energy, optimising market purchases and adjusting hedging strategies. Long‑term hourly forecasts will be necessary to assess investments in electrification, batteries, thermal storage, PPA and flexibility strategies.
In this sense, AI agents do not replace specialised energy forecasting. On the contrary, they make it even more important. The combination of hourly forecasting, probabilistic modelling, electricity market analysis and optimisation of storage and flexibility will become one of the major competitive advantages of the coming decade.
From technological advantage to strategic necessity
Europe faces one of the greatest industrial challenges in its recent history: decarbonising its economy without losing global competitiveness. To achieve this, deploying new energy technologies alone will not be enough. It will also be necessary to manage intelligently the complexity these technologies introduce into companies’ day‑to‑day operations.
Industries that integrate AI energy agents, high‑quality forecasting and supervised optimisation systems at an early stage will be better prepared to reduce costs, limit exposure to volatility, improve the profitability of investments, reduce emissions and increase the bankability of their energy projects.
The next major industrial transformation will not depend solely on electrifying processes or installing more renewables and batteries. It will also depend on the ability to turn millions of data points and thousands of energy‑related decisions into coherent, efficient operational strategies aligned with each company’s economic and climate objectives. In this new environment, artificial intelligence applied to energy will cease to be merely a technological advantage and will become a strategic necessity.
The role of forecasting and advanced models
For AI agents applied to industrial energy management to deliver real value, reliable forecasting, robust models and a deep understanding of energy markets will be essential. The optimisation of batteries, flexible consumption, self‑consumption systems, PPA contracts or hedging strategies cannot rely solely on automation, but must be based on high‑quality hourly forecasting, probabilistic scenarios and models capable of integrating technical, economic and operational constraints.
AleaSoft Energy Forecasting is working with large consumers, electro‑intensive industries, developers and energy companies on the design and analysis of energy optimisation strategies. These projects combine long-term hourly price forecasts, short- and mid‑term forecasting, multi‑market revenue analysis, battery simulation, flexibility modelling and scenario assessment to support robust and bankable investment decisions.
The aim is to help companies capture the economic value of the new energy management paradigm: reducing costs, optimising investments in storage and electrification, limiting exposure to market volatility and improving competitiveness in an increasingly complex environment.
On May 21, 2026, AleaSoft Energy Forecasting will hold the 66th edition of its monthly webinar series, which on this occasion will feature Alejandro Diego Rosell to analyse the prospects and opportunities for energy storage and its hybridisation with renewable energy sources.
Source: AleaSoft Energy Forecasting.