Part II: How AI-Driven Demand Forecasting Is Helping European Electricity Distribution Firms

In Part I, you saw an overview of the European energy market—how the electricity trading market is structured, how European government initiatives affect the market, and how the EU is aiming for more than climate neutrality. You also saw some use cases explaining how vital consistent Day Ahead and Intra-Day Forecasts are for Electric Distribution Companies. 

In Part II, you’ll see:

• The power of Energy Optimization 
• How Demand Forecasting Strategies are helping  
• The Shortage of data scientists in the Energy sector
• The current state of Machine Learning and how AutoML technologies can help the Energy space

Forecasting the demand for energy is crucial for planning periodical operations and facility expansion in varying levels of the electricity sector—and each player in the Energy industry needs the most reliable technology to serve customers and society, meet environmental regulations, and turn a profit. 

What is “Energy Optimization”?

Copenhagen is setting an example for Europe with its push towards energy optimization and a commitment to sustainability and environmental responsibility. In fact, Copenhagen has set an ambitious goal of becoming carbon neutral by 2025—and is well on its way to achieving this goal.

One way Copenhagen is achieving these goals is through the Finance Act project, which analyzes the use of buildings through artificial intelligence and sensors to find models that can predict and manage energy consumption as necessary. When energy consumption becomes smaller, power stations do not need to produce as much energy—and subsequent CO2—and carbon emissions are reduced.

European cities like Copenhagen are taking concrete steps to improve energy optimization—or the process of improving energy efficiency and reducing energy waste in a system or process. There are many ways to achieve energy optimization, including:

• Implementing energy-efficient technologies and more efficient systems
• Improving operational practices and maintenance procedures
• Adopting renewable energy sources

This can be achieved through better equipment, improved maintenance procedures, and smarter operational practices. Maximizing the use of available energy resources while minimizing waste and reducing environmental impact is the goal of these initiatives. Energy optimization, however, can also help reduce costs by lowering utility bills and reducing greenhouse gas emissions.

Additionally, it helps companies meet their sustainability goals as well as comply with industry standards for energy efficiency. By implementing an effective strategy for optimizing energy usage, businesses can save money in the long run while also doing their part to protect our environment.

The Benefits of Accurate Demand Forecasting

While local and national European governments are taking steps to improve energy optimization, European electricity distributors are taking steps to more accurately forecast customer demand. Better forecasts allow electricity distributors to meet their financial goals—while still providing reliable energy services. 

By having an accurate forecast of customer usage, electricity distributors can plan ahead for peak times when more resources are needed and adjust their strategies accordingly. Additionally, having an accurate forecast can help reduce costs by ensuring that resources are not wasted on unnecessary investments.

In Turkey, for example, if an energy company generates too much electricity—the surplus must be sold at a substantial loss. If it produces too little electricity—it must pay fines to EPİAŞ, a Turkish energy exchange that works for the transparent, predictable, and sustainable operation and development of energy markets. 

IC İçtaş Energy, a leading Turkish energy company, needs their daily electricity predictions into EPİAŞ by 11 a.m. and be ready to match supply and demand for the next day’s sell orders.

IC İçtaş Energy chose ML Studio to improve the predictive quality of their daily forecasts with machine learning. The result?

Before using ML Studio for daily electric consumption predictions, they had an error rate as high as 14–15 percent. Now, the error rate has plummeted to as low as three percent. By understanding how much energy customers will consume at any given time, electricity distributors such as IC İçtaş Energy can better manage their grid networks and maintain stability during peak periods.

This is especially important when dealing with renewable energy sources that may not be available at certain times due to weather conditions or other factors. With an accurate forecast, it’s easier to plan for these interruptions in supply.  

Demand Forecasting Strategies 

Electricity distributors can use several different strategies to create accurate forecasts of customer demand. The most common approach is using a combination of historical data analysis and statistical methods such as regression analysis or time series modeling.

By analyzing past customer usage patterns and trends over time, it’s possible to develop a model that accurately predicts future demand. Other approaches include using machine learning algorithms or leveraging artificial intelligence technologies such as natural language processing (NLP) or computer vision (CV) to generate more detailed predictions about customer behavior. 

For example, the wholesale division of Zorlu Energy includes customers who have chosen Zorlu Energy as their electricity distributor—including large customers (hotels and shopping centers) and relatively small consumers (individual customers who get their electricity from Zorlu).

By properly estimating imbalances in portfolios, Zorlu has more flexibility to understand how much power they will need to draw each hour during the next month—and help serve and retain customers.

By using ML Studio, Zorlu Energy can also train separate models for different hourly clocks—taking advantage of hourly weather forecasts and historical usage data. 

The Current State of Machine Learning Usage in the Energy Space

The energy industry is undergoing a rapid digital transformation—and machine learning is playing an increasingly important role in this process. With the emergence of new technologies such as big data analytics and AI, ML is being used to optimize operations and improve customer service across the entire energy sector.

For example, New York-based startup Intellastar knows that maintaining an uninterrupted power supply is one of the top priorities for the energy sector. Intellastar has developed Tipify, a platform for predictive maintenance of smart grids, distributed energy networks, and electricity systems of smart buildings.

Tipify—through the use of mathematical, logical, and statistical analysis—is able to:

• Monitor site performance 
• Detect faults
• Alert users about required performance improvements 

By leveraging ML for risk management and forecasting, companies can further enhance their competitive position within the market. In short, ML stands to revolutionize how businesses operate within the energy space.

The Rise of AutoML Technologies in the Energy Sector

From predictive maintenance to automated demand response, ML-powered solutions are helping utilities reduce costs, increase efficiency, and better serve their customers. By leveraging ML for risk management, companies can further enhance their competitive position within the market.

BP, headquartered in London, U.K., is a great example of an energy company that has started leveraging AI to augment its risk management decision-making. BP uses AI technology to analyze high volumes of complex corporate and open-source information to quickly find valuable connections and insights.

Out in the field, BP has implemented AI systems to help manage safety risks at its refineries and other facilities. BP’s AI system uses machine learning algorithms to analyze data from sensors, cameras, and other sources in real-time, looking for patterns and anomalies that could indicate potential safety risks. These systems can also identify potential risks by analyzing historical data and using predictive analytics to forecast future risks.

By using machine learning for risk management, BP is able to: 

• Proactively identify and mitigate potential safety risks
• Reduce the likelihood of accidents
• Improve overall safety at its facilities

This helps the company to protect its employees and the environment, as well as avoid costly fines and legal liabilities.

In Europe, electricity distribution companies are increasingly turning to machine learning for other applications such as: 

• Predicting maintenance needs
• Optimizing peak load management strategies
• Detecting fraud or theft within the system

As a result of its growing popularity among electricity distribution companies in Europe, machine learning will continue to play an important role in the industry’s future success.

AutoML Helps the Energy Sector Reduce Emissions While Reaching Customers Better

As the world continues to move towards a greener, more sustainable energy future, AutoML can help organizations save time and money by automating many complex tasks—while also reducing their environmental impact. 

Enel, an Italian manufacturer and distributor of electricity and gas, has been at the  forefront of initiating low environmental impact, AI-driven solutions for the maritime industry.

Have you ever seen a giant cruise ship docked at a port? Enel believes that cruise transport can reduce emissions by utilizing electrification solutions when cruise ships enter and leave ports and during quayside stops. 

As traditional energy storage alone is not sufficient, AI-powered software can fully optimize the power flow operation while keeping all the site’s parties under control—and increase the cruise sector’s drive towards decarbonization.

With its ability to quickly analyze large volumes of data from multiple sources, AutoML is also being used by leading energy companies around the world to develop new strategies for increasing efficiency and improving customer service.

E.ON—a European energy provider based in Essen, Germany that uses AI-powered customer segmentation to tailor its marketing and sales strategies—analyzes historical customer data and current energy market trends to identify patterns and preferences among different customer segments. 

Their system can predict how each segment is likely to respond to different offers and campaigns—allowing E.ON to tailor its marketing and sales strategies to each group’s specific needs and interests.

E.ON believes that, “with the right data (events, competitive pressure, quality of service and a long list of further attributes),” they can build models to quantify the propensity of a particular customer to leave them—making their insights highly actionable.

One example is making retention campaigns focused on addressing the most important drivers in a particular high-risk customer’s cluster. In addition, as they want customers to adopt many of their services, they carefully analyze which products and services would be relevant for each particular existing customer—and create cross-selling strategies. 

For example, if a PV and battery solution was installed, E.ON can compute the breakeven for a commodity customer owning a house. For customers where the business case is significant, they could run a campaign that boosts the share of homes with their own solar generation—a bonus for their customers and the environment.

Innovative companies like Enel and E.ON that utilize machine learning properly will gain insights into optimal algorithms and models along with the following benefits: 

• Cost savings from reduced manual labor requirements and more efficient and smarter forecasts
• Improved customer satisfaction through better targeted campaigns
• More accurate pricing strategies based on predictive analytics that contribute towards greater success in this ever-evolving landscape

With its ability to rapidly process large amounts of data and generate actionable insights quickly, AutoML provides an invaluable resource for electricity distributors in Europe looking to maximize their efficiency while providing reliable service at competitive prices.

AutoML Can Help Overcome the Shortage of Data Scientists in the Energy Sector

As the energy sector continues to experience rapid growth and transformation, there is a growing need for data scientists who can analyze large amounts of data and provide insights into trends.

Unfortunately, this demand has not been met with an adequate supply of qualified professionals in the field. The lack of skilled data scientists in the energy sector is causing businesses to struggle with making informed decisions due to limited resources and knowledge. 

This shortage of talent can be attributed to a number of factors such as:

• Difficulty recruiting qualified personnel
• Low salaries compared to other sectors
• Inadequate training opportunities

Companies must take proactive measures such as offering competitive compensation packages and investing in training programs if they are going to meet their goals for successful data-driven decision-making.

So, how can AutoML technologies help assuage the shortage of data scientists in the energy sector?

ML Studio can help energy providers that want to leverage AI and machine learning—but don’t have the in-house expertise or resources to do so.

ML Studio can also help automate the machine learning process—from data preparation and feature engineering to model selection and hyperparameter tuning—for the following roles:

• Junior Data Scientists: ML Studio can take preprocessed data sets and automatically generate and optimize machine learning models. This combination can help energy providers make more informed decisions, improve operational efficiency, and reduce costs. Junior data scientists can learn from the generated output and develop their skills further.
• Business Analysts (Citizen Data Scientists): Business analysts usually have a strong understanding of their business processes and data—but may not have the same level of technical expertise as professional data scientists. ML Studio can help citizen data scientists automate the machine learning process—enabling them to build and deploy machine learning models without requiring a deep understanding of the technical details of machine learning. 
• Domain Experts: Domain experts—such as department leaders, tech leaders, and project managers—may not have a background in data science or machine learning. ML Studio can help domain experts automate the machine learning process and generate insights that can inform their decision-making. For example, a marketing department leader could use ML Studio to analyze customer data and develop targeted marketing campaigns. A tech leader, meanwhile, could use the platform to optimize resource allocation and reduce costs.

When done right, the collaboration between energy companies and ML Studio can increase the output of energy projects, decrease cost and time of delivered projects, and increase focus on more interesting demand forecasting use cases that grow productivity and efficiency—and help meet environmental regulations. 

ML Studio democratizes AI by making it accessible for energy providers to solve real-world energy problems—such as Electricity Demand Forecasting and Renewable Energy Sources Production Forecasting—with a robust and ready-to-use End-to-End AI platform that doesn’t go off budget.

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