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Artificial Intelligence in Energy Management

AI has become a hot topic in the past couple of months, with the release of Chat GPT, the rise of AI-generated art, and a big debate in Hollywood around the use of AI in media. AI is not a new technology, but it is becoming ever more present in our daily lives, at home and at work. One sector that has greatly benefited from this is energy management. As the global demand for energy continues to rise, finding efficient and sustainable ways to handle energy consumption becomes imperative. AI offers an array of solutions that have the potential to transform the energy landscape, from predictive maintenance to forecasted spending and identifying trends. In this blog, we delve into the role of AI in energy management and explore how it can pave the way for a more sustainable future.


Anomaly Detection

One of the most significant contributions of AI in energy management is anomaly detection. Using existing models, you can compare current consumption to previous data to look for changes in patterns. AI models can analyse this data without human bias, using only the details to identify anomalies and create alerts.

By using an anomaly detection model, businesses can be informed whenever their consumption differs from the expected pattern, as predicted by the AI, allowing them to identify and address the cause. This ensures their consumption behaviours are maintained to maximise efficiency, reduce waste, and cut costs.


Predictive Maintenance

As well as identifying anomalies, AI can be trained to predict when equipment requires maintenance. Traditional maintenance practices have often relied on scheduled checks and reactive measures, leading to inefficient use of resources and costly downtime. AI-driven predictive maintenance, on the other hand, empowers businesses to identify potential equipment failures before they occur, allowing for proactive intervention.

By leveraging machine learning algorithms, AI systems analyse vast amounts of data collected from various sensors and devices, comparing it to the equipment’s expected output from the manufacturer’s specifications. These algorithms can detect patterns and anomalies, predicting when a component might malfunction or require maintenance. As a result, businesses can schedule maintenance precisely when it is needed, reducing downtime, minimising repair costs, and extending the lifespan of equipment.


Forecasted Spending

Energy management involves not only monitoring consumption but also managing budgets and expenditures effectively. AI plays a pivotal role in budgeting, by forecasting spending based on consumption patterns and market trends.

Using historical energy usage data and external factors such as weather forecasts, market prices, and seasonal demand fluctuations, AI algorithms can generate accurate predictions of future energy consumption and corresponding costs. This invaluable insight allows businesses to plan their budgets more efficiently, enabling them to adjust their consumption patterns during periods of peak energy prices.


At OAK Network, we use our machine-learning algorithm to do all of the above and much more. This blog was even co-authored by Chat GPT – thanks for the help. Artificial Intelligence has undoubtedly changed the game in energy management, empowering businesses and consumers to make informed decisions that drive efficiency and sustainability. As we move towards a greener and more sustainable future, AI's role in energy management will only continue to grow.

Our machine-learning algorithm allows us to offer our clients an enhanced energy management service, with in-depth data analysis, alerts, and predictive maintenance, leading to savings of up to 20% on energy bills. Don’t just take our word for it, ask our clients. We saved one business £1,750 using our predictive maintenance model to detect inefficient air conditioning units. Check out our last blog to find out more or get in touch today to find out how we (and AI) can help your business to become more efficient.