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Artificial Intelligence in the Energy Sector

Energy

AI in energyOptimize your energy strategy with decisions based on artificial intelligence

Build the energy future with Decision AI

With Decision AI, energy companies drive sustainability in energy generation, reduce distribution costs, and optimize their pricing strategies. We also facilitate the strategic location of infrastructure to accelerate ROI. Our solutions, based on data and advanced models, ensure efficient and profitable decisions.

Energy Summit Making decisions under uncertainty.

Markets, assets, and operations in the energy sector.

A morning to discuss how companies in the energy sector can turn data, forecasts, and constraints into better business and operational decisions.

November 19th · 9:00–15:00 h
Espacio Downtown · Calle de la Paloma, 15 · Madrid
I want to attend.
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Sector challengesTwo value chains. One shared challenge: making better decisions.

Case studies and conversations on how to apply models, algorithms, and optimization to make better decisions when markets, demand, or operational constraints change.

 Hydrocarbons

Planning, logistics, and operational decisions across the entire value chain.

Planning · Logistics · Inventory · Margin

Hydrocarbons and Algorithms Guide

 Electric Power

Real-time decisions on demand, prices, and asset flexibility.

Demand · Prices · Assets · Flexibility

Electricity and Algorithms Guide

Registration formReserve your spot.

Industry expertsSpeakers

Macario_Polo_Usaola_Thumbnail_play_BaobabAIHub_entrevistas_20260306

Dr. Macario Polo Usaola

Catedrático de Universidad. Tecnologías y Sistemas de Información

Universidad de Castilla-La Mancha

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Miguel_Ortega_Mier_Thumbnails_BaobabAIHub_entrevistas_20260306

Dr. Miguel Ortega-Mier

Catedrático de Universidad – Adjunto Al Vicerrector para Digitalización de Procesos Académicos

Universidad Politécnica de Madrid

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Amaia_Lusa_García_Thumbnail_play_BaobabAIHub_entrevistas_20260306

Dr. Amaia Lusa García

Catedrática de Universidad – Directora de la escuela de ingeniería ETSEIB

Universitat Politècnica de Catalunya

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Carlos_Andrés_ Romano_Thumbnail_play_BaobabAIHub_entrevistas_20260306

Dr. Carlos Andrés Romano

Catedrático de Universidad

Universitat Politècnica de València

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value chainSee how our Decision AI optimizes each phase of the energy value chain

Título del nodo

Aquí irá el contenido del nodo cuando lo conectemos.

Power generation
1
Power transmission and distribution
2
Energy storage
3
Marketing and regulation of the energy market
4
Energy consumption and efficiency
5
Transition to renewable energy and sustainability
6

Power Generation

Power transmission and distribution

Energy storage

Marketing and regulation of the energy market

Energy consumption and efficiency

Transition to renewable energy and sustainability

Energy guidesDive deeper into the decision-making challenges in the energy sector.

These guides are limited in edition; each request will be reviewed individually, and one copy will be sent per verified profile until the available number of copies is reached.

Submit your download request.

These guides are limited in edition. Each request will be reviewed individually, and one copy will be sent per verified profile until the available units run out.

Use casesSome use cases

Discover how our solutions have improved efficiency and optimized operations in the energy sector.

Energy production optimized with Decision AI

  • Industry: Energy
  • Challenge: Improve efficiency in industrial gas production by adjusting energy consumption based on demand, energy prices, and operating constraints.
  • Approach: Decision AI uses advanced predictive models to adjust plant configuration in real time, optimizing production and energy consumption.
  • Results: Reduced planning time, lower energy consumption, increased productivity, and operational flexibility.
  • Key metrics: Reduced operating costs by €300,000–€500,000 per plant annually.
0 .000€
Reduction of operating costs

Optimizing energy supply for the next-day market

  • Industry: Energy
  • Challenge: Improve competitiveness in the next-day energy market by optimizing supply based on production, demand, and pricing strategy.
  • Approach: Implementation of Decision AI to model supply and demand scenarios, optimizing the energy production and marketing strategy.
  • Results: Reduced costs and maximized revenue by optimizing energy supply.
  • Key metrics: Increased revenue of €500,000 per month.
0 .000€
Increase in monthly income

Optimization of wind farm layout

  • Industry: Energy
  • Challenge: Increase energy production in wind farms by optimizing wind turbine siting.
  • Approach: Use of decision intelligence tools to analyze wind, terrain, and technical constraint data to optimize wind turbine placement and minimize interference.
  • Results: Increased energy efficiency, reduced interference losses, and increased renewable energy generation.
  • Key metrics: Increased electricity production by 3%
0 %
Increase in electricity production

Optimization of hydroelectric production

  • Industry: Energy
    Challenge: Maximize the profitability of hydroelectric generation by adjusting the use of reservoir flow based on market demand and prices.
  • Approach: Application of Decision Intelligence to predict price fluctuations in the electricity market and optimize the opening and closing of floodgates based on the best financial performance.
  • Results: Efficient use of water resources, reduced waste, and increased revenue from energy sales.
  • Key metrics: 7% increase in total revenue.
0 %
Increase in total revenue

NewsNews and trends in AI applied to management, production, and supply chain in the energy sector.

Discover how to improve your strategic decisions. For example, we’ve managed to increase revenue by up to €500,000 per month by optimizing offers in the next-day energy market.

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    • Power Generation

    Activa DecisionAI
    • How to optimize production to reduce costs and maximize efficiency?
    • How to anticipate infrastructure failures to avoid unexpected outages?
    • How can we improve renewable energy forecasting to stabilize supply?
    • How can infrastructure (wind farms, solar farms, production plants) be planned and optimized to obtain the most energy at the lowest cost?

    Added value of AI and analytics:

    • Production optimization: ML models adjust production based on demand and resource availability.
    • Predictive maintenance: IoT sensors and algorithms anticipate failures in generating infrastructure.
    • Renewable energy forecasting: Advanced climate models improve solar and wind energy forecasting.

    Problems solved:

    • Inefficiency in energy production, which generates additional costs.
    • Unexpected failures in generating plants, which affect the stability of supply.
    • Variability in renewable generation, improving the integration of intermittent sources.
    Contact Us

    Power Generation

    Activa DecisionAI
    • How to optimize production to reduce costs and maximize efficiency?
    • How to anticipate infrastructure failures to avoid unexpected outages?
    • How can we improve renewable energy forecasting to stabilize supply?
    • How can infrastructure (wind farms, solar farms, production plants) be planned and optimized to obtain the most energy at the lowest cost?

    Added value of AI and analytics:

    • Production optimization: ML models adjust production based on demand and resource availability.
    • Predictive maintenance: IoT sensors and algorithms anticipate failures in generating infrastructure.
    • Renewable energy forecasting: Advanced climate models improve solar and wind energy forecasting.

    Problems solved:

    • Inefficiency in energy production, which generates additional costs.
    • Unexpected failures in generating plants, which affect the stability of supply.
    • Variability in renewable generation, improving the integration of intermittent sources.
    Contact Us

    Power transmission and distribution

    Activate DecisionAI
    • How can grid load be balanced to avoid losses and optimize costs?
    • How can infrastructure failures be detected before they occur?
    • How can energy distribution be automated to adjust to demand in real time?

    Added value of AI and analytics:

    • Grid load optimization: Prescriptive algorithms balance the flow of electricity in real time.
    • Early fault detection: AI identifies anomalies and predicts supply disruptions.
    • Smart grids: Autonomous systems adjust distribution based on demand.

    Problems solved:

    • Energy losses in transmission, optimizing system efficiency.
    • Lack of response to load fluctuations, ensuring supply stability.
    • Electrical infrastructure failures, reducing service outages.
    Contact Us

    Energy storage

    Activa DecisionAI
    • How can we optimize battery use to reduce waste and improve efficiency?
    • How can we anticipate future demand to better manage storage levels?
    • How can we extend the lifespan of storage infrastructure with smart maintenance?

    Added value of AI and analytics:

    • Battery utilization optimization: AI models maximize charging and discharging efficiency.
    • Demand and supply prediction: Machine learning adjusts storage levels based on future needs.
    • Battery degradation management: Algorithms analyze the performance of storage systems and extend their lifespan.

    Problemas resueltos:

    • Low efficiency in battery charging and discharging, optimizing their use.
    • High costs due to poor storage planning, adjusting energy management.
    • Premature wear of infrastructure, increasing the lifespan of batteries and equipment.
    Contact Us

    Marketing and regulation of the energy market.

    Activate DecisionAI
    • How can you define dynamic pricing that maximizes revenue without losing competitiveness?
    • How can you optimize energy purchasing and sales to avoid financial losses?
    • How can you predict customer consumption to improve planning and profitability?

    Added value of AI and analytics:

    • Dynamic pricing models: real-time adjustment based on supply, demand, and regulation.
    • Energy contract optimization: AI recommends energy purchasing and sales strategies.
    • Industrial and residential consumption prediction: machine learning anticipates demand and avoids cost overruns.

    Problems solved:

    • Inefficient pricing, ensuring competitive rates.
    • Poor planning in energy purchases and sales, avoiding economic losses.
    • Outdated market regulation, adjusting regulations with predictive analytics.
    Contact Us

    Energy consumption and efficiency

    Activate DecisionAI
    • How can we reduce energy consumption without affecting productivity or comfort?
    • How can we implement energy-efficient systems that reduce operating costs?
    • How can we leverage automation to manage smart buildings and cities sustainably?

    Added value of AI and analytics:

    • Real-time consumption monitoring: smart sensors optimize electricity use.
    • Energy efficiency systems: machine learning recommends adjustments to industrial and domestic processes.
    • Smart buildings and cities: algorithms automate energy management in urban infrastructure.

    Problems solved:

    • High energy consumption in industries, improving operational efficiency.
    • High electricity billing costs, reducing waste and adjusting consumption.
    • High environmental impact, promoting energy sustainability.
    Contact Us

    Transition to renewable energy and sustainability

    Activa DecisionAI
    • How can we integrate renewable energy without compromising supply stability?
    • How can we optimize investment in clean energy to accelerate the energy transition?
    • How can we manage microgrids and balance supply between renewable and conventional sources?

    Added value of AI and analytics:

    • Solar and wind generation prediction: Machine learning analyzes weather patterns to improve renewable energy integration.
    • Clean energy investment optimization: Financial models suggest the best adoption strategies.
    • Microgrid management systems: Decentralized algorithms balance supply between renewable and conventional sources.

    Problems solved:

    • Lack of grid stability with renewables, improving energy integration.
    • High energy transition costs, optimizing investment.
    • Low adoption of clean energy, incentivizing profitable production models.
    Contact Us