The energy sector has seen continuous interest in data-driven solutions for tackling energy consumption, demand, and production problems. The increased popularity of smart sensors and collectors has resulted in massive amounts of data, which in turn has created endless opportunities for modern predictive technologies. Machine learning tools are fast, efficient and accurate and can provide an understanding of most intricate data sources. Businesses in the energy sector have identified this opportunity and leverage their predictive powers for a wide range of applications. Below we list the main benefits of machine learning solutions and back them up with some examples of successful applications.
1. Increased Malfunction Prevention and Reduced Equipment Downtime
Machine learning solutions give energy providers a reliable way to monitor the proper functioning of their machines and devices. As part of preventive maintenance measures, algorithms for failure probability modeling are crucial for the immediate detection of potential glitches.
When analytic models are provided with access to baseline functioning conditions and to real-time sensor and tracker data, they become a safe-guard for smooth operations. Once a probable failure is identified, human operators can be alerted and costly mistakes avoided.
Reducing equipment downtime and preventing malfunctioning errors will help prevent massive outages. Also, companies can make decisions based on a solid model that helps them optimize maintenance costs and efficiently use their resources.
2. Accurate Demand Forecasts
The ability to forecast demand across consumers is a fundamental aspect of the energy economy. But finding the right balance between supply and demand can be a real challenge, given that massive amounts of data are involved, and that multiple data sources need to be interpreted altogether (e.g. weather and historical consumption data).
Machine learning algorithms have predictive superpowers that allow them to systematically process large amounts of electricity trading data. Modern storage solutions make it possible to easily tackle heterogeneous data sources and the models become more and more accurate with time. The more data they are fed, the better the predictions are.
Accurate demand predictions improve all aspects of demand management. This leads to an optimized energy flow between providers and consumers and to minimize costs on both sides.
3. Reduced Operational Costs
There are plenty of areas in the energy sector where machine learning solutions are crucial for improving operational efficiency and thus cutting costs. Applications range from performance monitoring and smart alerting systems to price scheme optimization and forecasting of solar / wind conditions.
Such intelligent solutions can deal with the complex nature of power grid systems and react in real-time to a live data stream. They ensure power grid stability and reliability, they are available 24/7 and require little to no human intervention. Lastly, by modeling the intricate dependencies among various participants (consumers, producers, storage facilities), they bring companies important competitive advantages, that would otherwise be expensive to achieve.
Thus, the aggregation, analysis, and interpretation of energy data translate to increased efficiency for a fraction of the cost.
4. Informed Business Decisions
Business decisions backed by quantitative data analysis have become the norm in many industries, thanks to the power of machine learning tools. For some sectors, the complex sources of information are simply not usable without a proper business intelligence platform.
The energy sector is no exception to this rule, given the challenging and sometimes contradictory objectives that need to be cared for. When trying to balance out functional performance, competitive pricing schemes, environmental impact, and grid efficiency, only advanced analytics can help find the right answer.
Machine learning supports informed decision making in multiple areas, from pricing to production and selling. Advanced recommendation systems aid human decision-makers and insights obtained from data can be used by all involved stakeholders.
5. Better Data Access & Quality
Building machine learning solutions requires access to large amounts of data. The energy sector is particularly rich in data, but its convoluted nature also makes it hard to interpret.
When energy data is prepared for machine learning, it is usually collected, aggregated and made available for training in storage locations that are optimized for quick access and real-time inference. This also makes it easily accessible for BI and visualization tools. This is a valuable side-effect of machine learning solutions since data access becomes possible to multiple stakeholders, beyond the data science team.
When business intelligence is enabled and available, organizations can be sure they are making informed decisions and have the correct overview of their operational aspects.
Summary: Energy and Utility Optimization
Blue Orange Digital has extensive experience developing machine learning algorithms, analytic models and custom big data solutions. We have previously tackled problems in the energy sector (read more about our advanced sensor analytics platform) and we are always happy to help companies understand the power of their data.
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