Machine Learning in Energy Sector: Use Cases

Nowadays Artificial intelligence (AI) is helping energy companies analyze massive amounts of data. Most of the data on energy consumption is collected through IoT devices, such as sensors. So, to analyze such Big Data and get insights, you need find machine learning consulting company to collect that unstructured data first…

After, transform it into a structured format, store, and then apply advanced machine learning algorithms to find correlations. That is why the use of machine learning in the Energy industry is no an easy solution.

Above all, the biggest advantage of AI in the energy sector (energy tech) is that algorithms can be trained on new data sets. This would definitely help scale such solutions. But how exactly could machine learning help? Some of the use cases are below.

Machine Learning Use Cases in the Energy Sector

Anomaly detection in energy consumption to ensure smooth operation and prevent unexpected events.

It is hard to see where the electricity is being used in the electricity consumption data. This makes it hard to detect a malfunctioning piece of equipment. If one of these systems fails or is misconfigured this could cause negative effects, such as fire. The Artificial Intelligence algorithms can constantly monitor, analyze energy consumption, detect emerging problems and assist in the analysis to improve performance. It also helps to avoid big financial losses.

Developed an artificial intelligence algorithm that can automatically define the type of object (e.g., supermarket, high school) and then, based on energy consumption, detect and categorize anomalies in real time. Such a solution automatically finds an anomaly in energy consumption and informs users, who can make decisions very quickly. It helps to avoid big financial losses.

Interested in machine learning?
Read our article: Machine Learning. What it is and why it is essential to business?

Energy demand prediction – the most popular application of Machine Learning in energy industry

Another use of machine learning algorithms is to determine energy demand will be on a particular day. This is done by tracking how daily energy consumption changes for individual customers over time.

Machine learning models are able to generate very accurate energy demand and consumption forecasts. Those predictions could be used by facilities and building managers, energy companies, and utility companies to deploy energy-saving policies. For instance, manufacturing companies with prediction help can make plans on how to optimize the particular operations and energy storage systems.

Indicating the optimal energy price using machine learning in energy industry

Price optimization models use the power of neural networks to predict demand for energy consumption and make optimized pricing recommendations to help the energy companies meet target goals. Therefore, it has several benefits over expert-based pricing system:

• Such models could analyze huge amounts of data (Big Data) that are unmanageable for people.

• Their able to learns non-linear correlations between energy supply and demand to make counterintuitive pricing recommendation.

• Transparent big data-driven pricing strategy and recommendations that are easy to monitor.

• Saves pricing managers time from routine work and allows them to make only high-level decisions.

Business recommendation engine

To support decision-making processes. Providing customer-oriented solutions that understand the changing needs of customers and automatically generate recommendations. Such recommendations could support decision-making in the area of pricing, energy production, and selling areas. Business departments could use it as a support and quantitative reference.

Machine Learning in energy industry helps to optimize contracted capacity

Machine Learning algorithms through elaborate selection the optimal contracted capacity (CC) helps to minimize total cost of energy expenses.

Energy disaggregation (signals disaggregation)

Separation of profiles of individual receivers from the energy profile signal to better consumption behavior improve energy efficiency.

And many more cases where AI could help. Select the optimum size of energy storage, Scale EV charging station, Calibration of photovoltaics (PV), Selection of optimal tariffs of the distribution system operator, Defining the relationship between power quality and customer productivity and correlation of receiver failures with power quality parameter.

Machine Learning in Energy Sector: Leveraging Business Intelligence Software for Data Analysis

Using AI solutions, you can collect a lot of signals and information. Some of the results could be integrated into applications for autonomous work, but some data needs to be analyzed by business users. Business intelligence services provide well-built reporting and data processing infrastructure that is able to ensure the collection, integration, measurement, and analysis of data in order to draw useful conclusions from it. Companies can use these insights to make more appropriate decisions. Intelligent reporting helps companies to make effective decisions based on data in the following business areas:

Analysis of historical data

To clarify, historical data on energy consumption can help monitor and track energy consumption over time and take appropriate actions in terms of energy consumption.

Additionally, historical data will help you to understand seasonalities and track trends that are significant in energy consumption, sales, and production.

Analysis of energy production

Most importantly, BI solutions must help in the analysis of power generation and power outages and help solve problems in real-time.

Real-time energy monitoring

Modern Business Intelligence software is able to extract data from data sources in real-time. Above all, this feature, together with a built-in notification system and machine learning signals, could inform decision-makers about different abnormous events.

Benefits that companies can get through the use of intelligent data solutions with machine learning in the energy industry

• Better data quality – bad information or data on past or current activities may lead to wrong decision-making. An appropriate reporting system with AI features helps companies to achieve better data quality and make smart decisions more effective.
• Flexible reporting – system users will be able to analyze data anywhere at any time. Mobile reports will allow you to make key decisions regardless of where the decision-maker is.
• Data Governance – responsible for determining information and data quality, setting good standards, and ensuring that the information and quality is achieved at very good level. Effective Business Intelligence solution ensures that these processes are carried out in an orderly fashion.
• Efficient Master Data Management – Business Intelligence (BI) helps to properly maintain data in companies database and data warehouse. Master Data Management (MDM) provides companies with valuable information about company’s key business entities and areas such as vendors, customers, products, etc. Companies from Energy industry rely heavily on this kind of data for critical and proper decision making.

To sum things up

Above mentioned solutions (Machine Learning and Business Intelligence) will allow you to save time, money, and achieve your business goals. Prepared BI dashboard with interactive charts and machine learning insight will make your work as pleasant as possible.

If you need more information about how machine learning, AI, or Business Intelligence is used in the Energy industry (sector), our team of Data Science experts can advise you on the best solution for your business and how to implement it and properly maintain it.

Drop us a message and let us know if there’s anything we can help you with.

Machine Learning in Energy Sector – FAQ

How does AI contribute to the energy industry?

Artificial Intelligence (AI) revolutionizes the energy sector by analyzing massive amounts of data collected through IoT devices. AI algorithms transform unstructured data into structured formats, enabling advanced machine learning algorithms to uncover correlations, thus aiding in predictive analytics, anomaly detection, and optimization of various processes.

What are some specific applications of Machine Learning in the energy sector?

Machine Learning in the energy industry finds diverse applications such as:

• Anomaly detection in energy consumption to ensure smooth operations and prevent unexpected events.
• Energy demand prediction for effective resource allocation and energy-saving policies.
• Optimal energy pricing to meet target goals and enhance profitability.
• Business recommendation engines for customer-oriented solutions and decision support.

How can Machine Learning optimize energy-related processes?

Machine Learning algorithms optimize energy-related processes by:

Determining optimal energy storage sizes.
Scaling Electric Vehicle (EV) charging stations.
Calibrating photovoltaics (PV) systems.
Selecting optimal tariffs of distribution system operators.
Defining relationships between power quality and customer productivity.

How does Business Intelligence (BI) complement Machine Learning in the energy sector?

Business Intelligence software enhances data analysis by providing:

• Analysis of historical data to monitor trends and seasonalities.
• Real-time energy monitoring to detect abnormalities and inform decision-makers promptly.
• Better data quality and governance to facilitate smart decision-making.
• Flexible reporting and mobile accessibility for on-the-go analysis.
• Efficient Master Data Management (MDM) for proper maintenance of key business entities.

What benefits can companies derive from implementing intelligent data solutions in the energy industry?

Companies leveraging Machine Learning and Business Intelligence in the energy sector can:

• Achieve better data quality and make more effective decisions.
• Access data analysis anywhere, anytime with mobile reporting capabilities.
• Ensure data governance and efficient Master Data Management.
• Save time, money, and achieve business goals through optimized processes and informed decision-making.

This article is an updated version of the publication from Feb 15, 2019 at 21:05.