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artificial intelligence

How AI can make Energy Management more efficient

960 640 Stuart O'Brien

In the realm of energy management, professionals are always on the hunt for tools and technologies to make their tasks more efficient. As energy consumption patterns grow more complex, the need for advanced solutions becomes paramount. Enter Artificial Intelligence (AI). This technology, which seemed futuristic a decade ago, is now at the forefront of revolutionising energy management. Here’s how AI is assisting energy professionals in their daily operations…

  1. Predictive Maintenance:
    • Function: AI algorithms can predict when equipment is likely to fail or require maintenance by analysing vast amounts of data on equipment usage and performance.
    • Benefit: This predictive approach results in reduced downtime, extending the lifespan of equipment and ensuring optimal performance.
  2. Demand Forecasting:
    • Function: By analysing historical data and integrating real-time variables such as weather patterns and occupancy rates, AI can accurately predict energy demand.
    • Benefit: Accurate forecasting allows for better allocation of resources, ensuring that energy production meets demand without excessive wastage.
  3. Optimisation of Energy Consumption:
    • Function: AI systems can monitor energy usage patterns and make real-time adjustments to HVAC systems, lighting, and other energy-consuming devices.
    • Benefit: This dynamic approach to energy consumption ensures maximum efficiency, reducing costs and carbon footprints.
  4. Integration of Renewable Sources:
    • Function: AI can manage the integration of multiple energy sources, especially renewables like wind and solar, by predicting their output and balancing it with the grid’s demand.
    • Benefit: Ensuring a smooth blend of renewable sources reduces reliance on fossil fuels and promotes sustainable energy practices.
  5. Advanced Data Analytics:
    • Function: AI-powered data analytics tools can sift through vast datasets to identify patterns, inefficiencies, or anomalies in energy consumption.
    • Benefit: With insights gleaned from this data, energy managers can make informed decisions, identify areas for improvement, and implement strategies for energy conservation.
  6. Automated Reporting:
    • Function: AI tools can automatically generate detailed reports on energy usage, cost savings, or carbon emissions without manual intervention.
    • Benefit: Automated reports save time, ensure accuracy, and provide stakeholders with timely information to make strategic decisions.
  7. Enhanced Security:
    • Function: With the increasing threat of cyberattacks on energy grids and infrastructure, AI can monitor network activities, detect anomalies, and deploy countermeasures in real-time.
    • Benefit: Enhanced security ensures the uninterrupted supply of energy and protects sensitive data.

As energy landscapes evolve, the role of AI in energy management is proving to be indispensable. With its ability to process massive amounts of data, make predictive analyses, and automate mundane tasks, AI is not only making the life of energy management professionals easier but also paving the way for a more efficient and sustainable energy future. For professionals in the sector, embracing AI is not just a smart move; it’s a necessary step towards modernisation.

The Energy Management Summit offers insights and solutions when it comes to AI – Register today!

Empowering Energy Management: The Impact of Generative AI in the UK

960 640 Stuart O'Brien

The rise of generative artificial intelligence (AI) is reshaping industries, and the energy management sector in the UK stands as a prime beneficiary of this digital revolution. Employing AI to create, simulate, and predict scenarios is promising unprecedented efficiencies and smarter resource utilisation.

Taking a step back to look at energy production, generative AI is used to forecast energy generation based on historical data and real-time factors such as weather patterns, enabling energy providers to optimise production schedules. This is particularly beneficial for renewable energy sources, where production can be highly variable.

AI is also making strides in energy consumption management. Building Management Systems (BMS) can use generative AI to learn from past consumption patterns, weather data, and occupancy rates to predict future energy needs. The BMS can then adjust HVAC, lighting, and other systems in real-time to minimise waste and maximise efficiency.

Generative AI is also bolstering the implementation of demand response programmes. By predicting periods of peak demand, AI enables energy providers to incentivise customers to reduce their usage, thus easing pressure on the grid.

Grid management is another area where generative AI is making a significant impact. AI can generate models that simulate various scenarios, allowing grid operators to prepare for different outcomes. This leads to more reliable service and faster response to outages or disruptions.

Furthermore, generative AI is playing a crucial role in the transition to a decentralised energy model. AI algorithms can optimise the operation of microgrids and virtual power plants, balancing local production and consumption, and ensuring a smooth interplay with the larger grid.

However, the adoption of generative AI in energy management is not without challenges. Issues such as data privacy, algorithmic transparency, and the need for a skilled workforce capable of working with AI need to be addressed. Moreover, the development and deployment of AI systems require significant investment.

Despite these challenges, the potential benefits of generative AI for energy management are immense. By enhancing forecasting, optimising consumption, and enabling smarter grid management, AI holds the promise of a more efficient and resilient energy system.

The energy management sector in the UK is just beginning to harness the power of generative AI. As its capabilities continue to be realised, it is certain to play a pivotal role in shaping the future of energy in the country – a future marked by efficiency, sustainability, and resilience.

As we stand on the cusp of this exciting era, it is clear that generative AI will be at the heart of the energy revolution.

Saving money using Artificial Intelligence

960 640 Stuart O'Brien

By OnSite Energy Projects

We are used to BMS (building management systems) to control buildings, and spreadsheets to analyse data. Both of these require human input (and are prone to human error). So does Artificial Intelligence have any role to play, and could it save money? Firstly, what is AI?

  • “Artificial Intelligence” (“AI”) is software “able to perform tasks normally requiring human intelligence, such as visual perception, decision-making, and translation between languages”.
  • “Machine Learning” (“ML”) is the “application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed”

At OEP, we are already using AI / ML in two very real building and energy management applications and seeing £££ financial benefits:

  • Energy Management. We can ingest half-hourly meter data daily, and the software will identify patterns that could indicate a fault or anomaly, so it can be investigated before it becomes a cost. The ML learns from human past action (or inaction) on the issues raised to know if it should raise the issue again in future, or simply log it.

We provide “Energy Management as a Service” for less than £10 per meter per month, which provides over-loaded energy managers with a proactive management tool, particularly across an estate of meters.  An example benefit we picked up within 24 hours, was a (human) BMS programming error that would have cost £60,000 had it gone undetected.

We have plans to extend this to monitoring sub-meter data as well.

  • Automated BMS. AI/ML can also be deployed directly to manage the BMS.  The software “learns” how the building reacts over time to different events and climate conditions (creating a “digital twin”) and can develop its own strategies for how to optimise the building to (1) deliver the climate goals consistently and (2) at least energy cost.   It can even re-commission the building regularly.  Saving are typically 25%-40% of HVAC load – the impact of running the equipment at the right times and loads, and turning off when not needed.

The benefits of using AI is the ability for it to react quickly to changing circumstances. Other applications we are engaged on using AI are compressed air management and refrigeration systems.

If you would like to know more email us at or call on 0161 444 9989.

Onsite Energy Projects provides energy savings and energy generation solutions to energy intensive businesses, without capex if required.